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  • #1

    Control-based correction in multispecies RNAseq comparison

    Hi all!

    I think this issue fits better here than on another forum I posted this question some time ago:

    In a multispecies gene expression comparison (per species one control group and 3 treatments) I want to compare the gene expression patterns of specific gene families. For this I want to correct the expression levels of these genes per species using the control group. In this way you get the percentage difference in expression of the genes for each treatment in comparison to the control treatment, and I can compare the gene expression levels of the different species directly instead of only looking at within species patterns and comparing both.

    I have not seen a similar approach in other multi-species RNAseq studies. Are there perhaps similar studies or papers dealing with a multispecies comparison of RNAseq data that I have overlooked?

    Like to hear your opinion!
    Tags: comparison, expression levels, multispecies, rnaseq
  • #2
    Sounds logical, but not sure. This article might be of interest...

    A statistical normalization method and differential expression analysis for RNA-seq data between different species - BMC Bioinformatics
    https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2745-1
    Background High-throughput techniques bring novel tools and also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, normalization serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and by using the hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named “SCBN”, which is freely available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html .

    Comment

    • #3
      Ah! Thanks for the suggestion, I will give it a try.

      Comment

      • #4
        thank you will try it
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        Comment

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