Read sparse new/total matrices

This function can be used to load matrix market data in case genes were quantified by (i) counting all reads (for total RNA) and (ii) counting T-to-C mismatch reads (for new RNA)

Usage

ReadNewTotal(
  genes,
  cells,
  new.matrix,
  total.matrix,
  detection.rate = 1,
  verbose = FALSE
)

Arguments

genes

csv file (or URL) containing gene information

cells

csv file (or URL) containing cell information

new.matrix

Matrix market file of new counts

total.matrix

Matrix market file of total counts

detection.rate

the detection rate of T-to-C mismatch reads (see details)

verbose

verbose output

Value

a grandR object

Details

Metabolic labeling - nucleotide conversion RNA-seq data (such as generated by SLAM-seq,TimeLapse-seq or TUC-seq) must be carefully analyzed to remove bias due to incomplete labeling. We advice against counting read with and without T-to-C mismatches for quantification, and encourage using a statistical method such as GRAND-SLAM that properly deals with incomplete labeling.

To correct for some bias, a detection rate (as suggested by Cao et al., Nature Biotech 2020) should be provided. This detection rate defines, how much new RNA is detected on average using the T-to-C mismatch reads.