Hi! I have to perform a differential expression analysis for a RIP-seq experiment. In my design I have two variables treatment (miR and control) and Immunoprecipitation (Ago, IgG and TL). The sample table looks likes as follow:
samples treatment IP condition
sample1 miR Ago Ago.miR
sample2 miR Ago Ago.miR
sample3 miR Ago Ago.miR
sample4 miR IgG IgG.miR
sample5 miR IgG IgG.miR
sample6 miR IgG IgG.miR
sample7 miR TL TL.miR
sample8 miR TL TL.miR
sample9 miR TL TL.miR
sample10 Control Ago Ago.Control
sample11 Control Ago Ago.Control
sample12 Control Ago Ago.Control
sample13 Control IgG IgG.Control
sample14 Control IgG IgG.Control
sample15 Control IgG IgG.Control
sample16 Control TL TL.Control
sample17 Control TL TL.Control
sample18 Control TL TL.Control
I want to create a two-factorial design considering the variables treatment (miR and control) and Immunoprecipitation. In paticular I would to compare:
Ago miR vs Ago Control
adjusting for the IgG factor
. The comparisons should looks like as follow: (Ago.miR – IgG.miR) vs (Ago. control– IgG. control)
.
I was wondering if the right way to get this result is to create the dds model and extract the result as follow:
enter code here
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = samples, directory = directory, design= ~ treatment * IP)
res <- results(dds, contrast=list(c("treatment_miR_vs_Control", "IP_Ago_vs_IgG")))
Thank you,
Concetta
Thank you for your reply. Unfortunately I did not understand very well how to extract the result. In my experiment I have RNA-seq experiments corresponding to TL and RIP-seq experiments corresponding to IgG and Ago. I would like to identify microRNAs targets comparing Ago vs Control. However in my comparisons I want to correct first for the aspecific binding to the beads considering the IgG factor and then I want to to correct for the transcriptome changes considering the TL factor. I have read this older post about RIP-seq (https://support.bioconductor.org/p/61509/) and I changed my command into:
I was wondering how I can extract the result of Ago miR vs Ago Control corrected for IgG and the results of Ago miR vs Ago Control corrected for TL. Thank you for your help.
results()
can pull out the coefficents byname
.When you have interaction terms (the 5th and 6th term above), these represent ratios of ratios as explained in the vignette diagram.
I might recommend you work with a statistician to help interpret the linear model terms.
Also see the vignette on how to set factor levels. Here you have
miR
as the reference level, instead ofControl
.