<p>Type of data you need depends on the downstream applications and since you would like to carry out DEA with <code>DESeq2</code>, you would need raw counts (non-normalized).</p>
<p>There are many ways to import/download TCGA data, one such tool, the <code>TCGAbiolinks</code> package gives a nice interface for not only downloading the read count (or pre-processed) data but also associated clinical data.</p>

CC BY-SA 4.0
2020-08-31T15:13:56.727Z
yes, I'm using TCGAbiolinks to download the data... what's the difference between limma and DESeq2? many thanks, @haci
FPS
CC BY-SA 4.0
2020-08-31T15:21:39.770Z
I'm planning to compare my gene of interest in different types of cancer with normal tissue.... How many datasets of each kind of cancer and normal tissue should I include?
FPS
CC BY-SA 4.0
2020-09-04T09:44:31.440Z
`limma` was designed for microarray data, but can be used for RNA-seq by using the `voom` transformation. However, the gold standard these days is DESeq2. As for how many datasets you need... what's your hypothesis? What are you looking for? Do you just want to blindly do tumor vs normal for multiple cancer types and check what comes up?
csgroen
CC BY-SA 4.0
2020-09-04T20:40:32.610Z
Hi @csgroen. yes, I just want to investigate my gene of interest in different types of cancer. Maybe I don't even need an extensive differential expression analysis. can I just compare my gene of interest in cancer cells with normal tissue using t student? it is probably enough to answer my question..
FPS
CC BY-SA 4.0
2020-09-04T20:48:16.697Z
If it is just for gene of interest, you can maybe do it using XenaBrowser... easier than downloading all the data using TCGAbiolinks then doing differential analysis.
csgroen
CC BY-SA 4.0
2020-09-04T21:43:11.537Z
@csgroen in XenaBrowser the number of datasets is small, in the TCGA I can access dozens or even hundreds of datasets of the same cancer type...
FPS
CC BY-SA 4.0
2020-09-04T21:55:17.997Z
I'm not sure what you mean. TCGA was a consortium that produced around 30 cohorts of various cancer types. But for each cancer type, there is only one TCGA cohort. All of them are available in XenaBrowser. Now, in GEO, you'll have hundreds of cohorts of other sources, but they are not 'in the TCGA'
csgroen
CC BY-SA 4.0
2020-09-05T09:26:27.833Z
@csgroen... no, look at the GDC website (https://portal.gdc.cancer.gov). The TCGA counts with 33 cancer types, but 585.428 datasets are available.. 86.582 datasets are RNA-Seqs..
FPS
CC BY-SA 4.0
2020-09-05T10:21:02.350Z
FYI, it's 86k samples, not datasets. And all the data from the GDC is available in other platforms, like XenaBrowser. But you do as you think is best ;)
csgroen
CC BY-SA 4.0
2020-08-31T15:07:01.587Z
2020-08-31T15:07:01.587Z
haci
<p>You should use &quot;counts&quot; from the list of gene expression metrics that you provided rather than either FPKM or FPKM-UQ. FPKM is a normalized gene expression metric and an acronym for fragments per kilobase per million mapped reads. FPKM normalizes for both sequencing depth and genome size. Those specific packages want non-normalized counts as the expression metric. This means the number of reads mapped to a particular gene or feature. This is a slightly ambiguous metric but it can be the number of single-end reads mapped to a loci, the amount of pair end reads counted towards a loci, or the amount of super-reads (the theoretical sequence that spans the pair of paired-end reads which includes the presumed sequence between the first and second strand of a set of pair end reads) counted toward a loci. These values should be integers based on the assumptions of RNA-Seq and microarray technology which measures RNA transcripts present in a sample and precludes transcripts from spanning multiple genes. It does not matter which of these metrics you use as long as you are consistent. Those Bioconductor packages provide several different normalization methods but rely on read counts as their input.</p>

CC BY-SA 4.0
2020-09-01T16:19:30.750Z
2020-09-01T16:19:30.750Z
Andrew Judell
<p>I'm trying to download data from the TCGA for gene expression analyses in R, but I'm in doubt if I should use FPKM, FPKM-UQ or counts? When the dataset is in counts, I suppose it's raw data, isn't it? So what's the best unit to compare multiple datasets?</p>
<p>I'm planning to use limma or Dseq2 for GE analysis and found that with Dseq2 I need to use count(non-normalized???) data... is that correct? so what's the best package and working strategy?</p>
<p>thank you very much, Fabiano</p>

<r><gene-expression>
FPKM, FPKM-UQ, TPM or counts: How do I know which kind of unit should I use?
# FPKM, FPKM-UQ, TPM or counts: How do I know which kind of unit should I use?

