scCombineCounts

This tool combines multiple count matrices (output of scCountReads) into one, either assuming they are different samples (multi-sample) or different measurements on the same set of cells (multi-modal). The result is a .loom file with combined counts. NOTE: it doesn’t perform any ‘batch effect correction’ or ‘integration’ of data from different technologies, which requires more sophisticated methods.

usage: Example usage: scCombineCounts -i sample1.loom sample2.loom -o combined.loom  > log.txt

General Options

--input, -i

Input files in .loom format

--outFile, -o

The file to write results to. For method: multi-sample, the output file is an updated .loom object, which can be used by other tools. For method: multi-omic, the output file is an .hdf5 file. This file can only be used by scClusterCells, to perform multi-modal clustering.

--labels, -l

User defined labels instead of default labels from file names. Multiple labels have to be separated by a space, e.g. –labels sample1 sample2 sample3

--method, -m

Possible choices: multi-sample, multi-modal

How to merge the counts from the provided samples. multi-sample: assumes that each sample is the independent, but were counted in the same manner (i.e. on same features), therefore it looks for feature overlaps, but not for barcode overlaps. multi-modal: assumes that the counts were generated in 2 different ways, but from the same set of cells (for example, using a multi-omic technology), therefore it looks for the overlap of cell barcodes, but not for the overlaps of features (Default: “multi-sample”)

Other options

--verbose, -v

Set to see processing messages.

--version

show program’s version number and exit