Design matrix in limma
I have quantile normalized counts, with negative values: The dataset is:
GeneID 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
1/2-SBSRNA4 1.84545405543259 0.6665398175808 1.59873554207786 1.89298926465623 1.26568208427265 1.88410700890907 1.74378606410793 1.48987360722618
A1BG 1.6165355686345 2.68681326811308 1.50663367983524 2.25377918290859 2.67375515222443 2.37256130394363 2.98553798816952 2.7872063927308 2.46972065233526
A1BG-AS1 0.0318256718292522 1.06223446343374 0.365739932809055 0.160454525989031 -0.189562119063132 0.451464743899586 1.00348737045778 0.521080706070493
A2LD1 2.42805396344275 3.29182280645954 3.42740052556388 3.54573585005455 2.6718279086933 2.84892844820465 3.23788385068264 2.5512302536277 3.00143277095887
A2M 8.69286445805741 9.01321493190288 9.06178519781115 9.37509171855292 9.15365897313587 10.225835039655 9.58498357891138 9.26308027293732 10.10798379
This data set has zeros. I have no idea about the comparisons between samples. I am new in limma
Kindly help me
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There are many tutorials available online for such analysis, I think it is important to have a look at them before asking such question : https://www.bioconductor.org/packages/devel/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf If you have specific question, we would be likely to help, but all the information you need is in the tutorial
Dear Basti,
Thank you for your response. I think , my question is pretty clear about limma and normalized count matrix. In my knowledge, EdgeR never use pre-normalized count matrix, it is prefered to use with raw count matrix as deseq2 (https://support.bioconductor.org/p/65890/#65891)