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I have 4 RNA-seq samples (1st is control and the other 3 are treatments) and 5 spikes. I used different concentrations for 5 spikes but over different samples, I used similar concentration. for example I used similar concentration for spike1 in 4 samples. and this is the case for the rest of spikes. I am trying to normalize the read counts for the spike. do you guys know how to do that?

this is what I have done so far: I counted the spikes in different samples and and got the ration of them (treatments compared to the control for all spikes). the ratio is as expected (looking at the concentration of spikes). one thing that I can do to correct the read counts for spikes is to multiply by the ratio between samples. but I am not sure if that is the right way.

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The most common and simplest normalization method for spikes is the Total Count Normalization. It's actually what you're doing here, so what you're doing here is correct. TCM is a useful algorithm for normalizing sequencing depth, that includes your spike amounts.

https://academic.oup.com/bib/article-lookup/doi/10.1093/bib/bbs046

has reference on the TCM normalization.

Gene counts are divided by the total number of mapped reads (or library size) associated with their lane and multiplied by the mean total count across all the samples of the dataset.

The paper doesn't talk about spike-in controls, but the idea is the same. We'd use the spike-in controls for estimating the difference in sequence depth (instead of the whole data set).

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