Recent studies revealed total transcript numbers per cell vary by cell type and the condition. Therefore, many statistical tests for differential expression are not necessarily applicable, since it assumed that many genes are not differentially expressed. One solution is addition of a same amount of spike-in control RNA molecules into each sample, for example [Islam2011], and this SAMstrt package provides the significance analysis of sequencing data with spike-in normalization. The statistical backgrounds and the benefits depend on SAMseq of the samr package [Li2011].
Development of this package was supported by the Karolinska Institutet Distinguished Professor Award to Juha Kere, and the Strategic Research Area Grant for Diabetes to Karolinska Institutet. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under Project b2010037.
- Islam S, et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 2011 Jun 1.
- Katayama,S. et al. SAMstrt: Statistical test for differential expression in single-cell transcriptome with spike-in normalization. Submitted.
- Li,J. and Tibshirani,R. (2011) Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data. Stat Methods Med Res. Nov. 28, 2011.