tl;dr: Why data linearization is applied in the identification o differentially expressed genes/proteins?
I am new to data analysis of big data like proteomics. As far as I know, a simple t-test is not enough as there is a high chance of false positives. I've been reading and it seems that Limma is a good package with better statistics to be applied in the identification of differentially expressed proteins (and genes). Most of the papers apply linearization in the process of identifying the genes but I would like to understand why this step is necessary.
An example of a R code that I generally see people using is this one:
design
Dear Luiz Gustavo Nogueira Almeida the core of the limma package is linear model. A very simple explanation about why use model.matrix is that this function allow u to create several type of design matrix. According to the matrix created LmFit will estimates the parameters of interest.
So back to your example
data
Dear Luiz, it is the column names while you extract gene expression info from GEO2R. The interesting one is you can set how many columns do you need by the following code. There are other ways too.
You can go through this link for better understanding, hope it will help you.
https://wiki.bits.vib.be/index.php/Analyse_GEO2R_data_with_R_and_Bioconductor
Dear Luiz Gustavo Nogueira Almeida the core of the limma package is linear model. A very simple explanation about why use model.matrix is that this function allow u to create several type of design matrix. According to the matrix created LmFit will estimates the parameters of interest.
So back to your example
data
Thank you both for your help! Moreno S. Rodrigues this book will be really useful, I appreciate it.
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