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The Study overview screen will display the following Study Information:
Date Created
The date and time the study was created.
Created By
The user who created the study.
Date Modified
The last date and time the study was modified. This date updates whenever a user performs an action within the study, such as adding data, creating a sample group, starting a new analysis, etc.
Modified By
The user who last modified the study.
ICA Project
The ICA project the study imports data from.
Description
The description of the study, provided by the user during study creation.
You will also see the following Data Metrics:
Sample Files
Metadata Files
The number of metadata files within the study, identified by the .tsv file extension.
Samples
Analyses
The number of analyses within your study, including both in-progress and completed analyses.
Attributed Sample Count
The number of samples in the study with linked metadata.
Metadata Entry Count
The number of metadata attributes displayed in the study. This corresponds with the number of metadata columns shown next to your samples.
If you remove any omic types for auto-ingestion, output files from the pipelines associated with the omic 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 omic type, output files from the corresponding pipelines will now be automatically imported into the study upon pipeline completion.
Protein
.adat
Multiple samples per file
scRNA
.matrix.mtx[.gz]
.(features|genes).tsv[.gz]
.barcodes.tsv[.gz]
One sample per set of files
Any additional descriptors prior to main extensions are supported (eg .scRNA.filtered.matrix.mtx).
Bulk RNA
.sf
.sf.gz
Multiple samples per file
Spatial
.h5ad
.ome.tiff
One sample per set of files
At least one .ome.tiff and multiple .h5ad files are required.
Metadata
.tsv
Multiple samples with metadata attributes per file
You can use the "Columns" tab on the right to add or remove columns and use the "Filters" tab to filter data. Each column also has a filter icon and three dots which you can click on to find more options.
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 omic types.
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. The number at the top indicates how many samples are included in your current view. You can search for a sample using the search bar on the right.
You can also drag and drop columns to reorder them, and click on the filter icons or the three dots next to each column name for additional filtering options.
Sample IDs for proteomic data will be extracted from the sample names provided in the ADAT files. Each file can contain multiple samples. For example, the Sample ID in the ADAT file (left image) corresponds to the sample name in a row of the ICM table (right image). A new sample will be created in ICM for each Sample ID listed in the ADAT file.
Metadata columns are populated based on the information in your metadata TSV file. 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 ICM 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 ICM table.
The following table describes the different sample statuses.
Ingested
The sample has been ingested to the ICM Study but may not have been uploaded to ICM Analysis yet.
Uploading
The sample is in the process of uploading to ICM Analysis. Samples cannot be used for analyses while they are uploading.
Ready
The sample is ready to be used in analysis.
Failed
The sample ingestion failed.
A sample group refers to a collection or subset of samples that are grouped together based on shared characteristics or attributes.
To create a sample group, use the Filters tab in the right-hand side of the "Samples" section. Filter your samples down into only those you want to be part of your sample group.
For example, to create a sample group of women older than 30 years in age, filter the Sex and Age At Collection fields.
Note how the sample number at the top will change to reflect the number of samples that meet these conditions.
You can view all your sample groups by clicking the "Sample Groups" tab in the top. From here, you can search for sample groups, and customize or filter the columns in your view.
You can also click on the action icons to view more details and update your sample groups.
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.
Analysis Name
Name of analysis.
Status
Analysis Type
Type of omic data used in analysis.
Modified By
Last user who modified the analysis.
Date Modified
Date and time when the last update was made.
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.
Give your analysis a name and choose an analysis type from the dropdown menu.
Default: Proteomic
General analysis pipeline for Illumina protein prep samples, with PCA and hierarchical clustering heatmap results
Default: Single Cell Transcriptomic
General analysis pipeline for Illumina single cell prep samples, including the processing steps to output PCA and UMAP visualizations
Default: Spatial Transcriptomics
General analysis pipeline for Illumina spatial prep samples, including a Spatial map with transcripts overlayed on the tissue image where each point is a grid. Graph-based clusters are also plotted on a UMAP and pie chart
Custom: Proteomic
Starts with the quantified samples which have undergone prior normalization and offers flexible analysis options
Custom: RNA
Starts with salmon format sample counts that have not been normalized and offers flexible analyses options. The assembly and annotation model used in secondary analysis is required to annotate the features
Custom: Single Cell Transcriptomics
Starts with single cell counts and offers flexibility with the analyses pipeline step
Custom: Spatial Transcriptomics
Two starting nodes as cell-binned or grid-binned data, including the spatial image outputs, and offers flexibility with the analyses parameters
Next, select the sample, sample group, or all samples for the analysis.
