pwilmart / irs_normalization

An exploration of internal reference scaling (IRS) normalization in isobaric tagging proteomics experiments.

Proteomics education, tools, and applications

Check out Start_Here for a roadmap to the available content. I have blogs here on some proteomics topics from the simple to the "I can't believe someone wrote a blog post on that". The OHSU proteomics core also has a GitHub account with some additional content.

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Phil Wilmarth
July 12, 2020

irs_normalization's Issues

Applying SL, TMM, and IRS on log2 data or raw data

Hey,
I have a question regarding the three normalization methods that you applied. Actually it makes a difference whether to apply for example SL on raw data and log2 the data for visualization, or to apply SL on already log2 transformed data. The same results for TMM and IRS.

What is best practice for this? And why would you (as in your markdown) use these methods on raw data?

Best,
Lis

IRS normalisation without imputation

Hi Phillip,

I wonder if IRS normalisation is still valid on data with NAs (without imputation)?
In that case, to get the geometric average intensity for each protein, the below modifications will be required as following:

# make new data frame with row sums from each frame
irs <- tibble(rowSums(exp1_sl,na.rm = TRUE), rowSums(exp2_sl,na.rm = TRUE), rowSums(exp3_sl,na.rm = TRUE))
# get the geometric average intensity for each protein
irs$average <- apply(irs, 1, function(x) exp(mean(log(x),na.rm = TRUE)))

Another point I'd like to mention is when a routine IRS normalisation is applied post imputation (unlike the above case), would it be still appropriate to implement ComBat normalisation on top of IRS-normalised data?

Thanks for your advice.

Overfitting data?

In your statistical_testing analysis, following IRS normalization, the result of the decide tests function looks you might be overfitting your data.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4679072/
https://www.biostars.org/p/145083/