how interrogate / download RNA-seq data from prostate TCGA data, Robinson 2015, and Trento/Cornell

[Alfonso Urbanucci]

Hi,

I would like to compare gene expression from primary tumours and metastatic from the three different datasets mentioned above (TCGA data, Robinson 2015, and Trento/Cornell). I know that comparing z-scores and RPKM values per gene is not the way to go. Moreover at the moment the data do not seem accessible on cbioportal.

Can you please indicate the fastest way to interrogate single genes (if there is a publicly available tool)?

Alternatively could you indicate where to download total number of reads per gene (e.g. RSEM/STAR outputs)? I can then normalize and compare the data myself.

Thank you,
Alfonso

[Nikolaus Schultz]

Hi Alfonso,

There is currently no way to merge expression data from different studies in cBioPortal (unless they are from TCGA - in this case, since they are all processed and normalized the same way, we show expression levels of individual genes in a separate tab of cross-cancer queries).

You can download gene-level expression data for each study from our data hub:

https://github.com/cBioPortal/datahub/tree/master/public

Niki.

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[Alfonso Urbanucci]

Thank you Nikolaus,

I have now downloaded the files:

TCGA:
From what I can tell, the “data_RNA_Seq_v2_expression_median.txt” are the RSEM values, described here:
https://wiki.nci.nih.gov/display/TCGA/RNASeq+Version+2
The meta file says:
profile_description: Expression levels for 20532 genes in 550 prad cases (RNA Seq V2 RSEM).
Something to keep in mind:  you can alternately get different version of the TCGA data at these locations:
https://portal.gdc.cancer.gov/ to download the STAR 2-pass HTSeq-counts or FPKM data.
http://gdac.broadinstitute.org/ to download RNAseq-v2 RSEM (I believe this should be similar to what is at cBio Portal)
 
Robinson:
They have two versions of RNA expression based on two different types of libraries.  One is polyA where the library method includes a polyA –selection step and generates a sequencing library from the mRNA.  The other library instead “captures” the coding genes from the total RNA using exonic probes, a different way of removing ribosomal RNA, but might end up with unprocessed/unspliced RNA.
I typically use the polyA library data but you could compare and decide which one you think is better.  They describe this in the extended version of the article (http://www.cell.com/cell/pdfExtended/S0092-8674(15)00548-6)
data_RNA_Seq_expression_median.txt
profile_name: mRNA expression / polyA (RNA Seq RPKM)
data_RNA_Seq_expression_capture.txt
profile_description: mRNA expression from capture (RNA Seq RPKM)
 
Trento/Cornell:
data_expression_median.txt
profile_description: Expression levels.
I believe these are FPKMs, pipeline described here: https://www.nature.com/articles/nm.4045

In your opinion, would I be able to compare these datasets like this, using FPKM data per gene… or should I download the bam files or fastq files somewhere (would be great if you could indicate a place where I could download them in block), and analyse all over again ?

Thank you in advance,

Alfonso

[Pichai Raman]

Hi Alfonso,

My recommendation is if you want to do a formal analysis, if you're combining data from different studies it's best to start from fastq or BAM. This way you can confirm the pipeline is the same (lots of pipelines can report FPKM) and parameters/reference files (HG19, HG38 etc...) are the same. Alternatively, if there are enough samples in each study you can do more of a meta-analysis, especially if you have a few more studies. Hope this helps. 

Cheers,

Pichai

Alfonso

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[Nikolaus Schultz]

Hi Alfonso,

We don’t keep track of that information - but you can go to the original publications and find the dbGAP accession numbers. TCGA data are available through the Genomic Data Commons.

However, even when reprocessing all data uniformly, you will likely still have batch effects related to the data source. See our study here:

https://www.biorxiv.org/content/early/2017/08/04/110734

Niki.

On Feb 16, 2018, at 3:06 PM, Alfonso Urbanucci <alfonsou...@gmail.com> wrote:

Thank you Pichai,

could you please indicate the fastest way to get the fastq/bam files for all these studies then?

Do you have a repository I could get the link to?

Thank you in advance,

Alfonso

[Pichai Raman]

Hi Alfonso,

For most of these data sets you will either need to get access to the study through dbGaP or EGA, depending on where they deposited it. For the TCGA specifically, once you get access through dbGaP for raw data you can query and download from the GDC. 

Cheers,

Pichai

On Fri, Feb 16, 2018 at 3:06 PM, Alfonso Urbanucci <alfonsou...@gmail.com> wrote:

Thank you Pichai,

could you please indicate the fastest way to get the fastq/bam files for all these studies then?

Do you have a repository I could get the link to?

Thank you in advance,

Alfonso

On 16 Feb 2018, at 20:44, Pichai Raman <pichai...@gmail.com> wrote:

[Pichai Raman]

Hi Alfonso,

You will first need a dbGaP/ERA account and you will need to formally apply through dbGaP for that specific study. It can take a little while to get access after applying and your IT director will have to be on board. 

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/GetPdf.cgi?document_name=GeneralAAInstructions.pdf

Cheers,

Pichai

On Fri, Feb 16, 2018 at 3:49 PM, Alfonso Urbanucci <alfonsou...@gmail.com> wrote:

Thank you Pichai and Nikolaus for your help,

so: e.g. once I am here:

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000909.v1.p1&phv=252257&phd=&pha=&pht=5250&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1

how do I manage to get access to this particular study?

thank you,

Alfonso

[Alfonso Urbanucci]

Thank you Pichai,

will do!

have a nice week end,

Alfonso

[Alfonso Urbanucci]

Thank you Pichai,

could you please indicate the fastest way to get the fastq/bam files for all these studies then?

Do you have a repository I could get the link to?

Thank you in advance,

Alfonso

On 16 Feb 2018, at 20:44, Pichai Raman <pichai...@gmail.com> wrote:

[Alfonso Urbanucci]

Thank you Pichai and Nikolaus for your help,

so: e.g. once I am here:

https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000909.v1.p1&phv=252257&phd=&pha=&pht=5250&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1

how do I manage to get access to this particular study?

thank you,

Alfonso