Wait for the analysis status to change from "Pending" to "Complete".
The following table describes the different analysis statuses.
Pending
The analysis has been initiated but has not yet started. It is waiting in the queue to be processed.
In Progress
The analysis is currently being executed. The system is processing the data and generating results.
Complete
The analysis has finished, and the results are ready to be viewed. Click into the analysis to view it.
In the "Data Management" tab, you'll see a list of all the data and metadata files within your study. From here, you can delete files if needed.
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.
A Study is used to manage your samples, metadata, comparison groups, and analyses for a specific set of data.
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.
To filter studies based on specific attributes, click the Filters tab.
You can also filter individual columns by clicking the icon next to each column header.
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.
Select an ICA project to ingest data from. The project list will only display the projects accessible to your workgroup. When adding data to your study, the available data will come from the ICA project you select. Make sure to choose the correct project, as this cannot be updated later.
You can optionally choose to auto-import data into your study. If you select an omic type for auto-import, then whenever a pipeline corresponding to that omic type is completed, the output files will automatically be imported into your study. Note that it will not ingest any existing omic data already in the ICA project; it will only ingest output files from pipelines that complete after the option is selected. The omic types you choose to ingest can be changed later.
Additionally, any new single-cell and spatial data that is added to a study will automatically create sample records and initiate a Default analysis workflow for each sample. For more information, please refer to the assay-specific walkthroughs.
When you enable auto-ingestion, a notification will be created in ICA to subscribe to the pipeline events for the omic type you’ve selected. Do not delete this notification.
The number of sample files within the study, including both manually added and auto-ingested sample files. Please see the for a list of possible sample file extensions.
The number of samples within the study. Note that multiple protein samples can come from a single ADAT file, whereas a single spatial or scRNA sample is created for each set of spatial/scRNA files. Please see the for more information.
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.
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, and auto-ingestion options. Note that the ICA project from which the study receives data cannot be changed.
Click to save the changes.
To add data, click the button in the top right. This will open a new screen where you can upload your data. At the top of the screen, you'll see the name of your study. On the top left, you'll see the name of the ICA project you're loading data from.
Select the omic type from the dropdown menu. For each omic type, you will only be able to select data with file extensions that match the specified extension for that omic type. Refer to the below to see which file extensions are accepted for each omic type. TSV metadata files can always be selected. For example, if you choose "Protein" as the omic type, you will only see ADAT files and TSV files as options for upload.
If you want to upload a metadata file from your local folder, click in the top right. This will add the metadata file directly to your study, rather than ingesting it from an ICA project.
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.
The "Columns" tab on the right-hand side 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.
The "Filters" tab on the right-hand side lets you filter your list based on metadata attributes. This is useful when creating sample groups based on specific attributes.
Sample IDs for scRNA data are obtained by removing the file extension from the file name. As a result, scRNA samples will be paired with their corresponding files based on the sample name. For example, the name of the scRNA files (left image) matches the sample name in a row of the ICM table (right image). Refer to the for information on the required files that make up a single scRNA sample.
Sample IDs for spatial data are derived by removing the file extension from the file name. Thus, spatial samples are linked to their corresponding files through the sample name. For instance, the name of the spatial data files (left image) corresponds to the sample name in a row of the ICM table (right image). Refer to the for details on the required files that make up a single spatial sample.
Now, click to create a sample group off of the filtered attributes. Give the sample group a name and click .
Opens a pop-up with details of your sample group.
Allows you to edit the sample group's name.
Allows you to delete the sample group.
Status of analysis. See for a table of statuses and descriptions.
To create a new analysis, click .
