Explore multiomics data with an interactive click-through demo of Connected Multiomics. Click to expand the selection for easier viewing.

About Illumina Connected Multiomics

Illumina Connected Multiomics is a cloud-based software platform designed for biologists to perform tertiary analysis of multiomics data for research purposes. It enables users to organize and manage biological data into studies, apply statistical methods, reference knowledge sources for biological interpretation, correlate results across various omics data types and modalities, and deliver visualization tools to support result interpretation. This functionality streamlines the process from samples to multiomics insights, accelerating data interpretation and publication efforts.

Additional Multiomics Software from Illumina

Illumina Multiomics Software offers a comprehensive range of solutions to streamline your journey from sample collection to multiomic insights. Discover assays designed for protein analysis, spatial data, single-cell RNA data, and more.

Our powerful study management tool helps organize your data from ingestion to analysis, while our advanced analysis platform generates actionable insights to drive your research forward.

Select your application of interest below to learn about how to use it.

Explore our for an introductory experience of available Multiomics workflows.

When a study contains multiple libraries, the data might contains variabilities due to technical differences (e.g. sequencing machine, library prep kit etc.) in addition to biological differences (like treatment, genotype etc.). Batch removal is essential to remove the noise and discover biological variations.

• General linear model

• Harmony

• Seurat3 integration

Connected Multiomics offers tools to perform analysis on methylation array data and Illumina 5-base DNA Prep data.

• Detect Differential Methylation

Study Overview

A Study is used to manage your samples, metadata, comparison groups, and analyses for a specific set of data.

Card View

In the Studies overview screen, each study is represented as a card. The study card displays the following information:

To switch between card view and list view, click the icons in the top right.

List View

In list view, you'll see a list of all studies in your workgroup. To customize the columns displayed, click the Columns tab on the right.

To filter studies based on specific attributes, click the Filters tab.

Click the refresh icon in the top right to clear your filters and return to the full list.

You can also filter individual columns by clicking the icon next to each column header.

For more options, click the three dots.

When entering a study, you can see study information, data metrics and recent analyses. Studies can be easily updated by adding sample data and meta data. This section will cover:

• Overview

• Samples

• Sample Metadata

If a single cell data node contains cell attribute information, e.g., clustering results, classifications, or imported attributes, a counts-type data node containing the number of cells from each attribute group for each sample can be generated and used for downstream analysis.

To invoke Generate group cell counts:

• Click a single cell count data node with cell-level attribute information

• Click Pre-analysis tools in the toolbox

• Click Generate group cell counts

• Select the attribute to group the cells from the Group by drop-down menu and click + button

• Click Finish

A group cell counts node will be generated. The data node contains a matrix of cell counts in each sample for each group. You can view the counts results in the Group cell counts report.

The Group cell counts data node is a counts type data node and downstream analysis tasks, such as normalization, PCA, and ANOVA, can be used to analyze the group cell counts data.

Connected Multiomics contains a number of quality control tools and reports that can be used to evaluate the current status of your analysis and decide downstream steps. Quality control tools are organized under the Quality Assurance / Quality Control (QA/QC) section of the context-sensitive menu and are available for different type of data nodes.

This section will illustrate:

• Feature distribution

• Single-cell QA/QC

• Cell barcode QA/QC

In addition to the tools listed above, many other functionalities can also be interpreted in sense of quality control. For instance, principal components analysis, hierarchical clustering (on sample level), variant detection report, and quantification report.

This task is to replace missing data in the data with estimated values based on selected method.

First select the computation is based on samples/cells or features, and click Finish to replace missing values. Some functions will generate the same results no matter which transform option is selected, e.g. constant value. Others will generate different results:

• Constant values: specify a value to replace the missing data

• Maximum: use maximum value of samples/cells or features to replace missing data depends transform option

• Mean: use mean value of samples/cells or features to replace missing data depends transform option

• Median: use median value of samples/cells or features to replace missing data depends transform option

• Minimum: use minimum value of samples/cells or features to replace missing data depends transform option

• K-nearest neighbor (mean): specify number of neighbors (N), Euclidean metric is used to compute neighbors, use mean of (N) neighbors to replace missing data

• K-nearest neighbor (median): specify number of neighbors (N), Euclidean metric is used to compute neighbors, use median of (N) neighbors to replace missing data

The Split by Attribute task is used to split a data node into different nodes based on the groups in a categorical attribute, each data node only includes the samples/cells from one group. It is a more efficient way to filter samples/cells if you plan to perform downstream analysis on each and every group separately in an attribute.

Click on the data node and select split by attribute from the Filtering section in task menu, select the attribute to split the data on.

Result of the split by attribute task will be two separate data nodes, each contains samples/cells from one gender group.

Connected Multiomics provides rigorous statistics methods to perform on the data, tasks in this section includes:

• Differential analysis

• Descriptive statistics

• Correlation

Connected Multiomics has the flexibility to subsample your data for further downstream analyses. Filter data by:

• Filter features

• Filter samples/cells

• Split by attribute

Sample Groups

A sample group refers to a collection or subset of samples that are grouped together based on shared characteristics or attributes.

Create Sample Group

To create a sample group, filter your samples down into only those you want to be part of your sample group. You can filter your data by clicking the filter icon at the top of each visible column or clicking

Click into your analysis to see details about your analysis steps.

Analyses

In the tab of your analysis within a study, you'll be able to see a depiction of your analysis pipeline, represented by data nodes and task nodes connected by arrows. Each rectangle represents a task, and each circle represents an output. You can click on each node to view more details on the right-side toolbox.

Double click on each node to see the task details appear in a separate window.

In the Samples tab, you'll see a list of samples that were added to the study from the ingested sample files, along with their associated metadata attributes. You can search for a sample using the search bar on the left or filter data to create your desired sample groups.

The Columns icon allows you to customize which columns you want to view. Click on a column to add it to the table, and click again to remove it. Click the refresh icon to refresh the sample view after adding more data or to clear your filters and reset to the default view.

You can filter your data by clicking the filter icon at the top of each visible column or clicking Filters icon above the table to filter all columns based on metadata attributes . This is useful when creating sample groups based on specific attributes. You can also drag and drop columns to reorder them.

To Create a new study, click in the top right and follow the steps below.

Click on the Analyses tab in the left panel to see a list of all analyses within your workgroup, including those across various studies. Use the Columns and Filters tabs on the right to adjust and refine your view. You can also search for specific analyses using the search bar in the top right. Additionally, you can perform actions on your analyses, such as renaming or deleting an analysis.

If your experimental design includes a sample or a group of samples serving as a baseline control, you can normalize the experimental samples by subtracting or dividing by the baseline sample(s) using the Normalize to baseline task in menu. For example, in PCR experiments, the delta Ct values of control samples are subtracted from the delta Ct values of experimental samples to obtain delta-delta Ct values for the experimental samples.

The Normalize to baseline task is available in the Normalization and Scaling section of the context-sensitive menu upon selection of any count matrix data node.

There are three options to choose the baseline samples:

• use all samples

• use a group

The Detect Differential Methylation task enables users to perform differential test on methylation array data and Illumina 5-base DNA Prep data. The task converts Beta-values to M-values and uses these to perform .

Running Detect Differential Methylation

• Click to select a methylation data node with beta values.

This section has tools that are useful in managing and understanding single cell data, especially for downstream analysis. To invoke Annotation/Metadata tools, click on any Single cell counts data node. These include the following tasks:

If you have attribute information about your cells, you can use the Annotate cells task in Connected Multiomics to apply this information to the data. Once applied, these can be used like any other attributes, and thus can be used for cell selection, classification and differential analysis.

To run Annotate cells:

• Click a Single cell counts data node

• Click the Annotation/Metadata section in the toolbox

• Click

Single cell RNA-seq gene expression counts are zero inflated due to inefficient mRNA capture. This normalization task is based on MAGIC[1]–MArkov Affinity-based Graph Imputation of Cells), to recover gene expression lost due to drop-out. The limitation on using this method is up to 50K cells in the input data node.

To invoke this task, click on a normalized data node which has less than 50K cells, it will first compute PCA to use the number of PCs specified to impute.

Click Finish to run the task, it will output low expression imputed matrix in the output report node, which can be used for downstream analysis like differential analysis, visualization etc.

References

1. Dijk D et al. MAGIC: A diffusion-based imputation method reveals gene-gene interactions in single-cell RNA-sequencing data

Cell level attributes can be used for filtering, cell selection, classification and differential analysis using the . Data nodes in the Analyses pipeline that contain this cell level information (e.g. Graph-based clusters data node) can be published to the project and will then be available in Study > Analysis > Metadata > Manage under Cell attributes where they can be modified and reordered.

If the task report is produced, but the results are missing for some features represented by "?", it may be because something went wrong with the estimation procedure. To better understand this, use the information available in the Extra details report . This type of information is present for many tasks, including Differential Analysis and Survival Analysis.

Click the Extra details report for the feature of interest. This will display the Extra details report.

When the estimation procedure fails, a red triangle will be present next to the information criteria value. Hover over the triangle to see a detailed error message.

In many cases, estimation failure is due to low expression, filter out low expression features or choose a reasonable normalization method will resolve this issue.

Sometimes the estimation results are not missing but the reported values look inadequate. If this is the case, the Extra details report may show that the estimation procedure generated a warning, and the triangle is yellow. To remove suspicious results in the report, set Use only reliable estimation results

To analyze scATAC-seq data, Connected Multiomics introduced a new technique - LSI (latent semantic indexing )[1]. LSI combines steps of frequency-inverse document frequency (TF-IDF) normalization followed by singular value decomposition (SVD). This returns a reduced dimension representation of a matrix. Although SVD and Principal components analysis (PCA) are two different techniques, the SVD has a close connection to PCA. Because PCA is simply an application of the SVD. For users who are more familiar with scRNA-seq, you can think of SVD as analogous to the output of PCA. And similarly, the statistical interpretation of singular values is in the form of variance in the data explained by the various components. The singular values produced by the SVD are in order from largest to smallest and when squared are proportional the amount of variance explained by a given singular vector.

SVD task can be invoked in Exploratory analysis section by clicking any single cell counts data node. We recommend running SVD on the normalized data, particularly the TF-IDF normalized counts for scATAC-seq analysis.

