Dear R people,
I downloaded the normalized RNA seq data of Pancreatic adenocarcinoma with the help of TCGA assember. The data looks like this
GeneSymbol EntrezID TCGA-2J-AAB1-01A-11R-A41B-07 TCGA-2J-AAB4-01A-12R-A41B-07 TCGA-2J-AAB6-01A-11R-A41B-07 TCGA-2J-AAB8-01A-12R-A41B-07 TCGA-2J-AAB9-01A-11R-A41B-07 TCGA-2J-AABA-01A-21R-A41B-07
A1BG 1 81.9122 56.7551 82.5497 56.9307 105.7878 99.3455
A1CF 29974 25.3659 53.4512 8.1871 33.8425 21.4362 18.7882
RBFOX1 54715 0.4878 2.1044 0 0 1.0718 0
GGACT 87769 180.4976 111.0774 163.1228 185.8143 166.7095 99.2767
A2ML1 144568 85.8537 0 1815.7895 16.9213 642.015 873.6496
A2M 2 19703.8049 15837.8241 8517.4444 14413.913 24311.7792 10302.0072
A4GALT 53947 1541.4634 1154.8822 1121.0526 392.9495 1125.4019 633.1611
So my question is,
Thanks in advance.
I downloaded the normalized RNA seq data of Pancreatic adenocarcinoma with the help of TCGA assember.
Anyway, To me it seems normalised data. Typical FPKM/RPKM/TPM values looks like this (in the range of thousand). Also, your values are in decimals suggesting they are not raw read counts but normalised by some methods.
Cufflink, DEseq, edgeR, limma, ballgown are some of the well known package form RNA-seq normalisation.
Also, your values are in decimals suggesting they are not raw read counts but normalised by some methods.
Could be estimated counts, like from, e.g., RSEM, Kallisto, or Salmon.
Can you please quote the source of that? Also, what exactly are you aiming to do with the data? There are likely easier ways than using TCGA Assembler.
Well, this: 'percentile method for normalizing the data' is not a typical method for normalising data. Can you please plot a histogram of your data and add it as a figure (in R, use hist()). Please go HERE to upload a screenshot and obtain a link for the figure.
You do not need to worry about the large values - that is normal in RNA-seq.
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I am attaching the figure for the data.
Very difficult for us to know by just looking at a tiny snapshot of your data. You can most likely answer your own question by reading the TCGA Assembler manual / quick start guide, and / or simply looking at the options / parameters for the function that you use to retrieve this gene expression data.
Ultimately, you should have exhaustively tried to answer your own question before coming here.
Please use the formatting bar (especially the
codeoption) to present your post better. I've done it for you this time.Thank you!