SVD

To analyze scATAC-seq data, Connected Multiomics 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 task can be invoked in Exploratory analysis section by clicking any single cell counts data node. We recommend running SVD on the normalized data, particularly the TF-IDF normalized counts for scATAC-seq analysis.

To run SVD task,

• Click a single cell counts data node

• Click the Exploratory analysis section in the toolbox

• Click SVD

Features to include in calculation

You don't have to use all the features in the computation, especially when the input matrix is very large, this option allows you to choose subset of features based on a selected statistics, the default is using the top 2000 features with the highest variance.

Number of singular values to calculate

When the matrix is large like single cell data, depends on what you would like to do downstream with SVD output, you don't have to compute all the values. Choose less number of values will reduce the running time of this task. By default, it output the top 100.

Click the Finish button if you want to run the task as default.

The task report for SVD is a scatterplot, each dot represents an observation in the input data. The output will be used for downstream analysis and visualization, including Harmony.

References

1. Cusanovich, D., Reddington, J., Garfield, D. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018). https://doi.org/10.1038/nature25981