To run SVD task,

• Click a single cell counts data node

In complex projects, different data matrices (e.g. observations on rows and features on columns) need to be merged in order to achieve the analysis goals. For example, two cell populations were identified on separate branches of the analysis pipeline and to combine them before any joint downstream steps, the expression matrices have to be combined. Alternatively, two assays (gene expression and protein expression) were performed on the same cells so the expression matrices have to be merged for joint analysis.

Merge matrices task is located in the Pre-analysis tools section of the toolbox and it can handle two scenarios: Merge cells/samples and Merge features. To start, select the first data node on the pipeline (e.g. single cell counts) and then select the Merge matrices task.

The Downsample Cells task is used to randomly downsample the number of cells in a single cell data set. This task can be used to reduce the size of large single cell datasets to small and manageable sizes for quick analysis. Another use case for this task is for a study with multi samples with each sample having different number of cells. Downsample Cells can be used to randomly select an equal number of cells for all the samples in the study. For the default setting, the sample with the minimum number of cells is used with the number of cells in that sample set as the number of cells to be selected in the other samples. However, this default setting can be changed to a preferred number by the user. If the number selected by the user is greater than the number of cells in one or more samples, those samples will not be downsampled and all the cells in those samples will be returned. If the number selected by the user is greater than the number of cells in all the samples, then none of the samples will be downsampled.

To run a downsample task first click on a single cell count data node. Go to the Filtering section and select Downsample cells task to open the dialogue.

The minimum number of cells in any of the samples is used in the default settings. Or you cand specify the percentile to reduce to for each sample. Click Finish to run the task.

This task can be invoked from count matrix data node or clustering task report (Statistics > Compute biomarkers). It performs Student's t-tests on the selected attribute, comparing one subgroup at a time vs all the others combined. By default, the up-regulated genes are reported as biomarkers.

Compute biomarker dialog

In the set-up dialog, select the attribute from the drop down list. The available attributes are categorical attributes which can be seen on the Data tab (i.e. project-level attributes) as well as and data node-specific annotation.

By default, the result outputs the features that are up-regulated by at least 1.5 fold change (in linear scale) for each subgroup comparing to the others.

Split matrix can be invoked on any counts data node with more than one feature types (multiomics data). For example, a CITE-Seq experiment would have Gene Expression counts and Antibody Capture counts in the single cell counts data node. This task to split different feature measurements for downstream analysis.

There are no parameters to configure, to run:

• Click the counts data node you want to split

• Click the Pre-analysis tools section of the toolbox

To ensure that different data sets are comparable, several normalization and scaling options are available in Connected Multiomics. These include newly-developed algorithms specifically tailored for genomic analysis.

Left single clicking on any task (the rectangles) in the analysis pipeline will cause a Task Actions section to appear in the pop-up menu. This allows users to:

• Rerun tasks: rerun the selected task, the task dialog will pop-up and users can change parameters of the task. Previous downstream analysis of the selected task will not be rerun.

• Rerun with downstream tasks: rerun the selected task, the task dialog will pop-up, users can change the parameters of the current task and the downstream analysis will be rerun with the same configuration as the previous one.

• Edit description: the description of the task can be replaced by manually typing in string.

Connected Multiomics offers tools to perform analysis on regions data.

• Get Regional Methylation

• Annotate regions

Cells undergo changes to transition from one state to another as part of development, disease, and throughout life. Because these changes can be gradual, trajectory analysis attempts to describe progress through a biological process as a position along a path. Because biological processes are often complex, trajectory analysis builds branching trajectories where different paths can be chosen at different points along the trajectory. The progress of a cell along a trajectory from the starting point or root, can be quantified as a numeric value, pseudotime.

Connected Multommics offers Monocle 2 and Monocle 3 methods.

• Monocle 2

• Monocle 3

Difference Between Monocle 3 and Monocle 2

Major updates in Monocle 3 (compared to Monocle 2) include:

• Monocle 3 learns the principal trajectory graph in the UMAP space;

• the principal graph is smoothened and small branches are excluded;

• support for principal graphs with loops and convergence points;

• support for multiple root nodes.

Sample Metadata

Sample metadata allows you to add important additional information to your study that can be used to create sample groups or make comparisons as part of your study design. Sample metadata can be uploaded from local storage or added to an ICA project.

Metadata mapping

Metadata columns are populated based on the information in your metadata TSV file. Metadata files are required per study. Upon importing a metadata file, the samples will automatically be updated to reflect the metadata attributes. The sample names in the metadata file must match those in the data files. For example, in the figure below, you can see that the Sample ID in the ADAT file matches the SampleID in the metadata file. As a result, the attribute columns in the metadata file will correspond with the metadata columns available for selection in the Samples table.

You can add multiple metadata files. In this case, any samples that do not overlap with existing ones will be added as new entries in the Samples table.

Download Sample Metadata file

Download a formatted Metadata file example for your study to review the metadata information. Make sure to correct the SampleID column header to match the exact name of the samples.

• Navigate to Sample groups > Info > Download Metadata. Click the Sample groups tab, under Action click the Info button, then use the Download Metadata button to download an example metadata file to your machine.

Upload sample metadata file to Connected Multiomics

Upload the sample metadata file to Connected Multiomics to update the sample records of the study.

• Within a study, click + Add Data > Upload metadata

• Upload data from your local or server based drives. Click Browse to select the files or drag and drop the files to the purple box.

Using a sample metadata file contained in the Illumina Connected Analytics project

To add data, click the button in the top right. You can and choose your data type and format. TSV metadata files can always be selected. For example, if you choose Bulk > Proteomics as the data type, you will only see ADAT files and TSV files as options for upload.

Sync metadata from Study

After the new metadata file has been added to the Study, users will be able to update their metadata within an existing analysis by clicking the "Sync metadata from study" button in the Metadata tab.

A new confirmation dialog will appear to make sure users understand what will happen.

Once the above Sync metadata button is pushed, the metadata will be updated accordingly.

The Data summary report in Connected Multiomics provides an overview of all tasks performed as part of a pipeline. This is particularly useful for report writing, record keeping and revisiting projects after a long period of time.

Viewing the Data Summary Report

Click on an output data node under the Analyses tab of a project and choose Data summary report from the context sensitive menu on the right. The report will include details from all of the tasks upstream of the selected the node. If tasks have been performed downstream of the selected data node, they will not be included in the report.

Each task will appear as a separate section on the Data summary report.

Click the arrow ( / ) to expand and collapse each section. When expanded, the task name, user that performed the task, start date and time, duration and the output file size are displayed. To view or hide a table of task settings, click Show/hide details.

Saving the Data Summary Report

The Data summary report can be saved in different formats via the web browser. The instructions below are for Google Chrome. If you are using a different browser, consult your browser's help for equivalent instructions.

Save as a PDF

On the Data summary report, expand all sections and show all task details. Right-click anywhere on the page and choose Print... from the menu or use Ctrl+P (Command+P on Mac).

In the print dialog, set the destination to Save as PDF. Select the Background graphics checkbox (optional), click the blue Save button and choose a file location on your local machine.

The PDF can be attached to an email and/or opened in a PDF viewer of your choice.

Save as HTML

On the Data summary report, right-click anywhere on the page and choose Save as… from the menu or use Ctrl+S (Command+S on Mac). Choose a file location on your local machine and set the file type to Web Page, Complete.

The HTML file can be opened in a browser of your choice.

It is challenging to analyze scRNA-seq data, particularly when they are assayed with different technologies. Because biological and technical differences are interspersed. Harmony[1] is an algorithm that projects cells into a shared embedding where cells group by cell type rather than dataset-specific conditions. Harmony is able to simultaneously account for multiple experimental and biological factors while integrating different datasets.

Harmony can be invoked in Batch removal section only on PCA data node

1. the data has some categorical attributes (only categorical attributes can be included in the model)

2. PCA data node is selected

To run Harmony,

• Click a PCA data node

• Click the Batch removal section in the toolbox

• Click Harmony

You will be prompted to pick some attribute(s) for analysis. To set up the model, you need to choose which attributes should be considered. For example, in the case of one dataset that has different cell types from multiple batches, the batch may have divergent impacts on different cell types.

To remove batch effects with default settings,

• Click Batch

• Click Add factors

• Click Finish

Diversity clustering penalty (theta): Default theta=2. Higher value of penalty will have stronger correction, which results in better mixing . Zero penalty means no correction. The range of this value is from 0 to positive infinity.

The output of Harmony is a new data node. This data node contains the Harmony corrected values and can be used as the input for downstream tasks such as Graph-based clustering, UMAP and T-SNE

Users can click Configure to change the default settings In Advanced options.

Number of clusters (nclust): Number of clusters in model. Set this to the distinct count of cell types. nclust=1 equivalent to simple linear regression. Use 0 to enable Seurat’s RunHarmony() default setting.

Width of soft kmeans clusters (sigma): The range of this value is from 0 to positive infinity. When set it to 0, an observation will be assigned to 1 cluster (hard clustering). When the value is greater than 0, the observation will be potentially belong to multiple clusters (soft clustering, or fuzzy clustering). Default sigma=0.1. Sigma scales the distance from a cell to cluster centroids. Larger values of sigma result in observations assigned to more clusters. Smaller values of sigma make soft kmeans cluster approach hard clustering.

Ridge regression penalty (lambda): Default lambda=1. Lambda must be strictly positive. Smaller values result in more aggressive correction.

Random seed: Use the same random seed to reproduce the results.

References

1. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh P-r, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nature Methods; 2019. .

Multiomics single cell analysis is based on simultaneous detection of different types of biological molecules on the same cells. Common multiomics techniques include feature barcoding or CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) technologies, which enable parallel assessment of gene and protein expression. Specific bioinformatics tools have been developed to enable scientists to integrate results of multiple assays and learn relative importance of each type (or each biological molecule) in identification of cell types. Connected Multiomics supports weighted nearest neighbor (WNN) analysis (1), which can help combine output of two molecular assays.

Invoking Find Multimodal Neighbours

This task can only be performed on data nodes containing PCA scores – which are PCA output and graph based clustering output nodes generated from PCA nodes. To start, select a PCA data node of one of the assays (e.g. gene expression) and go to Exploratory analysis > Find multimodal neighbors in the toolbox. On the task setup page, use the Select data node button to point to the PCA data node of the other assay (e.g. protein expression), by default, there is a node selected.

