Hi guys,

I am working with RNA-seq data generated by Illumina GAII. The experiment design is a randomized complete block design (RCBD) with 4 different tissues, 2 treatments (control and treated), and 3 blocks (reps). My interest is to use the mixed ANOVA in ProcMixed (SAS) to determine differentially expressed genes. Initially, I thought I can normalize read counts in RPKM and use that to do the Mixed ANOVA in SAS or JMP Genomics. Reading through the literature I realized that RPKM is not the best normalization tool for RNA-seq. I am leaning toward DESeq or edgeR for normalization.

My questions are:

1) How do I extract normalized counts from DESeq and EdgeR?

2) Has anybody tried to analyze differential gene expression in normalized data using ProcMixed in SAS? Is there anything I should be cautious in using mixed effect model for RNA-seq? I don't think I can use DESeq and edgeR for a mixed model.

I appreciate your input.

Thanks.

T. Abebe

I am working with RNA-seq data generated by Illumina GAII. The experiment design is a randomized complete block design (RCBD) with 4 different tissues, 2 treatments (control and treated), and 3 blocks (reps). My interest is to use the mixed ANOVA in ProcMixed (SAS) to determine differentially expressed genes. Initially, I thought I can normalize read counts in RPKM and use that to do the Mixed ANOVA in SAS or JMP Genomics. Reading through the literature I realized that RPKM is not the best normalization tool for RNA-seq. I am leaning toward DESeq or edgeR for normalization.

My questions are:

1) How do I extract normalized counts from DESeq and EdgeR?

2) Has anybody tried to analyze differential gene expression in normalized data using ProcMixed in SAS? Is there anything I should be cautious in using mixed effect model for RNA-seq? I don't think I can use DESeq and edgeR for a mixed model.

I appreciate your input.

Thanks.

T. Abebe

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