SAMstrt: statistical test for differential expression in single-cell transcriptome with spike-in normalization

Abstract

Motivation: Recent transcriptome studies have revealed that total transcript numbers vary by cell type and condition; therefore, the statistical assumptions for single-cell transcriptome studies must be revisited. SAMstrt is an extension code for SAMseq, which is a statistical method for differential expression, to enable spike-in normalization and statistical testing based on the estimated absolute number of transcripts per cell for single-cell RNA-seq methods.Availability and Implementation: SAMstrt is implemented on R and available in github (https://github.com/shka/R-SAMstrt).Contact: shintaro.katayama@ki.seSupplementary Information: Supplementary data are available at Bioinformatics online.

A statistical approach for identifying differential distributions in single-cell RNA-seq experiments

“…3 c, which indicates the presence of a distinct cell type in one condition, but not the other. Recent methods for scRNA-seq [17, 18, 27, 28, 46] may be able to identify genes such as those shown in Fig. 3 b–d as differing between conditions.…”

“…When the overall mean expression level for a given gene is shifted across conditions, then bulk methods, or recent methods for scRNA-seq [17, 18, 27, 28], may be able to identify the gene as showing some change. However, as we show here, they would be relatively underpowered to do so, and they would be unable to characterize the change, which is often of interest in an scRNA-seq experiment.…”

The human PRD-like homeobox gene LEUTX has a central role in embryo genome activation

“…The differential expression between samples was quantified using SAMstrt, which is a statistical test utilizing spike-in normalization for differential expression sequenced in transcriptomes by the STRT method (Katayama et al, 2013; Töhönen et al, 2015). The method is an extension of SAMseq (Li and Tibshirani, 2013), a non-parametric approach for identifying differential expression in RNA-seq data.…”

Human Wound-Healing Research: Issues and Perspectives for Studies Using Wide-Scale Analytic Platforms

“…One solution is the addition of exogenous control RNAs into the target RNAs just before reverse transcription, and normalization according to the controls. 29,[45][46][47] …”