When you click the Select data node button, another dialog will be open, showing your current pipeline. Data nodes that can be used for WNN are in color of the branch, other nodes are disabled (greyed out). To pick a node, left-click on it and then push the Select button.

The selected data node is shown under the Select data node button. If you made a mistake, use the Clear selection link.

If there are graph-based clustering task performed on PCA data node, the output of graph-based clustering node also has PCA score from the input data, so the output graph-based clustering data nodes also can be candidate of WNN task.

To customize the Advanced options, select the Configure link. At present you can only change the number of nearest neighbors for each modality (-k.nn option of the Seurat package); the default value is 20. An illustration on how to use that option to assess the robustness of WNN analysis can be found in Hao et al. (1). The nearest neighbor search method is K-NN and distance metric is Euclidean.

To launch the Find multimodal neighbors task, click the Finish button on the task setup page. For each cell, the WNN algorithm calculates its closest neighbors based on a weighted combination of RNA and protein similarities. The output of the Find multimodal neighbors task is a WNN data node.

For downstream analysis, you can launch a UMAP or graph-based clustering tasks on a WNN node. For example, The example below shows a snippet of analysis of a feature barcoding data set; gene expression and protein expression data were processed separately, and then Find multimodal neighbors was invoked on two respective PCA data nodes. UMAP and graph-based clustering tasks were performed on WNN node.

References

1. Hao Y, Hao S, Andersen-Nissen E, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. doi:10.1016/j.cell.2021.04.048

What is UMAP?

Uniform Manifold Approximation and Projection (UMAP) is a dimensional reduction technique [1]. UMAP aims to preserve the essential high-dimensional structure and present it in a low-dimensional representation. UMAP is particularly useful for visually identifying groups of similar samples or cells in large high-dimensional data sets such as single cell RNA-Seq.

Running UMAP

UMAP task can run on any counts data node, however, it is very computationally intensive, we recommend running PCA first and run this task on PC output data node using the top few number of PCs.

• Click the counts data node or PCA data node (recommended)

• Click the Exploratory analysis section of the toolbox

• Click UMAP

• Click Finish to run

Initialize output values

Sets the initialization mode. Options are Spectral and Random.

Spectral - good initial points are chosen using spectral embedding (more accurate)

Random - random initial points are chosen (faster)

PCs to use

Choose how many PCs to use, default is the top 20 PCs. Low number of PCs will reduce the run time.

Split cells by sample

Chose whether to run UMAP on all samples together or on each sample individually.

Checking the box will run UMAP on each sample individually.

UMAP produces a UMAP task node. Opening the task report launches a scatter plot showing the UMAP results. Each point on the plot is a cell for single cell data or a sample for bulk data. The plot will open in 2D or 3D depending on the user preference.

UMAP vs. t-SNE

Both t-SNE and UMAP are dimensional reduction techniques that are useful for identifying groups of similar samples in large high-dimensional data sets. A comparison of the techniques for visualizing single cell RNA-Seq data by the authors of UMAP suggests that UMAP runs faster, is more reproducible, gives a more meaningful organization of clusters, and preserves more information about the global structure of the data than t-SNE [2].

We find UMAP to be more informative than t-SNE for many data sets. For example, the similarities and differences between clusters are clearly visible with UMAP, but more difficult to judge with t-SNE.

Advanced UMAP parameters

Local neighborhood size

UMAP preserves the local structure of the data by focusing on the distances between each point and its nearest neighbors. Local neighborhood size is the number of nearest neighbors to consider.

You can adjust this value to prioritize global or local relationships. Smaller values will give a more local view, while larger values will give a more global view. Default is 30.

Minimal distance

The effective minimum distance between embedded points. Smaller values will create a more clustered embedding, while larger values will create a more evenly dispersed embedding.

You can decrease this value to make clusters more tightly packed or increase it to make them looser. Default is 0.3.

Distance metric

The metric to use when computing distances in high-dimensional space. Options are Euclidean, Manhattan, Chebyshev, Canberra, Bray Curtis, and Cosine. Default is Cosine.

Number of iterations

UMAP uses an iterative algorithm to optimize the low-dimensional representation. The value 0 corresponds to the default, which chooses the number of iterations based on the size of the input data. More iterations will result in a more accurate embedding, but will take longer to run. Default is 0.

Random generator seed

Several parts of UMAP utilize a random number generator to provide an initial value. Default is 42. To reproduce the results, use the same random seed at all runs.

References

[1] McInnes L and Healy J, UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction, ArXiv, 2018, e-prints 1802.03426,

[2] Becht E, McInnes L, Healy J, Dutertre A-C, Kwok I, Guan Ng L, Ginhoux F, and Newell E, Dimensionality reduction for visualizing single-cell data using UMAP, Nature Biotechnology, 2019, 37, 38-44.

This task allows you to annotate features only to the matrix data node.

• Click a Single cell counts data node

• Click the Annotation/Metadata section in the toolbox

• Click Annotate features

The assembly and annotation files should be consistent with the files used in upstream analysis.

• Click Finish

A new data node, Annotated counts, will be generated . The annotations will be available in downstream analysis tasks.

Why Merge differential expression results

Plotting fold changes of different data types (mRNA and Protein in this example) together is a common integrative approach in multi-omics studies. By visually examining the scatter plot and specifically looking at the outlier genes, researchers can gain crucial insights into the hierarchy and kinetics of regulation within the biological system being studied. In ICM, this analysis can be performed by running the “Merge Differential Expression Results” task.

Running Merge differential expression results

The task can be invoked on any differential analysis results data node.

• Click a data node of differential analysis such as DESeq2

• Click the Exploratory analysis section of the toolbox

• Click Merge differential expression results

• In the task set up page, push the Select data node button to pick the right option and hit the Select button

• Hit the Next button to proceed

• Select the matching features for each data node

• Click Finish to run

The result is stored under a Merge results node. To open it, double click on the node or select the respective Task report from the context sensitive menu.

Task report

For this task, the report directly opens a 2D scatter plot in Data Viewer. Fold changes of different data types (mRNA and Protein in this example) in log2 scale are labeled at two axes separately. In the example, mRNA fold change reflects transcriptional activity (gene expression at RNA level); Protein fold change reflects translational and post-translational processes. By plotting them together, one can see how changes in mRNA abundance correlate with protein abundance.

This section constitutes tools that are useful in preparing single cell data for downstream analysis, such as multi-sample comparison or multiomics analysis. To invoke Pre-analysis tools, click on any Single cell counts data node. These include the following tasks:

• Pseudobulk

• Split by feature type

• Generate group cell counts

Spatially variable genes (SVGs) are genes whose expression patterns vary significantly across different spatial locations within a tissue. Identifying SVGs is a key step in spatial transcriptomics analysis and can reveal biologically meaningful spatial expression patterns, tissue architecture, cell-type niches, or gradients of signaling molecules.

We have implemented PROST, a highly scalable algorithm for SVG detection [1].

Performing SVG analysis

The task can be invoked on any non-normalised node containing spatial data, we recommend filtering cells and genes before running the task. The analysis is species-agnostic.

• Select the appropriate node and click on 'Statistics>Spatially Variable Genes'

• Edit the task settings as necessary:

• Note the percentage parameter can be adjusted depending on dataset size, this may affect running times.

• Adjust the advanced settings as needed:

• We currently recommend using 2,000 HVGs for PROST calculation in order to make the task more scalable on large datasets. This parameter can be increased to include all genes in the data, but may severely affect performance.

• Click Finish to run the task.

Once the task has completed you will nee a new 'Spatially variable genes' node on the task graph:

Clicking twice on the node will open the task report, a table of the most significant SVGs identified:

The table contains the genes identified as SVGs and the PROST Index (PI) per feature, an indicator of spatial variability [1]. The results can be downloaded as a table, or visualised in the Data viewer. Here is an example:

References

[1] Liang, Yuchen, et al. "PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics." Nature Communications 15.1 (2024): 600.

An important aspect of variant analysis is the ability to prioritize specific variants for further investigation. As variant detection can often identify a large number of variants, it may be difficult to determine which variants may impact phenotypes. SnpEff (version 4.1k) provides a means to annotate and predict the effects of variants on genes, allowing for prioritization of variants within the project. In addition, the SnpEff databases utilized for prediction support a large number of genome assemblies. Information regarding the implementation of the predictions is detailed by Cingolani et al. [1] The predicted effect of the variant is categorized by impact:

• HIGH - frame shifts, addition/deletion of stop codons, etc;

• MODERATE – codon change/deletion/insertion, etc;

• LOW – synonymous changes, etc;

• MODIFIER – changes outside coding regions, etc.

Further details about output metrics can be found in the . The Annotate variants (SnpEff) task can be invoked from any Variants or Annotated variants data node, and the task will supplement any existing annotation in the vcf files. Annotation information will also be visible in the View variants Variant report and the Cohort mutation summary report

Annotate variants (SnpEff) dialog

The task dialog for Annotate variants (SnpEff) contains two sections: Select SnpEff database and Advanced options. Select SnpEff database will specify the reference assembly to utilize for variant detection. If the variant detection was performed in Connected Multiomics, the Assembly will be displayed as text in the section, and you do not have the option to change the reference. In the event that variant detection was performed outside of Connected Multiomics, you will need to select the appropriate Assembly utilized for variant detection in the drop-down list. Assemblies previously added will be available for selection or New assembly… can be utilized to import the reference sequence within the task. Select SnpEff database will allow selection of databases utilized for prediction, and Connected Multiomics provides automated download of a limited number of these databases. Databases previously added will be available for selection or Add SnpEff variant database in the menu can be utilized to import the reference sequence within the task. Additional information of SnpEff databases can be found in the .

Advanced options provides a means to tune parameters for annotation generated from the SnpEff database. Upon invoking the task dialog, Option set is set to Default, and these parameters are prescribed by the developers of SnpEff. Clicking Configure will open a window to tune advanced options. SnpEff has Advanced options for Results filter options, Annotation options, and Database options. Moving the mouse cursor over the info button will provide details for each parameter.

References

1. Cingolani P, Platts A, Wang LL, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80-92.

Due to fundamental differences between the statistical models employed by different variant detection tools, as well as varying parameter optimizations within tools selected for discrete discovery goals, there can be a large number of unique and common variants identified between each instance of variant detection. In some cases the goal of the study is to provide a list of all possible variants, whereas in other studies the goal is to generate a list of variants with increased confidence of true polymorphic sites. To facilitate both possible goals, Combine variants tool generates either the union or intersection of two variants data nodes. This task provides a means to identify common and unique variant calls in samples that have undergone two discrete variant calling tasks. The Combine variants task can be invoked from any Variants or Annotated variants data node, assuming at least discrete variants node(s) exists in the analysis. The task will generate two new variant data nodes and underlying vcf files: one for the union and one for the intersection of the variant data.

To run the task, select a Variants data node and then click the Combine variants from the task menu. The task dialog will allow you to select a second Variants data node to be combined with the first. The selection allows for only one other data node to be used in the task. If there are no other valid variant tasks available within the project, a message stating "No connections to upstream task found" will be displayed.

Currently, this is the only task in Connected Multiomics that requires two input data nodes and then generates two output data nodes.

The imported count matrix report is a summary report on sample distribution information of imported counts matrix data e.g. ILMN miRNA count matrix data

• Click an imported miRNA data node

• Click the QA/QC section of the toolbox

• Click Imported count matrix report

A new task node is generated with the Imported count matrix report. Double click on the report to open it.

In the Feature distribution table title, it displays the size of the matrix, number of samples and number of features. Each row is a sample in the table, columns contains descriptive statistics of features in the sample.

If there are less than 30 samples in the data node, a bar chart is presented. Each bar is a sample. The X-axis is the read count range, Y axis is the number of features within the range. Hovering your mouse over the bar displays the following information:

• Sample name

• Range of read counts, “[ “represent inclusive, “)” represent exclusive, e.g. [0,0] means 0 read counts; (0,10] means the range is greater than 0 count but less than and equal to 10 counts.

• Number of features within the read count range

• Percentage of the features within the read count range

A Box-whisker plot is displayed below the bar chart. In the box-whisker plot, each box is a sample on X-axis, the box represents 25th and 75th percentile, the whiskers represent 10th and 90th percentile, Y-axis represents the feature counts, when you hover over each box, detailed sample information is displayed

• Sample name

• Range of read counts, “[ “represent inclusive, “)” represent exclusive

• Number of features within the read count range in the sample

Latent semantic indexing (LSI) was first introduced for the analysis of scATAC-seq data by Cusanovich et al. 2018[1]. LSI combines steps of frequency-inverse document frequency (TF-IDF) normalization followed by singular value decomposition (SVD). Connected Multiomics wrapped Signac's TF-IDF normalization for single cell ATAC-seq dataset. It is a two-step normalization procedure that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks[2].

To run TF-IDF normalization,

• Click a single cell ATAC counts data node

• Click the Normalization and scaling section in the toolbox

• Click TF-IDF normalization

The output of TF-IDF normalization is a new data node that has been normalized by log(TF x IDF). We can then use this new normalized matrix for downstream differential analysis and/or visualization.

References

1. Cusanovich, D., Reddington, J., Garfield, D. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

What is Compare clusters?

Compare clusters is a tool to identify the optimal number of clusters for K-means Clustering using the Davies-Bouldin index. The Davies-Bouldin index is a measure of cluster quality where a lower value indicates better clustering, i.e., the separation between points within the clusters is low (tight clusters) and separation between clusters is high (distinct clusters).

Running Compare clusters

We recommend normalizing your data prior to running Compare clusters, but the task will run on any counts data node.

• Click the counts data node

• Click the Exploratory analysis section of the toolbox

• Click Compare clusters

• Configure the parameters

The parameters for Compare clusters are the same as for .

Compare clusters task report

The Compare clusters task report is line chart with the number of clusters on the x-axis and the Davies-Bouldin index on the y-axis.

The Compare clusters task report can be used to run K-means clustering.

• Click a point on the plot to select it or type the number of clusters in the text box Partition data into clusters

Selecting a point sets it as the number of clusters to partition the data into. The number of clusters with the lowest Davies-Bouldin index value is chosen by default.

• Click Generate clusters to run K-means clustering with the selected number of clusters

A K-means clustering task node and a Clustering result data node are produced. Please see our documentation on for more details.

Seurat v3[1] introduced new methods for the integration of multiple single-cell datasets, no matter whether they were collected from different individuals, experimental conditions, technologies, etc. Seurat 3 integration method aims to use a subset of the data as reference for the integrate analysis. The method integrates all other data with the reference subset. The subset can be one sample or a subgroup of samples defined by the factor attribute.

Seurat3 integration in Flow can be invoked in Batch removal section if a Normalized counts data node is selected.

To run Seurat3 integration,

• Click a Normalized counts data node

• Click the Batch removal section in the toolbox

• Click Seurat3 Integration

You will be promoted to pick some attribute(s) for analysis. The first Seurat3 integration dialog is a drop-down list that includes the factors for data integration. To set up the model, choose the attribute represents the batch factor, and click Finish.

The output of Seurat3 integration is a new data node - Integrated counts. We can then use this new integrated matrix for downstream analysis and visualization.

Users can click Configure to change the default settings In Advanced options.

Use reference to find anchors: when this box is checked, the first group of the selected attribute is used as reference to find anchors. To use a different group as reference, change the order of subgroups of the attribute in the attribute management page on Metadata tab. When the box is unchecked, anchors will be identified by comparing all pairs of subgroups, this option is very computationally intensive.

Perform L2 normalization: Perform L2 normalization on the CCA cell embeddings after dimensional reduction.

Pick anchors: How many neighbors to use when picking anchors.

Filter anchors: How many neighbors to use when filtering anchors.

Score anchors: How many neighbors to use when scoring anchors.

Nearest neighbor finding methods: Method for nearest neighbor finding. Options include: rann, annoy.

References

1. Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell, 2019. DOI:

Variations in nucleotide sequence, in the form of single nucleotide variants (SNVs) and insertion and deletion events (INDELs), can either be neutral in nature or can have functional effects. Connected Multiomics provides all the tools necessary to interrogate and prioritize variants for further analysis. Variants stored in Variant Call Format (vcf) files can be analyzed to filter, annotate, summarize, visualize, and validate your panel of identified variants. Multiple vcf processing tools are available under the Variant analysis section of the context sensitive menu.

• Annotate Variants

• Annotate Variants (SnpEff)

Pseudobulk task combines expression values from all cells of a particular cell type classification for each sample. In essence, it creates virtual bulk level data from single cell level data. Because it is virtual bulk data, all the same tasks that can be performed on bulk level gene counts data can be performed on the output of Pseudobulk task.

Pseudobulk makes it easy to compare gene or protein expression for a cell type of interest between experimental groups.

Before running Pseudobulk , you must classify the cells. To run Pseudobulk , select the data node with your classified cells and invoke this task.

Select how you would like to group the cells, by default sample name is selected, this option allows you to pool all the cells in a sample to generate sample level expression. You can add other attribute from the drop-down list, e.g. cell type, then it will pool cells in each cell type within one sample as sample level expression on all the features.

Agrreation method option are Sum, Maximum, Mean, and Median. Expression values for cells from the same sample with the same cell type classification will be merged using the chosen pooling method. If the input data node contains raw count, Sum is recommended; if the input data node contains normalized count, Mean or Median will make more sense

After clicking Finish, a Pseudobulk data node will be generated which contains bulk level expression data.

Attribute report

This task is only available on single cell matrix data node. It will summarize the cell level attributes of a data node and the result displayed in two tables with one table containing the categorical attributes while the other table contains the numerical attribute.

Running attribute report

Click on any single cell count data node and select Attribute report from the Annotation/Metadata task menu

Double click on the result node to view the tables.

The result of the attribute report summarize the categorical and numerical attributes in two tables.

To download a text-file version of one of the tables, click Download in upper-left corner of the table.

The Cell barcode QA/QC task lets you determine whether a given cell barcode is associated with a cell. This is an important QC step in all droplet-based single cell RNA-seq experiments, where all barcodes are sequenced.

To invoke Cell barcode QA/QC:

• Click a Single cell counts data node

• Click the QA/QC section of the task menu

• Click

ATAC-seq and 5-base DNA methylation analysis generate regions in genome. To better understand roles of the regions in regulating gene expression, we can use Annotate regions task to add information about overlapping or nearby genomic features to the regions.

Running Annotate regions

The input for Annotate regions task is a region data node.

• Click to select a regions data node.

Powerful Partek Flow statistical analysis tools help identify differential expression patterns in the dataset. These can take into account a wide variety of data types and experimental designs.

This method is based on general linear model, much like ANOVA in reverse, it calculates the variation attributed to the factor(s) being removed then adjusting the original values to remove the variation.

By including batch in the differential analysis model, the variability due to the batch effect is accounted for when calculating p-values. In this sense, batch effects are best handled as part of the differential analysis model. However, clustering data or visualizing biological effects can be very difficult if batch effects are present in the original data. We transform the original values to remove the batch effect using this tool.

We recommend normalizing your data to log normal distribution prior to removing batch effects using this method, but the task will run on any counts data node.

• Click the counts data node

• Click the Batch removal

Welche's ANOVA is similar to one-way ANOVA, but it doesn't have equal variance assumption. It applies to data with an attribute has more than two groups, but the variances among the groups are not equal. It is based on weighted means. When the attribute has only two groups, it is equivalent to the unequal variance t-Test (also known as Welch's t-Test).

Running the task

To invoke Welch's ANOVA, select any count-based data nodes, these include:

• Gene counts

Principal components analysis (PCA) is an exploratory technique that is used to describe the structure of high dimensional data by reducing its dimensionality. It is a linear transformation that converts n original variables (e.g. genes/transcripts/protein) into n new variables, which are called PCs, they have three important properties:

• PCs are ordered by the amount of variance explained

• PCs are uncorrelated

• All PCs explain all variation in the data

PCA is a principal axis rotation of the original variables that preserves the variation in the data. Therefore, the total variance of the original variables is equal to the total variance of the PCs.

Both Kruskal-Wallis and Wilcoxon tests are rank tests, such rank-based tests are generally advised for use with larger sample sizes. They both can only take one factor into account at a time. Kruskal-Wallis can perform on an attribute with two or more subgroups.

