We recently ran a paired-end RNA-seq experiment and we have the quantile normalized data on for 7 different samples. I want to measure differential expression of genes between samples however I do not know how to pursue.
I've been advised that the proper method to compare gene X expression for sample 1 and 2 is the following formula:
log2((X1+median1)/(X2+median2))
Is this correct? If so, can anyone explain why...or better yet, why not pursue the more sophisticated statistical models of bayseq, edgeR, etc...?
I would recommend using the raw counts per gene and look into DeSeq2 or edgeR to do the testing. These are packages used by a large number of people and give robust results.
The formula as it is written above does mot make much sense to me.
To my knowledge, "quantile normalization" works differently: if you have a matrix of genes (in rows) and samples (in columns), then each column is ordered, and the values in the rows are substituted by the row-means. Then the values in the columns are brought back to their original position. As a result, the empirical distribution of the values in all columns are identical (and, thus, all moments like mean, variance, kurtosis, ...).
This normalization works very well for whole transcriptome data from comparable biological sources (it might not be suited when the global profiles can be drastically different, like for single-cell analyses or the comparisons of different cell types).
- Badges
- Science topic
- Similar topics
- Mathematics
- Statistics
- Statistical Modeling