Click to run the analysis. You will receive a notification that the analysis was successfully created.
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.
Analyses can only be created from within a study. See the section for more information on how to create an analysis.
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.
Click the refresh icon in the top right to clear your filters and return to the full list.
For more options, click the three dots.
To Create a new study, clickin the top right and follow the steps below.
Click "Create Study" to create your study. You will be redirected to the new study. For information on how to navigate within your study, see .
Study Name
Name of your study, provided by the user during study creation.
Omic Types
Number of Samples
Number of samples within your study, based on the sample files.
Number of Analyses
Number of analyses within your study, including both in-progress and completed analyses.
Study Description
Description of your study, provided by the user during study creation.
Click into your analysis to see details about your analysis.
In the Analyses tab, you'll be able to see a depiction of your project 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.
The "Sample Metadata" tab displays a table of the samples used in the analysis, along with the sample groups they originated from. You can download the sample data and manage the sample attributes.
The "Log" tab displays a record of tasks from the task graph, including details such as the user who performed the task, as well as the start and end dates.
The "Project Settings" tab displays the details of the analysis, such as its name and description. From here, you can edit the analysis details, but note that the name cannot be changed.
In the "Data Viewer" tab, you can return to your saved sessions or start new sessions. Saved sessions will retain all the graphs and settings from your last use. You can click on a session to continue where you left off, or create a new Data Viewer session to set up a different data view with new graphs.
Omic Types included in your study, determined by the files within your study. This will look at the sample files and assign omic types based on their extensions. See the for a list of omic types and their corresponding file extensions.
See the for walkthroughs on how to run analyses for specific omic types.
Click into the Data Viewer to access your session. For guidancee on how to analyze your data, refer to the .
Click on the "Sample Groups" tab in the left panel to view a list of all the sample groups in your workgroup, spanning across different studies. Use the "Columns" and "Filters" tabs on the right side to customize and filter your view. You can also search for sample groups with the search bar in the top right.
Additionally, you can perform actions on your sample groups, such as updating the name or deleting a group. Note that if you delete a sample group that was used in an analysis, it will not impact the analysis (as long as the sample group was deleted after the analysis was completed).
Sample Groups can only be created from within a study. See the section for more information on how to create a sample group.
Updated the type of analysis automatically created for newly imported single-cell and spatial samples.
Improved caching and updated a configuration for user-assigned subscriptions
Addressed an issue with the user flow for signing the end-user license agreement (EULA)
Intermittent failures with import and analysis tasks.
Non-Illumina assay data is not supported in ICM at this time, including proteomics, single-cell, and spatial.
Support for Illumina Multiomic Assays
Illumina Protein Prep
Illumina Single Cell 3’ RNA Prep
Illumina Spatial Transcriptomics
Illumina DRAGEN RNA (bulk)
Pre-configured & Custom Analysis Workflows
Default Workflows (Pre-configured)
Proteomics: Results in PCA and hierarchical clustering heatmap.
Single-cell Transcriptomics: Results in PCA and UMAP visualizations.
Spatial Transcriptomics: Results in spatial map with transcript overlay; graph-based clusters plotted on a UMAP and pie chart.
Default workflows are automatically initiated for individual single-cell or spatial samples added to an ICM study.
Custom Workflows (User-defined parameters)
Available for all Illumina multiomic assay types.
Includes all default analysis options and additional analysis workflows:
Differential Expression
Pathway Analysis
Gene Set Enrichment
Scalability & Performance Improvements
Up to 10x performance improvement in data import and analysis processing.
Single-cell RNA analyses can now be performed with multiple samples simultaneously.
New samples can be automatically imported into ICM when they are generated as outputs of a DRAGEN secondary analysis pipeline in a linked ICA project.
UI & UX Enhancements
Simplified UI/UX to improve overall usability, reduce complexity, and streamline user flows:
Reworked and consolidated UI components for improved consistency and user experience.
Improved information architecture for easier navigation.
Resolved known issues and made improvements related to technical debt.
Intermittent failures with import and analysis tasks.
Non-Illumina assay data is not supported in ICM at this time, including proteomics, single-cell, and spatial.