Wilcoxon test is a close alternative to Kruskal-wallis task, match the results of . This test is also called "Wilcoxon Rank-Sum Test" or "Mann-Whitney U Test". When you perform comparisons on the two groups, it will filter only include the two groups first and then perform the differential analysis.

Running the task

To invoke the Kruskal-Wallis test, select any count-based data nodes, these include:

The Feature distribution plot visualizes the distribution of features in a counts matrix data node.

Running Feature distribution

To run Feature distribution:

• Click a counts data node

SC transform task performs the variance stabilizing normalization proposed in [1]. The task's interface follows that of SCTransform() function in R [2]. SCTransform v2 [3] provides the ability to perform downstream differential expression analyses besides the improvements on running speed and memory consumption. v2 is the default method.

We recommend performing the normalization on a single cell raw count data node. Select SCTransform task in Normalization and scaling section on the pop-up menu to invoke the dialog.

By default, it will generate report on all the input features. Unchecking the Report all features, user can limit the results to a certain number of features with highest variance.

In Advanced options, users can the click Configure to change the default settings.

Scale results: Whether to scale residuals to have unit variance; default is FALSE

Center results: When set to Yes, center all the transformed features to have zero mean expression. Default is TRUE.

The algorithm details for DESeq2 can be found at the external .

If the value of the raw count includes a decimal fraction, the value will be rounded to an integer before DESeq2 is performed. Before you run this task, we recommend that you first remove (filter out) features expressed at a low level and then perform normalization using Median ratio (DESeq2 only).

Note: DESeq2 differential analysis can only be performed on the following normalization output data node, those methods can produce library sizes:

TMM, CPM, Upper Quartile, Median ratio, Postcounts

Configuring DESeq2

What is AUCell?

is a tool to identify cells that are actively expressing genes within a gene list [1]. For each input gene list, AUCell calculates a value for each cell by ranking all genes by their expression level in the cell and identifying what proportion of the genes from the gene list fall within the top 5% (default cutoff) of genes. This method allows the AUCell value to represent the proportion of genes from the gene list that are expressed in the cell and their relative expression compared to other genes within the cell. Because this is a rank-based method and is calculated for each cell individually, AUCell can be run on raw or normalized data. As an AUCell value is the proportion of genes from the list that are within the top percentile of expressed genes, AUCell values can range from 0 to 1, but may have a more restricted range.

AUCell values can be used directly as input for downstream analysis, such as clustering. Another common use is to set an AUCell value cutoff for expressing vs. not and used this to classify cells. AUCell values will separate cells most effectively when the genes in the list are highly and specifically expressed in a population of cells. If the genes are specifically expressed, but not highly expressed, the AUCell value will not be as useful.

Connected Multiomics offers a wide variety of tools to help you explore your data. Which tools are available depends on the type of data node selected.

What is t-SNE?

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a dimensional reduction technique [1]. t-SNE aims to preserve the essential high-dimensional structure and present it in a low-dimensional representation. t-SNE is particularly useful for visually identifying groups of similar samples or cells in large high-dimensional data sets such as single cell RNA-Seq.

Running t-SNE

Library size normalization is the simplest strategy for performing scaling normalization. But composition biases will be present when any unbalanced differential expression exists between samples. The removal of composition biases is a well-studied problem for bulk RNA sequencing data analysis. However, single-cell data can be problematic for these bulk normalization methods due to the dominance of low and zero counts[1]. To overcome this, Connected Multiomics wrapped the calculateSumFactors() function from R package . It pools counts from many cells to increase the size of the counts for accurate size factor estimation. Pool-based size factors are then “deconvolved” into cell-based factors for normalization of each cell’s expression profile[1].

To run Scran deconvolution,

• Click a single cell counts data node

• Click the Normalization and scaling section in the toolbox

Variant detection can identify large numbers of variants, dependent both on the size of the regions being interrogated and the parameters utilized during detection. As such, filtering variants is often a necessary task to aid in identifying variants that may be relevant for downstream investigation. The Filter variants task enables users to filter variant data both in regards to quality metrics generated during detection and annotation information. The task can be invoked from any Variants or Annotated variants data node.

Filter variants dialog

The Filter variants task dialog can contain two to five sections, dependent on the variant caller used for detection and the level of annotation. All instances of the task will include the following: Include region overlapping variants and a section for Quality.

The Get Regional Methylation task uses a region annotation (.bed) file to generate regions from CpG methylation count data. For each region specified in the annotation file, methylation counts of all CpG sites within the region are averaged to produce a count value in a regions-by-samples count matrix.

Using a built-in promoter regions file

A built-in annotation model based on human (hg38) promoter regions from ENSEMBL version 114 is provided. To invoke Get Regional Methylation task,

• Click on 5-base Methylation

The Annotate variants task provides a means to add information with regards to genomic features, such as transcript models, and existing variant databases to the variants contained in the projects. This information can be useful for filtering, interpreting, and prioritizing variants for downstream investigation. The Annotate variants task can be invoked from any Variants or Annotated variants data node, and the task will be added to and supplement any existing annotation in the underlying vcf files. Annotation information will also be visible in the downstream View variants Variant report.

Annotate variants dialog

The task dialog for Annotate variants contains three sections: Assembly, Annotate with genomic features, and Annotate with known variants

An important aspect of variant analysis is the ability to prioritize variants for downstream analysis. As variant detection can often identify a large number of variants, it may be difficult to determine which variants may impact phenotypes. As implemented in Connected Multiomics, the Ensembl Variant Effect Predictor (VEP, version 84) [1] provides a means to add detailed annotation to variants in the analysis such as discrete aspects of transcript models and variant databases not available in the task. For variants identified in human data, information from popular tools that predict the impact of variants that cause amino acid changes, SIFT [2] and PROVEAN [3] (available for the hg19 genome assembly), will be included. VEP databases can be obtained for multiple species, and content will be dependent on available transcript and variant information for that organism. The Annotate variants (VEP) task can be invoked from any Variants or Annotated variants data node, and the task will supplement any existing annotation in the vcf files. Annotation information will also be visible in the downstream View variants Variant report .

Annotate variants (VEP) dialog

Filter samples or cells depends on the input data in order to perform downstream analysis on a subset of data.

Click a count matrix or single cell counts data node, in the Filtering section of the pop-up menu, and choose to Filter samples (bulk data) or Filter cells (single cell data).

The dialog lets you build a series of filters based on sample or cell attributes.

Click Finish to apply the filter. If no sample or cell will pass the filter criteria, a warning message will appear and the task will not run.

Filter by metadata

The first drop-down menu allows you to choose to

This normalization is performed on observations (samples) using internal control features (genes). The internal control features, usually housekeeping genes, should not vary among samples[1].

Note: The input data node must contain all positive values to compute geometric mean.

Select Normalize to housekeeping genes task in Normalization and scaling section in the pop-up menu when you select a count matrix data node, the dialog will list all the features included in the data node on the left panel

Select control genes on the left panel and move them to the right panel. You can also use search box to find the feature and click the plus button to add it to the right panel.

Click Finish.

The implementation details is as follows:

Let's use S represents sample, F represents feature (gene), G represents geometric mean, n represents number of samples

To have data to be used as input for Connected Multiomics, data can be:

• Generated from a DRAGEN pipeline

• Added from ICA project

• Uploaded from local drive

Use data from DRAGEN pipeline

Connected Multiomics uses data from DRAGEN pipelines within the Illumina Connected ecosystem.

When kicking off DRAGEN pipelines in BaseSpace, ensure the same workgroup is used in BaseSpace as Connected Multiomics so that the DRAGEN outputs are visible to Connected Multiomics. To change the workgroup on BaseSpace, select the desired workgroup in the top right-hand corner.

If DRAGEN pipeline are kicked off in ICA or in BaseSpace using Personal, these results can be shared with the workgroup by adjusting the permissions set as indicated in the section.

Add data from ICA project

Connected Multiomics inputs are the results of secondary analysis pipelines that are stored on projects in (ICA). Secondary analysis results may be generated from , run manually or come from such as legacy pipelines or commercial applications.

To access ICA in your software, select the Illumina Connected Analytics application tile from your Product Dashboard. For guidance on uploading and managing data in ICA, please refer to the instructions . For data types and files format supported for analysis in the Connected Multiomics, please refer .

To ensure users can proceed smoothly with their data exploration in Connected Multiomics, the workgroup that is in use in Connected Multiomics needs to be added to the ICA Project(s) at settings, with the following permissions set:

• Contributor or higher

• permissions

• permissions

After data files are uploaded and workgroup is added, you may click on the Product Dashboard icon to navigate to Illumina Connected Multiomics to start working on your data.

Add data from local drive

In a Connected Multiomics study, click on Add Data and choose Upload local files

There are two options:

• Upload files and add to study: the wizard will guide you create samples in a study after the files are uploaded

• Upload files on: files will be stored in an ICA project. If you want to create samples from those files later, you need to choose "Select from ICA project" when you create samples

• When choosing the first option, data type needs to be specified

• From the data type drop-down list, specify technology, assay from the menu and the supported file types will be displayed

Select file format and click Continue

The files will be uploaded to ICA, specify an ICA project folder and drag & drop files from your local device or click on the Browse Files button to select files, click Upload to ICA

A progress bar will be displayed during uploading, once it is done, the status is displayed as Uploaded

Files will be uploaded to the folder of the ICA project name, subfolder "uploads" and ICM project name subfolder

Click on Ingest to Study to generate samples in the study.

Transfer data from public BaseSpace

If the data is in BaseSpace in public domain, see on how to transfer data to an enterprise domain.

The 5-base methylation QC task in the Connected Multiomics enables you to visualize sample-level QC metrics that describe reads mapping quality and CpG methylation calling. The QC metrics are extracted from the DRAGEN analysis metric files that were ingested into the study as required files for 5-base DNA Prep data analysis in the Connected Multiomics. To invoke the 5-base methylation QC task:

• At Analyses page, click on the 5-base Methylation node.

• Click QA/QC section in the context-sensitive task menu on the right.

• Click 5-base methylation QC.

There is no parameters setting required for the 5-base methylation QC task. After click on the 5-base methylation QC task from the context-sensitive task menu, a task node called 5-base methylation QC report is initiated. When completed, double-click on the 5-base methylation QC report task node to open the QC report in a data viewer. The QC report consists of plots and tables organized in 2 sheets. Click on sheet name at the bottom of the data viewer to navigate from one sheet to another.