(Asked by: username_0 on 2020-08-31)

I'm trying to download data from the TCGA for gene expression analyses in R, but I'm in doubt if I should use FPKM, FPKM-UQ or counts? When the dataset is in counts, I suppose it's raw data, isn't it? So what's the best unit to compare multiple datasets?

I'm planning to use limma or Dseq2 for GE analysis and found that with Dseq2 I need to use count(non-normalized???) data... is that correct? so what's the best package and working strategy?

thank you very much, Fabiano

**Tags:** r, gene-expression

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## 2 answers

### Answer by username_1 on 2020-08-31. Score: 3

Type of data you need depends on the downstream applications and since you would like to carry out DEA with `DESeq2`, you would need raw counts (non-normalized).

There are many ways to import/download TCGA data, one such tool, the `TCGAbiolinks` package gives a nice interface for not only downloading the read count (or pre-processed) data but also associated clinical data.

> **Comments:**
> **username_0**: yes, I'm using TCGAbiolinks to download the data... what's the difference between limma and DESeq2? many thanks, @haci  
> **username_0**: I'm planning to compare my gene of interest in different types of cancer with normal tissue.... How many datasets of each kind of cancer and normal tissue should I include?  
> **username_2**: `limma` was designed for microarray data, but can be used for RNA-seq by using the `voom` transformation. However, the gold standard these days is DESeq2. As for how many datasets you need... what's your hypothesis? What are you looking for? Do you just want to blindly do tumor vs normal for multiple cancer types and check what comes up?  
> **username_0**: Hi @csgroen. yes, I just want to investigate my gene of interest in different types of cancer. Maybe I don't even need an extensive differential expression analysis. can I just compare my gene of interest in cancer cells with normal tissue using t student? it is probably enough to answer my question..  
> **username_2**: If it is just for gene of interest, you can maybe do it using XenaBrowser... easier than downloading all the data using TCGAbiolinks then doing differential analysis.  
> **username_0**: @csgroen in XenaBrowser the number of datasets is small, in the TCGA I can access dozens or even hundreds of datasets of the same cancer type...  
> **username_2**: I'm not sure what you mean. TCGA was a consortium that produced around 30 cohorts of various cancer types. But for each cancer type, there is only one TCGA cohort. All of them are available in XenaBrowser. Now, in GEO, you'll have hundreds of cohorts of other sources, but they are not 'in the TCGA'  
> **username_0**: @csgroen... no, look at the GDC website (https://portal.gdc.cancer.gov). The TCGA counts with 33 cancer types, but 585.428 datasets are available.. 86.582 datasets are RNA-Seqs..  
> **username_2**: FYI, it's 86k samples, not datasets. And all the data from the GDC is available in other platforms, like XenaBrowser. But you do as you think is best ;)  

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### Answer by username_3 on 2020-09-01. Score: 1

You should use "counts" from the list of gene expression metrics that you provided rather than either FPKM or FPKM-UQ. FPKM is a normalized gene expression metric and an acronym for fragments per kilobase per million mapped reads. FPKM normalizes for both sequencing depth and genome size. Those specific packages want non-normalized counts as the expression metric. This means the number of reads mapped to a particular gene or feature. This is a slightly ambiguous metric but it can be the number of single-end reads mapped to a loci, the amount of pair end reads counted towards a loci, or the amount of super-reads (the theoretical sequence that spans the pair of paired-end reads which includes the presumed sequence between the first and second strand of a set of pair end reads) counted toward a loci. These values should be integers based on the assumptions of RNA-Seq and microarray technology which measures RNA transcripts present in a sample and precludes transcripts from spanning multiple genes. It does not matter which of these metrics you use as long as you are consistent. Those Bioconductor packages provide several different normalization methods but rely on read counts as their input.


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