I am using limma to look at a GSE microarray (GSE10918 to be specific). I am trying to figure out which design matrix to use before fitting the linear model and am having a hard time figuring it out. It seems from the phenoData, the GSM's are in this format (so they are already subtracted and log2 transformed):
gene1 - control
gene1 - control
gene1 - control
gene2 - control
gene2 - control
gene2 - control
What I want is to find the effects of gene1 and gene2 separately. I am wondering what the best design matrix is for this.
•
written
by
Dave Bridges
1 answer
You could read the raw data (the GPR files) to obtain separate values for the Cy3 and Cy5 channel.
However the experiment has a common reference design without dye swaps. Even after dye normalization you will not be able to distinguish dye effects from biological differences between renilla and the two genes studied.
Log in to answer this question.
-
Question about anova-like test using Limma
written by Daniel •
-
What to do with Seurat metadata and counts in separate text files?
written by 5b54ea20 •
-
can anybody help me to check if the code is correct to use limma package to find the significantly …
written by sw.arker •
-
How To Understand The Encoding And Trim Their Adapters
written by bmechuangye •
-
Which Of The Genes Are Enriched With Repeat Elements
written by roll
-
Downloading Geo Dataset'S Sample Information, But Not Platform Information
written by Backlin •
-
How To Define Batch And Covariate To Remove Batch Effects With Combat.R
written by fbrundu
-
R: Gene Matrix (.Txt File) To Boxplot And Rle, Nuse Plots
written by michaelsbreen1 •
-
Using R To Perform Statistical Tests On Microarray Data And Cluster The Results
written by Walter Jessen
-
From Microrna Prediction Databases To Microrna / Target Matrices
written by Some •
Would do please explain it in more details? are you going to transform the raw dataset of GSE for R package limma?
As descripted in the raw dataset of GSE10918 GSE10918_series_matrix.txt (ftp://ftp.ncbi.nih.gov/pub/geo/DATA/SeriesMatrix/GSE10918/), I think the value is log2(cy5/cy2).
You can find some background infromation about microarray analysis here (http://soybeangenomics.cropsci.illinois.edu/files/NSF_Web_Microarrayresults.pdf).