Moderator: Bastien Hervé
Bastien Hervé ♦ 4.5k
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Posts by Bastien Hervé
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... Glad it helps, you can accept it as an answer to your post (the check marker next to the thumb) ...
written 7 months ago by
Bastien Hervé ♦ 4.5k
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... If you want in the same output file to extract lines from file 1 where sequences are not present in file 2 AND lines from file 2 where sequences are not present in file 1 :
awk 'NR==FNR { a[$1]++ } NR!=FNR && a[$1]==1' <(cat sorted_mo17matrix.txt sorted_b73matrix.txt) <(cat sorted ...
written 7 months ago by
Bastien Hervé ♦ 4.5k
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... From your example it seems like you want to extract specific rows not columns, right ? ...
written 7 months ago by
Bastien Hervé ♦ 4.5k
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... Hello https://www.biostars.org/u/57656/,
In your example we do not really see where are the spaces and where are the tabs. I can suggest you to use semi colon to better represent your example.
Also,
> Acc to the above eg oculocerebrorenal syndrome should match LOWE OCULOCEREBRORENAL SYNDROME.
...
written 7 months ago by
Bastien Hervé ♦ 4.5k
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... Looks like this is the modal value of your copy number
https://www.mathsisfun.com/mode.html ...
written 10 months ago by
Bastien Hervé ♦ 4.5k
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... I think you are talking about the GRCh37.p13 not GRCh38.p13 from [this ftp][1]
You will be good using dbSNP GRCh38.p7 for your GRCh38.p12 genome, you will just miss some variations in few patches sequences coming along with minor release (.pX)
[1]: ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/ ...
written 10 months ago by
Bastien Hervé ♦ 4.5k
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... Depends of what you want to show. Usually heatmap are made on the log of the counts
http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#heatmap-of-the-count-matrix
Something like :
pheatmap(log2(counts(dds, normalized=TRUE))...) ...
written 10 months ago by
Bastien Hervé ♦ 4.5k
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... It is really close to what could be asked as a school assignment, is it one ?
The policy here is to help OP on specific steps when they cannot move forward, not to design the complete analysis.
Please, as a start, try to add some code lines where you put your data into a dataframe then sum() your ...
written 11 months ago by
Bastien Hervé ♦ 4.5k
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... DESeq2 (RLE and median normalization using dispersion estimation) and voom (Quantile and mediand normalization using linear regression) normalization methods are slightly differents but DE genes from both methods are very close. It is not what you can call an issue, both methods have to take the who ...
written 11 months ago by
Bastien Hervé ♦ 4.5k
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... You can not do a DE analysis removing parts of reads (aligned on genes) because RNAseq is a relative quantification tool. Meaning that number of reads mapped to gene A is relative to the number of reads on Gene B, C etc...
DESeq2 normalization use all your read set minus reads falling in outlier ge ...
written 11 months ago by
Bastien Hervé ♦ 4.5k
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