Partek Flow Documentation

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Multi-omics single cell analysis is based on simultaneous detection of different types of biological molecules on the same cells.  Common multi-omics 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. Partek Flow 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 analysisFind 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 (Figure 1).

Figure 1. Setting up the Find multimodal neighbors task. Use the Select data node button to point to the second (of two) assay

When you click the Select data node button, Partek Flow will open another dialog, showing your current pipeline (Figure 2). 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.

Figure 2. Select data node dialog. A node that can be used for WNN is highlighted in blue (in this example, the PCA node on the right)

The selected data node is shown under the Select data node button. If you made a mistake, use the Clear selection link (Figure 1).

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 (Figure 1). 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 (Figure 3). 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.

Figure 3. Advanced options of the Find multimodal neighbors

To launch the Find multimodal neighbors task, click the Finish button on the task setup page (Figure 1). 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, Figure 4 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.

Figure 4. Example of a pipeline with a Find multimodal neighbors task and the resulting WNN data node
For an excellent illustration on advantages of WNN algorithm for identification of cell types, please see this blog post.


  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

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