Join us for a webinar: The complexities of spatial multiomics unraveled
May 2
Flow Documentation

Partek Flow Documentation

Page tree

Versions Compared

Old Version 7

changes.mady.by.user Ivan Lukic

Saved on

compared with

New Version Current

changes.mady.by.user Xiaowen Wang

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

...

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 (Figure 1).


Numbered figure captions
SubtitleTextSetting up the Find multimodal neighbors task. Use the Select data node button to point to the second (of two) assay
AnchorNameWNN select node

Image RemovedImage Added

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 bluecolor of the branch, other nodes are disabled (greyed out). To pick a node, left-click on it and then push the Select button.

...

Numbered figure captions
SubtitleTextSelect data node dialog. A node that can be used for WNN is highlighted in blue (in this example, the PCA node on the right)
AnchorNameselect_data_node_WNN

Image RemovedImage Added


The selected data node is shown under the Select data node button. If you made a mistake, use the Clear selection link (Figure 31).

Numbered figure captions
SubtitleTextCurrently selected data node shown under the Select data node button (in this example: PCA). To change the node, use the Clear selection link
AnchorNameselected_data_node

Image Removed

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 31). 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 43). 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.


Numbered figure captions
SubtitleTextAdvanced options of the Find multimodal neighbors
AnchorNameKNN_advanced_options

Image Modified


To launch the Find multimodal neighbors task, click the Finish button on the task setup page (Figure 31). 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 5 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.


Numbered figure captions
SubtitleTextExample of a pipeline with a Find multimodal neighbors task and the resulting WNN data node
AnchorNameWNN_data_node

Image RemovedImage Added

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
  2. https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html