To analyze scATAC-seq data, Partek^® Flow^® 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 CellPhoneDB[1] 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 tissue sample. Partek^® Flow^® wrapped the statistical analysis pipeline (method 2) from CellPhoneDB v5 for the purpose. 

CellPhoneDB task in Flow can be invoked in Exploratory analysis section by clicking any single cell the normalized counts data node (Figure 1). We recommend running SVD CellPhoneDB on the normalized data directly, particularly the TF-IDF normalized counts for scATAC-seq analysislog normalized data.  

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