Question: Normalisation before log2 transformation or after in Microarray Gene expression data?
gravatar for Saheb
10 months ago by
Saheb0 wrote:

Hi friends.

I have a doubt on the order of steps performed on Microarray Gene expression data / RNASeq data.

1) Whether we should apply normalisation techniques like quantile or lowess to Microarray gene expression and then perform log2 transformation or steps are correct other way round? I have found both types of order in different sources. Which one is correct?

2) And what about the order of steps in RNASeq data?

Thanks in advance.

rna-seq • 1.2k views
ADD COMMENTlink modified 10 months ago • written 10 months ago by Saheb0
gravatar for Kevin Blighe
10 months ago by
Kevin Blighe39k
Republic of Ireland
Kevin Blighe39k wrote:

For microarray, the broadly accepted method of normalisation is known as Robust Multiarray Average (RMA):

  1. background correction
  2. quantile normalisation
  3. probe summarisation (i.e. across transcripts)
  4. log (base 2) transformation

Extra notes:

  • An alternative to this which also adjusts for the GC content and how it affects probe-binding affinities is called GC-RMA.
  • Other types of normalisation (step 2) exist, namely: Qspline; LOESS; VSN (variance stabilising normalisation); et cetera
  • Step 3 is usually a 'median polish'
  • There are intricate differences in each step based on different microarray platforms

Log transformation is not performed prior to normalisation.

For more, read the really great review by Professor Quackenbush: Microarray data normalization and transformation.


Current RNA-seq normalisaton methods / count values differ quite a bit from each other. We have:

  • FPKM
  • RPKM
  • RSEM
  • TPM
  • CPM
  • TMM
  • Median normalisation (DESeq2)

A log transformation is not typically involved in the normalisation process for RNA-seq. Statistical comparisons are performed on the normalised, unlogged counts, which generally do not follow a binomial distribution. RNA-seq count data, in fact, follows a negative binomial distribution, akin to a Poisson. However, one can later log the normalised counts, e.g. for plotting functions, in order to bring them to a binomial distribution. DESeq2, for example, implements a regularised log transformation.

For more, read A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis


ADD COMMENTlink modified 5 months ago • written 10 months ago by Kevin Blighe39k

Dear Kevin, Many many thanks for your detailed response to my query.

ADD REPLYlink written 10 months ago by Saheb0

No problem - best of luck. Please do also read the mentioned publications.

ADD REPLYlink written 10 months ago by Kevin Blighe39k

Thanks... I will surely go through those publications...

ADD REPLYlink written 10 months ago by Saheb0
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