# Normalization methods for single cell RNA sequencing that take read count into account

I have two RNA-seq datasets. One was sequenced at an average read count of 1.5 million per cell the other at 43K average reads per cell. For the first I also have the meta data from reads alligned and unaliagnable for each cell, but I may have to do some digging for the other dataset. I am looking for the presence of a few very informative genes being expressed at similar levels in the second dataset to confirmed examples of these cells in the first. The first dataset is much smaller.

My question is this: Are there normalization methods which I could use given the data I have to allow me to accurately say these cells are in the 90th or 80th percentile of a a particular gene being expressed?

• More reads/UMIs will lead to more counts in the matrixes, so you can normalize for sequencing depth without using the fastq/bams directly. Indeed this is common with metrics like CPM. However, what are the single cell technologies used in this case? Feb 13 at 19:18

If that's not good enough, DESeq2 allows you to provide alternative information (e.g. cell counts) to bypass the usual normalisation process using the sizeFactors function: