Dataset Information

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.se

SUBMITTER: Katayama S

PROVIDER: S-EPMC3810855 | biostudies-other | 2013 Nov

REPOSITORIES: biostudies-other

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

Katayama Shintaro S Töhönen Virpi V Linnarsson Sten S Kere Juha J

Bioinformatics (Oxford, England) 20130831 22

<h4>Motivation</h4>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.<h4>Availability and implem ...[more]

PMID: 23995393

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Project description:UnlabelledBackgroundBecause of the large volume of data and the intrinsic variation of data intensity observed in microarray experiments, different statistical methods have been used to systematically extract biological information and to quantify the associated uncertainty. The simplest method to identify differentially expressed genes is to evaluate the ratio of average intensities in two different conditions and consider all genes that differ by more than an arbitrary cut-off value to be differentially expressed. This filtering approach is not a statistical test and there is no associated value that can indicate the level of confidence in the designation of genes as differentially expressed or not differentially expressed. At the same time the fold change by itself provide valuable information and it is important to find unambiguous ways of using this information in expression data treatment.ResultsA new method of finding differentially expressed genes, called distributional fold change (DFC) test is introduced. The method is based on an analysis of the intensity distribution of all microarray probe sets mapped to a three dimensional feature space composed of average expression level, average difference of gene expression and total variance. The proposed method allows one to rank each feature based on the signal-to-noise ratio and to ascertain for each feature the confidence level and power for being differentially expressed. The performance of the new method was evaluated using the total and partial area under receiver operating curves and tested on 11 data sets from Gene Omnibus Database with independently verified differentially expressed genes and compared with the t-test and shrinkage t-test. Overall the DFC test performed the best - on average it had higher sensitivity and partial AUC and its elevation was most prominent in the low range of differentially expressed features, typical for formalin-fixed paraffin-embedded sample sets.ConclusionsThe distributional fold change test is an effective method for finding and ranking differentially expressed probesets on microarrays. The application of this test is advantageous to data sets using formalin-fixed paraffin-embedded samples or other systems where degradation effects diminish the applicability of correlation adjusted methods to the whole feature set.

Dataset Information

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

Publications

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

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