User: noorpratap.singh
noorpratap.singh • 110
- Reputation:
- 110
- Status:
- Trusted
- Location:
- India
- Website:
- https://github.com/NPSDC
- Last seen:
- 20 hours ago
- Joined:
- 2 years, 1 month ago
- Email:
- n***************@research.iiit.ac.in
I am currently doing my MS at IIIT-H where I am enrolled in CND program(Btech in Computer Science and MS by Research in Computational Natural Sciences. My interests lie in interplay of Computer Science and Biology.
Posts by noorpratap.singh
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Answer:
A: GDC portal barcode / metadata
... If you are familiar with R, then things are really easy. There is a package called [TCGABiolinks][1]. For your case examples 3 and 4 are useful. Make sure to use legacy = T, since I am not sure whether the subtypes exist for the updated data.
If you are uncomfortable with R then you have to downloa ...
written 27 days ago by
noorpratap.singh • 110
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... Hi,
You can extract that information from the following link:
http://firebrowse.org/?cohort=HNSC&download_dialog=true ...
written 4 months ago by
noorpratap.singh • 110
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... Hi,
Have a go through this link
http://satijalab.org/seurat/ ...
written 4 months ago by
noorpratap.singh • 110
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... Hi David,
For RNA-Seq data use DESeq2. This is the most common package used for analysing RNASeq data and carrying differential analysis with a very helpful documentation and large community to help.
For Methylation you can use minfi.
Hope it helps. ...
written 5 months ago by
noorpratap.singh • 110
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Answer:
A: Gene expression study
... Directly comparing datasets from two studies might impact interpretation because of individual dataset biases. Usually for these there exist packages that enable meta analyses i.e combining data from platforms. [Mtea Seq][1] is one such package. I would urge to you explore on these lines.
[1]: h ...
written 5 months ago by
noorpratap.singh • 110
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... get.int.genes <- function(genes.list)
{
total.genes <- Reduce(union, genes.list)
genes.req <- list()
for(i in c(paste('only ', c(1:5)), paste("common ", c(2:5))))
genes.req[[i]] <- c()
for(gene in total.genes)
{
count = 0
set. ...
written 6 months ago by
noorpratap.singh • 110
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... I can't help with analysing normalised data but you could use TCGABiolinks for downloading raw count data from TCGA.
[1]: https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html ...
written 6 months ago by
noorpratap.singh • 110
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... Could you share the script. It seems it is trying to create a temp directory which you cannot excess or does not exist. Possibly changing that directory where it stores might help. ...
written 6 months ago by
noorpratap.singh • 110
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... Just find the correlation among the genes of interest and if its negative it means they are anti-correlated. Assuming you are using R, you can use the cor function which would return you a g*g matrix where g are the number of genes. Now if there is strong anticorrelation. then you can keep a thresho ...
written 6 months ago by
noorpratap.singh • 110
0
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... Hi Kevin,
I understand your point that family wide variations are not that big but they definitely seem to bigger than case vs control and they seem to cluster more than families as such for patient 20 and and 30. ...
written 6 months ago by
noorpratap.singh • 110
Latest awards to noorpratap.singh
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6 months ago,
created an answer with at least 3 up-votes.
For A: Random forest for gene expression
Scholar
6 months ago,
created an answer that has been accepted.
For A: Random forest for gene expression
Teacher
6 months ago,
created an answer with at least 3 up-votes.
For A: Random forest for gene expression
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11 months ago,
has more than 80 characters in the information field of the user's profile.
Popular Question
12 months ago,
created a question with more than 1,000 views.
For Getting subtypes of cancer from TCGA
Popular Question
19 months ago,
created a question with more than 1,000 views.
For Retrieving data from new data portal of tcga
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