Dear all,

This is a newbie question :)

I'm building a linear model to identify significant predictors of mutation count/types in tumours from TCGA. I want to include expression levels of a couple of genes, but I am quite new to RNA-Seq analyses and best practices. TCGA provides RNA-Seq data at the gene level in three formats: HTSeq-counts, FPKM and FPKM-UQ. I have been reading (tutorials and the questions here) and asking around and I have reached the conclusion that I can use FPKM-UQ values to compare across samples without any further pre-processing - Is this true? Or would you recommend doing pre-processing to these values before comparing?

Thanks so much,
Daniela
Hi Daniela,

I would highly recommend the HTSeq counts, actually, because these will be raw counts. The normalisation method that produces FPKM expression levels has come under criticism in recent years and is now not even recommended by some sources. The main issue with this [FPKM] method is that cross-sample normalisation is non-existent, as such, it's akin to comparing multiple batches without even doing any correction for batch.

Use HTSeq counts and load these into DESeq2 or EdgeR for downstream analyses.

I have recently analysed an entire TCGA RNAseq dataset (>500 samples) and I used HTSeq counts. They work very well.

Kevin

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**Update May 2, 2018:**

The TCGA states that "*To facilitate cross-sample comparison and differential expression analysis, the GDC also provides Upper Quartile normalized FPKM (UQ-FPKM) values and raw mapping count.*" - https://gdc.cancer.gov/about-data/data-harmonization-and-generation/genomic-data-harmonization/high-level-data-generation/rna-seq-quantification

My original advice still stands, i.e., better to obtain the raw HT-seq counts (where available), and re-process those using an updated normalisation method, like TMM (EdgeR) or geometric (DESeq2). Some TCGA datasets are only available in RSEM counts, which are also possible to use and input to DESeq2 using *tximport*