Hello,

I am analyzing a dataset of single-cell RT-qPCR of sorted human CD8+ T lymphocytes from 4 groups of individuals. My goals are to identify CD8+ T cell signatures commonly shared by the different settings and signatures discriminating the groups.

I used fluidigm (Standard BioTools) technology to capture single cells (C1 autoprep) then perform RT-qPCR (Biomark). For analysis, I converted ct values, normalized gene expression to the geometric mean of the expression of the two housekeeping genes from assays. I have some contamination by CD4+ T cells. I have set up a strategy to identify and extract them by ct values, so that I have likely heterogeneous but "true" CD8+ T cells only for clustering and differential expression analysis

I have looked for tools to perform batch correction and subsequent data analysis (e.g. clustering, differential expression etc) I was wondering whether I may use Seurat to do the analysis of my dataset (multiple runs of single-cell RT-qPCR) and if so whether the main functions/steps of Seurat analytic pipeline may be applied (or what should I change). Should/may I apply Log normalization to gene expression normalized by geo mean of housekeeping gene)?

Thank you in advance for reading and your advices

Best regards

Francois