Metrics

Sheet Metrics shows sample-level QC metrics plot. Each sample is a data point, they are randomly spead out on x-axis. The QC metric is represented by y-axis. Each plot is overlay with a violin plot to show distribution of the QC metrics.

• Percent methylation in samples: Percentages of CpG methylation in samples.

• Percent methylation in unmethylated control: Percentage of CpG methylation in the unmethylated control (lambda). Low value indicates good quality.

• Percent methylation in methylated control: Percentage of CpG methylation in the methylated control (pUC19). High value indicates good quality.

• Percent duplicate reads: Percentage of duplicate marked reads, as a result of PCR amplification.

In Metric sheet, samples can be selected using Selection > Select & Filter. The Select & Filter dialog is pre-loaded with the selection criteria, one for each QC metric.

Hovering the mouse over one of the selection criteria reveals a histogram showing you the frequency distribution of the respective QC metric. The minimum and maximum thresholds can be adjusted by clicking and dragging the sliders or by typing directly into the text boxes for each selection criteria.

Adjusting the selection criteria will select and deselect samples in all 6 plots simultaneously. Depending on your settings, the deselected points will either be dimmed or gray. The filters are additive. Combining multiple filters will include the intersection of the the filters. The number of samples selected is shown in the figure legend of each plot.

To filter the dataset to the selected samples, click the include selected points icon ( ) in Filter in the top right of Select & Filter, and click Apply observation filter...

Select the input data node for the filtering task and click Select.

A new data node, Filtered samples, will be generated under the Analyses tab.

M-bias

Sheet M-bias shows M-bias plots for methylation level and coverage across positions on read1 and read2. The M-bias should be consistent across all positions. It is common for the first/last 10 bases to have un-even methylation due to end-repair and sequencing artifacts.

All plots in one data viewer screen can be downloaded into local computer as a single image by clicking Export button on the top of the screen. To download an individual plot into local computer, select the plot, click Plot button from the left panel within the plot, then click Export, follow the wizard to set image file format, image size, and resolution.

What is K-means clustering?

K-means clustering is a method for identifying groups of similar observations, i.e. cells or samples. K-means clustering aims to group observations into a pre-determined number of clusters (k) so that each observation belongs to the cluster with the nearest mean. An important aspect of K-means clustering is that it expects clusters to be of similar size (equal variance) and shape (distribution of variance is spherical). The Compare Clusters task can also be used to help determine the optimal number of K-means clusters.

Running K-means clustering

We recommend normalizing your data prior to running K-means clustering, but the task will run on any counts data node.

• Click the counts data node

• Click the Exploratory analysis section of the toolbox

• Click K-means clustering

• Configure the parameters

Distance metric

Choose which distance metric to use for cluster distance calculations. Options include Euclidean, Absolute Value, Euclidean Squared, Kendall Correlation, Max Value, Min Value, Pearson Correlation, Rank Correlation, Average Euclidean, Shape, Cosine, Canberra, Bray Curtis, Tanimoto, Pearson Correlation Absolute, Rank Correlation Absolute, and Kendall Correlation Absolute. The default is Euclidean.

Number of clusters

Choose between specifying a set number of clusters or a range to test for the best fit number of clusters. The best fit is determined by the number of clusters with the lowest Davies–Bouldin index. The default is set to 10 for a fixed number of clusters. The initial values for the range option are 3 to 20 clusters. When this option is selected, will be performed.

Compute biomarkers

Compute biomarkers task will be performed after this task run if the checkbox is selected. The attribute used in is the k-means cluster annotation.

Split cells by sample

This option is present in single cell data. If enabled, K-means clustering will be run separately for each sample. If disabled, K-means clustering will be run on all cells from the input data. Default is set by the Split single cell by sample option in the user preference page.

K-means clustering produces a K-means Clusters result data node; double-click to open the task report which lists the cluster statistics. If clustering was run with Split by sample enabled on a single cell counts data node, the cluster results table displays the number of clusters found for each sample and clicking the sample name opens the sample-level report.

The total number of clusters is listed along with the number and percentage of cells in each cluster.

The K-means Clustering result data node includes the input values and adds cluster assignment as a new attribute, K-means, for each observation.

Advanced K-means clustering parameters

Random cluster initialization

If enabled, the initial cluster centroids will be selected randomly from among the data points. If disabled, the initial cluster centroids will be selected to optimize distance between clusters. Default is Disabled.

Random seed

This sets the random seed used if Random cluster initialization is enabled. Use the same random seed to reproduce results.

Batch centroid computations

If enabled, all cluster centroids will be recomputed at the end of each iteration. If disabled, each cluster centroid will be recomputed as the members of the cluster change. Default is Enabled.

Max iterations

The maximum number of iterations to perform before setting on a set of clusters. Default is 1000.

In Connected Multiomics, we use tools from Monocle 2 [1] to build trajectories, identify states and branch points, and calculate pseudotime values. The output of Trajectory analysis includes an interactive scatter plot visualization for viewing the trajectory and setting the root state (starting point of the trajectory) and adds a categorical cell level attribute, State. From the Trajectory analysis task report, you can run a second task, Calculate pseudotime, which adds a numeric cell-level attribute, Pseudotime, calculated using the chosen root state. Using the state and pseudotime attributes, you can perform downstream analysis to identify genes that change over pseudotime and characterize branch points.

Prerequisites for trajectory analysis

Note that trajectory analysis will only work on data with <600,000,000 elements in the matrix (number of cells × number of features). If your data set exceeds this limit, the Trajectory analysis task will not appear in the toolbox. Prior to performing trajectory analysis, you should:

1) Normalize the data

Trajectory analysis requires normalized counts as the input data. We recommend our default "CPM, Add 1, Log 2" normalization for most scRNA-Seq data. For alternative normalization methods, see our documentation.

2) Filter to cells that belong in the same trajectory

Trajectory analysis will build a single branching trajectory for all input cells. Consequently, only cells that share the biological process being studied should be included. For example, a trajectory describing progression through T cell activation should not include monocytes that do not undergo T cell activation. To learn more about filtering, please see our documentation.

3) Filter to genes that characterize the trajectory

The trajectory should be built using a set of genes that increase or decrease as a function of progression through the biological processes being modeled. One example is using differentially expressed genes between cells collected at the beginning of the process to cells collected at the end of the process. If you have no prior knowledge about the process being studied, you can try identifying genes that are differentially expressed between clusters of cells or genes that are highly variable within the data set. Generally, you should try to filter to 1,000 to 3,000 informative genes prior to performing trajectory analysis. The list manager functionality is useful for creating a list of genes to use in the filter. To learn more, please see our documentation on .

Parameters

Dimensionality of the reduced space

While the trajectory is always visualized in a 2D scatter plot, the underlying structure of the trajectory may be more complex and better represented by more than two dimensions.

Scaling

You can choose to scale the genes prior to building the trajectory. Scaling removes any differences in variability between genes, while not scaling allows more variable genes to have a greater weight in building the trajectory.

Task report

Click on the task report, a 2D scatterplot will be opened in Data viewer.

The trajectory is shown with a black line showing the trajectory. Branch points are indicated by numbers in black circles. By default, cells are colored by state. You can use the control panel on the left to color, size, and shape by genes and attributes to help identify which state is the root of the trajectory.

Calculating pseudotime

To calculate pseudotime, you must choose a root state. The tip of the root state branch will have a value of 0 for pseudotime. Click any cell belonging to that state to select the state. The selected state will be highlighted while unselected cells are dimmed. Choose Calculate pseudotime in the Additional actions on the left control panel.

The Calculate pseudotime task will be performed, it generates a new Pseudotime result data node, which contains Pseudotime annotation for each cell. Open the Pseudotime result report, a 2D scatterplot will be displayed in data viewer, colored by Pseudotime by default.

References

[1] Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah Pliner, and Cole Trapnell. Reversed graph embedding resolves complex single-cell developmental trajectories. Nature methods, 2017.

CellPhoneDB addresses the challenges of studying cell-cell communication in scRNA-seq and spatial data. It allows researchers to move beyond just measuring gene expression and delve into the complicated cellular communication world. By analyzing the scRNA-seq or spatial data through the lens of CellPhoneDB, researchers can identify potential signaling pathways and communication networks between different cell types within the sample. Connected Multiomics wrapped the statistical analysis pipeline (method 2) from CellPhoneDB v5 [1][2] for this purpose.

How to use CellPhoneDB

Invoke the CellPhoneDB task from a normalized counts data node using the Exploratory analysis section. We recommend running CellPhoneDB on the log normalized data directly.

To run CellPhoneDB task,

• Click a Normalized counts data node

• Click the Exploratory analysis section in the toolbox

• Click CellPhoneDB

Species

Currently only human, mouse and rat are supported. Select the species of the data from the drop-down list.

Cell type

Select the attribute from the drop down. Any categorical attribute associated with the data can be selected, though typically the task is performed on cell typing results.

Micro environment

The micro environment file is typically used when analysing spatial data (see the tool's ), and it is optional. The micro environment information can be added to the task using the text box in the task. This can be simply copy-pasted from a micro environment file, a .txt with two columns indicating which cell type (1st column) is in which spatial microenvironment (end column).

P-value

Specify the p-value cutoff to employ for significance

Threshold

By default, the value of 0.10 will be used as threshold to select which cells are used for the analysis in the cluster. However, the number could be adjusted manually or typed in directly.

Click the Finish button if you want to run the task as default.

Double click the CellPhoneDB result data node will open the task report in Data Viewer. It is a heatmap that summarizes how many significant interactions identified in the cell type pairs.

To explore more, the task of Explore CellPhoneDB results under Exploratory analysis on the pop-up menu. It allows you to focus on specific cell type pairs and genes of interest. Genes of interest are data dependent and usually come from the published results of similar studies or the differential gene analysis between different conditions (eg, cancer patient vs healthy controls). Once set up, click the Finish button to submit the job.

Double click the Output matrix data node will open the task report in Data Viewer. It is another variant of heatmap that displays how genes of your interest interact in the defined cell type pairs.

The exampled plot also indicates the data are from two environments. For instructions on setting up the Micro environment file for your spatial study. CellPhoneDB analysis classifies signaling pathways for genes of interest. These classifications are then used to annotate the heatmap within the task report.

Why are the values of clusterA-clusterB different to the values of clusterB-clusterA?

It is important to note that the interactions are not symmetric. The authors state that, "Partner A expression is considered for the first cluster/cell type (clusterA), and partner B expression is considered on the second cluster/cell type (clusterB). Thus, IL12-IL12 receptor for clusterA-clusterB (i.e. the receptor is in clusterB) is not the same as IL-12-IL-12 receptor for clusterB-clusterA (i.e. the receptor is in clusterA), and will have different values." [3][4]

Where do the interactions come from?

The interactions come from the CellphoneDB database. It is manually curated repository using reviewed molecular interactions with demonstrated evidence for a role in cellular communication. [5]

References

1. Troule, etc (2023). CellPhoneDB v5: Inferring cell-cell communication from single cell multiomics data.

A common task in bulk and single-cell analysis is to filter the data to include only informative features before downstream analysis. Feature here means the measurement of observation, like gene expression, protein expression etc.

There is no gold standard for what makes a feature informative or not, and ideal feature filtering criteria depend on your experimental design and research question, Connected Multiomics has a wide variety of flexible filtering options.

Filter features task can be invoked from any counts or single cell data node. The filter is applied to the values in the selected data node and the output is a filtered version of the input data node.

In the task dialog, select the filter option to activate the filter type and configure the filter, then click Finish to run.

Noise reduction filter

The Noise reduction filter lets you exclude features that meet specified descriptive statistics criteria.

you can choose are:

• Geometric mean

• Maximum

• Mean

• Median

For each of these you can choose to exclude features that are:

• <: less than

• <=: less than or equal to

• == equal to

• >: greater than

The threshold is set using the text box. The input must be a number; it can be an integer or decimal, positive or negative.

If you select value, you can also choose a percentage of samples or cells that must meet the criteria for the feature to be excluded.

Statistics based filter

The Statistics based filter lets you include a number or percentile of features based on

Select Counts to specify a number of top features to include or select Percentiles to specify the top percentile of features to include.

Descriptive statistics you can choose are:

• Coefficient of variance

• Geometric mean

• Maximum

• Mean

Feature metadata filter

If the data linked to feature (gene) annotation, different fields in the annotation can be used to filter, e.g. genomic location information, gene biotype information etc.

You can specify "AND" "OR" logical operation using different annotation field information.

Saved list filter

You can filter features based on a feature lists.

You should have a saved list in the page when you use this option. Choose a feature list name from the drop-down menu which displays all the feature lists added in .

You can choose to include or exclude features in any list that you have added.

Use the Feature identifier option to choose which identifier from your annotation matches the values in the feature list.

Manual list filter

This option allows you to type in or paste a list of features, the delimiter should be commas or new lines between the feature names.

This task will generate a filtered counts data node which contains all the samples/cells from the input data but only the features meet the criteria.

This task is only available on single cell matrix data node. It will publish one or more cell level attributes to the project level, so the attribute can be edited and seen from all single cell count data nodes within an analysis. This function can be used on annotate cell task output data node, graph based cluster data node etc.

Running publish cell attributes to project

• Click on a graph-based clusters data node which contains graph-based clustering group result.

• Choose Publish cell attributes to project in the Annotation/Metadata section of the toolbox

This invokes the dialog as

From the drop-down list to select one or more attributes to publish. Only numeric attributes and categorical attributes with less than 1000 levels will be available in the list.

After selection e.g Graph-based attribute from the list , click on the plus button () to add, change the attribute by typing in the New name box

Click Finish at the bottom of the page, all of the attributes will be available to edit on the Data tab > Cell attributes Manage. All data nodes in the project will be able to use those attributes.

The Single-cell QA/QC task in Connected Multiomics enables you to visualize several useful metrics that will help you include only high-quality cells. To invoke the Single-cell QA/QC task:

• Click a Single cell counts data node

• Click the QA/QC section of the task menu

• Click Single cell QA/QC

Hurdle model is a statistical test for differential analysis that utilizes a two-part model, a discrete (logistic) part for modeling zero vs. non-zero counts and a continuous (log-normal) part for modeling the distribution of non-zero counts. In RNA-Seq data, this can be thought of as the discrete part modeling whether or not the gene is expressed and the continuous part modeling how much it is expressed if it is expressed. Hurdle model is well suited to data sets where features have very many zero values, such as single cell RNA-Seq data.

On default settings, Hurdle model is equivalent to MAST, a published differential analysis tool designed for single cell RNA-Seq data that uses a hurdle model [1].

Running Hurdle model

We recommend normalizing you data prior to running Hurdle model, but it can be invoked on any counts data node.

Differential methylation is used to detect differentially methylated CpG loci (DML) or regions (DMR) between two conditions. The method is based on Bioconductor package DSS (Dispersion Shrinkage for Sequencing data), it is a count-based test. Detailed implementation can be found .

This task can be invoked from the imported 5-base Methylation data node, which contains total read count and methylated read count for each CpG site.

Click on 5-base Methylation data node, choose Statistics > Differential Methylation

What is Graph-based clustering?

Graph-based clustering is a method for identifying groups of similar cells or samples. It makes no prior assumptions about the clusters in the data. This means the number, size, density, and shape of clusters does not need to be known or assumed prior to clustering. Consequently, graph-based clustering is useful for identifying clustering in complex data sets such as scRNA-seq.

Variant information is stored on a per sample basis, but it can be informative to view variants in the context of recurrent variants identified within the analysis's sample cohort to identify both the frequency of variants and the samples that share a particular variant. The Summarize cohort mutations task can be invoked from any Variants or Annotated variants data node to generate a report of shared variants identified from detection against a reference sequence or among paired samples.

Summarize cohort mutations dialog

The Summarize cohort mutations task, user needs to specify Minimum coverage for genotype calls. In general, it is likely that if a variant is not called in a sample at a particular locus then the sample has a homozygous reference genotype. Yet this may not always be the case as factors such as insufficient depth or low quality bases at that locus may lead to an inability of the variant caller to identify any genotype at that locus. As such, setting a minimum coverage will make the assumption that the sample contains a homozygous reference genotype if the depth requirement is met. This is done for the purpose of generating genotype calls for all samples (even reference homozygotes) at all variant loci within the project.

Analyses

In the Analyses tab, you'll see all the analyses in your study. The analyses will be displayed in card view by default. Each card will display the following information.

You can switch to list view by clicking the list icon in the top right. In the list view, you can customize the columns and filter your analyses. Click the info icon to get more detail about the analysis including error details.

Create Analysis

To create a new analysis, click .

Give your analysis a name and choose an analysis type from the dropdown menu.

Analysis Options

Next, select the sample, sample group, or all samples for the analysis.

Run Analysis

Click to run the analysis. You will receive a notification that the analysis was successfully created.

Wait for the analysis status to change from "Pending" to "Complete".

Analysis Statuses

The following table describes the different analysis statuses.

Once the analysis is complete, you can click into it to view the results. Refer to the "" section for more information on how to view your analysis.

Data

In the Data tab, you'll see a list of all the data and metadata files within your study from which your samples are derived.

Descriptive statistics task can be invoked on matrix data node e.g. Gene Counts, Normalized Counts data node in bulk RNA seq analysis pipeline or Single Cell counts Data node etc. It calculates measures of central tendency and variability on observations or features of the matrix data.

Running Descriptive statistics

• Click on a matrix data node

• Choose Descriptive Statistics in Statistics section of the toolbox

This will invoke the dialog configuration dialog; use it to specify which calculation(s) will be performed on observations or features.

When select the calculation is for observations (samples or cells), there is a drop-down option to use all the features in the input data node or a list of features. If you use a saved feature list, you can use the check button to select whether match of the saved list to your data is case sensitive or not.

When select the calculation is for features, selecting Group by drop-down list allows to compute the statistics in each group separately

Click on the button to add more than one attributes, the result will be on the groups from the interaction terms of selected attributes.

The available statistics are listed on the left panel, suppose "x1, x2, ..., xn"represent an array of numbers

• Coefficient of variation (CV): s represent the standard deviation

• Geometric mean:

• Max:

• Mean:

Left click to select measurement and drag to move to the right panel one at a time, or when you mouse over on a measurement, click on the + button to move to the right panel and click Finish.

The output data node can be downloaded or visualized in Data Viewer:

The Study overview screen will display the following Study Information:

You will also see the following Data Metrics:

If there are analyses in your study, you will see the Recent Analyses section at the bottom of the Overview screen. For more information on analyses, see the section.

Update Study

To update any details of your study, click the gear icon in the top right. This will open a popup where you can edit the study name, description, ICA project, automatic single sample default analysis and automatic data import options.

If you remove any data types for automatic data import, output files from the pipelines associated with the data types you've removed will no longer be automatically imported into the study upon pipeline completion. On the other hand, if you add a new data type, output files from the corresponding pipelines will now be automatically imported into the study upon pipeline completion.

Click to save the changes.

Add Data

To add data, click the button in the top right. You can select data from an ICA project, , or . Selecting data from an ICA project will open a new screen where you can select your data. At the top of the screen, you'll see the name of your study. Here you can select the data type to import.

For each data type, you will only be able to choose data formats from Illumina or supported third-party formats. Refer to the to see which file extensions are accepted for each data format. TSV metadata files can always be selected. For example, if you choose Bulk > Proteomics as the data type, you will only see ADAT files and TSV files as options for upload.

Each column has a filter icon to filter data and three dots which you can click on to find more options. Select an entire folder or click into the folder to select files individually. Demo data that is used to generate the is available to you to add to your Study by clicking .

Once you've selected your data, click to add the data to your study. Upon submitting, you'll be redirected to the Samples tab, and a message will indicate your data ingestion was successful. Here you can from local storage, from an ICA project, create , or create .

Now, if you click back to the Overview tab, you'll see that the Date Modified was updated and the Data Metrics reflect the newly added files and samples.

Repeat the process to add data for additional data types.

In Connected Multiomics, we use tools from Monocle 3 (1) to build trajectories, identify states and branch points, and calculate pseudotime values. The output of Trajectory analysis task includes an interactive 2D/3D visualization for viewing the trajectory trees and setting the root states (starting points of the trajectories). From the Trajectory analysis report, you can run a second task, Calculate pseudotime, which adds a numeric cell-level attribute, Pseudotime, calculated using the chosen root states.

