# Find multimodal neighbors 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 2.