In anticipation of using a similar group of functions that was used in the first pass of benchmarking analysis, there was some discussion about creation of an R package to house some commonly used functions to work with the output files from various pre-processing tools. In going back to benchmarking and adding additional samples, it seemed like a good time to break out these functions into a separate R package to use for benchmarking and ultimately import of the scpca counts files for all samples.
We should include functions that can import the counts files into single cell experiments, perform any filtering using emptyDrops, ability to use the splici index and combine counts from spliced cDNA + intronic regions (dependent on type of sample), and calculate general QC metrics currently being used in benchmarking.
In anticipation of using a similar group of functions that was used in the first pass of benchmarking analysis, there was some discussion about creation of an R package to house some commonly used functions to work with the output files from various pre-processing tools. In going back to benchmarking and adding additional samples, it seemed like a good time to break out these functions into a separate R package to use for benchmarking and ultimately import of the scpca counts files for all samples.
We should include functions that can import the counts files into single cell experiments, perform any filtering using
emptyDrops
, ability to use thesplici
index and combine counts from spliced cDNA + intronic regions (dependent on type of sample), and calculate general QC metrics currently being used in benchmarking.