Prerequisites for the Analysis

Trajectory analysis by Monocle 3 requires normalized counts data. According to the Monocle 3 authors, you may want to filter in the top 5,000 genes with the highest variance (2,000 genes for datasets with fewer than 5,000 cells, and 300 genes for datasets with fewer than 1,000 cells) (1). Those numbers should be used as a guidance for the first-pass analysis and may need to be optimized, depending on the project at hand and the biological question.

Setting up Trajectory Analysis

To run Trajectory analysis tool, select the Normalized counts data node (or equivalent) and go to the toolbox: Exploratory analysis > Trajectory analysis

The configuration dialog presents four options

1. Dimensionality of reduced space. This option specifies the number of UMAP dimensions that the original data are reduced to, in order to learn the trajectory tree (dimensionality of original data equals the number of genes). Default is two, meaning that the trajectory plot will be draw in two dimensions. To get a 3D trajectory plot, increase this option to 3.

2. Scaling. Normalized expression values can be further transformed by scaling to unit variance and zero mean (i.e. converting to Z score). The use of this option is recommended (1).

3. Data is logged. Select this option if the data have already been log-transformed upstream. When selected, Monocle 3 will skip the log2 step on the input data (see below).

Under the hood, Monocle 3 will perform log2 transformation of the gene count matrix (if Data is logged was unselected), scale the matrix (if Scaling was selected), and project the gene count matrix into the top 50 principal components. Next, the dimensionality reduction will be implemented by UMAP (using default settings of the reduce dimension command).

Trajectory Analysis Result

Result of running Trajectory analysis is the Trajectory result data node. Double clicking on the node opens a Data Viewer window with the trajectory plot. Cell trajectory graph shows position of each cell (blue dot) with respect to the UMAP coordinates (axes). Cell trajectories (one or more, depending on the data set) are depicted as black lines. Gray circles are trajectory nodes (i.e. cell communities).

To show / hide cell trajectory tree and trajectory nodes, select Axes in Configure section and at the bottom of the dialog, select the Extra data drop-down options

Pseudotime Analysis

To perform pseudotime analysis, you need to point to the cells at the beginning of the biological process you are interested in. For example, cells at the earliest stage of differentiation sequence. There are two ways to perform pseudotime analysis in Partek Flow, depending on the way the root nodes (=cells at the beginning of pseudotime) are defined.

1. Manual selection of root node. The user has to specify the root nodes (one or more).

2. Automatic selection of the root node. The root node is picked by the algorithm.

Manual Selection of the Root Node

If you want to manually pick the root nodes, leave the option Programmatically calculate default root nodes unselected when setting up the Trajectory analysis.

To start, select the root cell nodes (gray circles in trajectory tree) by left-clicking. If the trajectory result consists of more than one trajectory tree, you can specify more than one root node, e.g. one root node per trajectory tree (ctrl & click). If no root node is specified for a tree, that tree will not be included in the pseudotime calculation. The following dialog shows an example where two root nodes were identified.

Click on Additional button in Tools section on the left panel, push the Calculate pseudotime button in the dialog

As a result, the cells will be annotated by pseudotime, If, for a particular tree, no root node has been identified, those cells will be omitted from the pseudotime calculation and will be colored in black.

Pseudotime calculation display the structure of the graph using black lines. The circles with numbers (cell nodes) on the black lines represent special points. There are three types of cell nodes:

1. Root node (white). Root nodes are start points of the pseudotime and were defined by the user in the previous step.

2. Branch node (black). Branch nodes indicate where the trajectory tree forks out; i.e. each branch represents a different cell fate or different trajectory.

3. Leaf (light gray). Leaves correspond to different cell fates / different trajectory outcomes . The leaves correspond to cell states of .

The numbers within the circles are provided for reference purposes only. The intermediate nodes from the previous step have been removed.

Automatic Selection of the Root Node

If suitable meta-data are available, it is possible to automatically select the root node. For example, you may know which cells were harvested from the earliest time points. The cells need to be annotated by that information ( task) before running Trajectory analysis. The annotation will, in turn, be available in the Trajectory analysis setup dialog, upon selecting the Programmatically calculate default root nodes option.

1. Attribute for root nodes. The drop down list will show the available cell-level attributes. Specify the one which should be used to identify the root nodes.

2. Attribute value for root nodes. The drop down list will show the content of the attribute selected under Attribute for root nodes. Specify the entry that corresponds to the earliest time point

Once the options have been set, Monocle 3 will first group the cells according to which trajectory node they are nearest to. It then calculates the fraction of the cells from the earliest time point at each trajectory node. Finally, it picks the node with the highest prevalence of the early cells and treats it as the root node.

References

1. Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM, Hill AJ, Zhang F, Mundlos S, Christiansen L, Steemers FJ, Trapnell C, Shendure J. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019 Feb;566(7745):496-502. doi: 10.1038/s41586-019-0969-x. Epub 2019 Feb 20. PMID: 30787437; PMCID: PMC6434952.

ANOVA method is applying a specified log normal model to all the features.

• ANOVA dialog

• ANOVA advanced options

• ANOVA report

ANOVA dialog

To setup ANOVA model or the alternative Welch's ANOVA (which is used on normally distributed data that violates the assumption of homogeneity of variance), select factors from sample attribute. The factors can be categorical or numeric attribute. Click on a check button to select and click Add factors button to add it to the model.

LIMMA-trend and LIMMA-voom setup dialogs are identical to ANOVA's setup.

Note: LIMMA-voom method can only be invoked on the following normalization output data node, those methods can produce library sizes:

TMM, CPM, Upper Quartile, Median ratio, Postcounts

When more than one factor is selected, click Add interaction button to add interaction term of the selected factors.

Once a factor is added to the model, you can specify whether the factor is a random effect (check Random check box) or not.

Most factors in an analysis of variance are fixed factors, i.e. the levels of that factor represent all the levels of interest. Examples of fixed factors include gender, treatment, genotype, etc. However, in experiments that are more complex, a factor can be a random effect, meaning the levels of the factor only represent a random subset of all of the levels of interest. Examples of random effects include subject and batch. Consider the example where one factor is type (with levels normal and diseased), and another factor is subject (the subjects selected for the experiment). In this example, “Type” is a fixed factor since the levels diseased and normal represent all conditions of interest. “Subject”, on the other hand, is a random effect since the subjects are only a random sample of all the levels of that factor. When model has both fixed and random effect, it is called a mixed model.

When more than one factor is added to the model, click on the Cross tabulation link at the bottom to view the relationship between the factors in a different browser tab.

Once the model is set, click on Next button to setup comparisons (contrasts).

Start by choosing a factor or interaction from the Factor drop-down list. The subgroups of the factor or interaction will be displayed in the left panel; click to select one or more level(s) or subgroup name(s) and move them to one of the boxes on the right. The ratio/fold change calculation on the comparison will use the group in the top box as numerator, and the group in the bottom box as the denominator. When multiple levels (groups) are in either numerator or denominator box(es), in Combine mode, click on Add comparison button to combine all numerator levels and combine all denominator levels in a single comparison in the Comparison table below; in Pairwise, click on Add comparison button will split all numerator levels and denominator levels into a factorial set of comparisons – in other words, it will add every numerator level paired with every denominator level comparisons to the Comparison table . Multiple comparisons from different factors can be added from the specified model.

ANOVA advanced options

Click on the Configure to customize Advanced options

Multiple test correction sections are the same as the matching GSA advanced option, see above .

Report option

• Use only reliable estimation results: There are situations when a model estimation procedure does not fail outright, but still encounters some difficulties. In this case, it can even generate p-value and fold change on the comparisons, but they are not reliable, i.e. they can be misleading. Therefore, the default of Use only reliable estimation results is set Yes.

• Report p-value for effects: If set to No, only the p-value of comparison will be displayed on the report, the p-value of the factors and interaction terms are not shown in the report table. When you choose Yes in addition to the comparison’s p-value, type III p-values are displayed for all the non-random terms in the model.

• Shrinkage to error term variance: by default, None is select, which is lognormal model. Limma-trend and Limma-voom options are lognormal with shrinkage. (Limma-trend is the same as the GSA default option–lognormal with shrinkage). Shrinkage options are recommended for small sample size design, no random effects can be included when performing shrinkage. If there are numeric factors in the model, the partial correlations cannot be reported on the numeric factors when shrinkage is performed. Limma-trend works well if the ratio of the largest library size to the smallest is not more than 3 fold, it is simple and robust for any type of data. Limma-voom is recommended for sequencing data when library sizes vary substantially, but it can only be invoked on data node normalized using TMM, CPM, or Upper quartile methods while Limma-trend can be applied to normalized data using any method.

ANOVA report

Since there is only one model for all features, so there is no pie charts design models and response distribution information. The Gene list table format is the same as the .

References

1. Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing, JRSS, B, 57, 289-300.

2. Storey JD. (2003) The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31: 2013-2035.

3. Auer, 2011, A two-stage Poisson model for testing RNA-Seq

4. Burnham, Anderson, 2010, Model selection and multimodel inference

During the software registration process, you will create a domain and workgroup. and are used by Illumina Software to control access to different customer’s data and assets. Make sure all users are added to the workgroup and that the workgroup has the necessary permissions to access the Connected Multiomics application, the users need to have the subscription of the software.

Here are the details of the steps to register in video format and as a step-by-step walkthrough:

Raw read counts are generated after quantification for each feature on all samples. These read counts need to be normalized prior to differential expression detection to ensure that samples are comparable.

This chapter covers the implementation of each normalization method. The Normalize counts option is available on the context-sensitive menu upon selection of any data node contains sample/cell by feature matrix.

The format of the output is the same as the input data format, the node is called Normalized counts. This data node can be selected and normalized further using the same task.

Selecting Methods

Select whether you want your data normalized on samples/cells or per features. Some transformations are performed on each value independently of others e.g. log transformation, and you will get an identical result regardless of this choice.

Correlation analysis is used to test the relationship of two numeric variables. It determines the strength and direction of the association between them. The methods included in Connected Multiomics are linear correlation like Pearson's correlation and rank correlation like Spearman's rank correlation, Kendall's Tau correlation.

There are four type of format used to compute correlation: