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

Shintaro Katayama1, Virpi Töhönen, Sten Linnarsson

  • 1Department of Biosciences and Nutrition, Karolinska Institutet, 141 83 Huddinge, Sweden, Science for Life Laboratory, Karolinska Institutet Science Park, 171 21 Solna, Sweden and Department of Medical Biochemistry and Biophysics, Karolinska Institutet, 171 77 Stockholm, Sweden.

Bioinformatics (Oxford, England)
|September 3, 2013
PubMed
Summary
This summary is machine-generated.

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New statistical methods are needed for single-cell RNA sequencing (scRNA-seq) due to varying transcript counts. SAMstrt offers spike-in normalization and absolute transcript number estimation for more accurate differential expression analysis in scRNA-seq.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Transcriptome studies show cell-type and condition-specific variations in total transcript numbers.
  • Existing statistical assumptions for single-cell RNA sequencing (scRNA-seq) require reevaluation.
  • Accurate normalization and quantification are crucial for reliable scRNA-seq data analysis.

Purpose of the Study:

  • To introduce SAMstrt, an extension of the SAMseq statistical method.
  • To enable spike-in normalization for scRNA-seq data.
  • To facilitate statistical testing based on estimated absolute transcript counts per cell.

Main Methods:

  • SAMstrt is implemented as an R package.
  • The method extends SAMseq for differential expression analysis.

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  • It utilizes spike-in controls for normalization and absolute quantification.

    • Main Results:

    • SAMstrt provides a robust framework for normalizing scRNA-seq data using spike-ins.
    • Enables estimation of absolute transcript numbers per cell.
    • Facilitates more accurate differential expression testing.

    Conclusions:

    • SAMstrt addresses the need for revisited statistical assumptions in scRNA-seq.
    • The tool enhances the reliability of differential expression analysis in single-cell studies.
    • Spike-in normalization and absolute quantification are key for robust scRNA-seq interpretation.