To analyze scATAC-seq data, Partek Flow introduced a new technique - LSI (latent semantic indexing ).  Singular  LSI combines steps of frequency-inverse document frequency (TF-IDF) normalization followed by singular value decomposition (SVD) on the TD-IDF matrix  .  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 . 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.

If read quantification (i.e. mapping to a transcript model) was performed by Partek^® E/M algorithm, PCA can be invoked on a quantification output data node (Gene counts or Transcript counts) or, after normalization, on a Normalized counts data node. Select a node on the canvas and then PCA in the Exploratory analysis section of the context sensitive menu.

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