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I am trying to normalize my RNAseq data to take into consideration transfection efficiency. I have two conditions that were applied to cells in culture, control and treated. Control cells were transfected with a vector just containing eGFP. Treated cells were transfected with a vector containing both eGFP and the experimental manipulation gene. There are 3 biological replicate control samples and 3 biological replicate treated samples.
The cells are not from a model organism, so I have run trinity to generate the transcriptome. I have successfully run RSEM and have the read counts information. I have also annotated the features using Trinotate and a modified Trinotate swissprot database so that I correctly annotate eGFP (the default only contains GFP). Now I would like to run a differential expression analysis with edgeR that normalizes the samples based on eGFP read counts.
The manual addresses this concept in passing without fully fleshing it out, so I'm not certain how to proceed. It appears that the glm functions should be used and that my normalization should use calcNormFactors. I'm not sure exactly how to apply this to eGFP.
The cells are not from a model organism, so I have run trinity to generate the transcriptome. I have successfully run RSEM and have the read counts information. I have also annotated the features using Trinotate and a modified Trinotate swissprot database so that I correctly annotate eGFP (the default only contains GFP). Now I would like to run a differential expression analysis with edgeR that normalizes the samples based on eGFP read counts.
The manual addresses this concept in passing without fully fleshing it out, so I'm not certain how to proceed. It appears that the glm functions should be used and that my normalization should use calcNormFactors. I'm not sure exactly how to apply this to eGFP.