Tweet
I tried the option to emit count tables in cuffdiff. I expected the counts to be integers, but I get fractions. Are these normalized counts?
-
-
Yes, these are the result of "common scale transformation" of counts. See the DESeq paper for how these are computed - we do the same thing to transform all counts across the replicates for a condition to a common scale for that replicate before fitting the dispersion model. -
about --emit-count-tables
Hi Cole and PFS,
I have some questions about --emit-count-tables option, which is said that
"Cuffdiff will output a file for each condition (called <sample>_counts.txt)".
I run Cuffdiff with 4 samples' bam file together, but I got only locus_var.txt and pooled_counts.txt.
Q1, what does pooled_counts.txt indicate?
Q2, in locus_var.txt, the number of condition column indicates each samples in the same order as those in cuffdiff command line?
*Here is my command,
cuffdiff -p 2 -u --emit-count-tables ./../compare/cuffcmp.combined.gtf ./../Nipponbare/tophat_out/accepted_hits.bam ./../Kasalath/tophat_out/accepted_hits.bam ./../Nip_Kas/tophat_out/accepted_hits.bam ./../Kas_Nip/tophat_out/accepted_hits.bam
*the content of locus_var.txt
0 TCONS_00005163 1348.57 15275.1 132648 50019.6 30 3 0 0 0.219325 0 0 0 0 -1.0565
0 TCONS_00005164 1348.57 15275.1 132648 50019.6 30 3 0 0 0.0778509 0 0 0 0 0.0389116
1 TCONS_00000001 1348.57 15275.1 132648 96366.6 30 3 0 0 0.751663 0 0 0 0 0.676941
1 TCONS_00005163 1348.57 15275.1 132648 96366.6 30 3 0 0 0.00888725 0 0 0 0 -5.68775
1 TCONS_00005164 1348.57 15275.1 132648 96366.6 30 3 0 0 0.240978 0 0 0 0 1.74781
2 TCONS_00000001 1348.57 15275.1 132648 94122.3 30 3 0 0 0.699411 0 0 0 0 0.572296
2 TCONS_00005163 1348.57 15275.1 132648 94122.3 30 3 0 0 0.223565 0 0 0 0 -1.03559
2 TCONS_00005164 1348.57 15275.1 132648 94122.3 30 3 0 0 0.0784949 0 0 0 0 0.138381
0 TCONS_00005166 55.532 1915.14 986.219 19.6345 30 4 0 0 0 0 0 0 0 -inf
0 TCONS_00006797 55.532 1915.14 986.219 19.6345 30 4 0 0 0.996369 0 0 0 0 2.04356
0 TCONS_00006796 55.532 1915.14 986.219 19.6345 30 4 0 0 0.503341 0 0 0 0 1.0567
0 TCONS_00005165 55.532 1915.14 986.219 19.6345 30 4 0 0 0.00028983 0 0 0 0 -9.7241
3 TCONS_00000001 1348.57 15275.1 132648 158408 30 3 0 0 0.676484 0 0 0 0 0.521408
The each numbers of condition column are corresponding to...
0 -> Nipponbare
1 -> Kasalath
2 -> Nip_Kas
3 -> Kas_Nip
correct?
And one more question, after grouping this file into each sample_txt by condition number and sorting by description, I got 2 same records like this..
0 TCONS_00000001 1348.57 15275.1 132648 50019.6 30 3 0 0 0.703948 0 0 0 0 0.588801
0 TCONS_00000001 1348.57 15275.1 132648 50019.6 30 3 0 0 0.703948 0 0 0 0 0.588801
0 TCONS_00000002 3202.34 180770 597601 89875.9 30 8 0 0 0.00282914 0 0 0 0 -4.4797
0 TCONS_00000002 3202.34 180770 597601 89875.9 30 8 0 0 0.00282914 0 0 0 0 -4.4797
0 TCONS_00000005 885.268 8618.77 68982.9 14506.8 30 3 0 0 0.440866 0 0 0 0 0.181828
0 TCONS_00000005 885.268 8618.77 68982.9 14506.8 30 3 0 0 0.440866 0 0 0 0 0.181828
Why are there same records??
If possible, please describe the details about each columns of locus_var.txt?
I really appreciate any your helps,
zunComment
-
Cole, Thanks for the clarification. By any chance, are the raw counts available in the intermediate temp files generated by cuffdiff ? Thanks.Originally posted by Cole Trapnell View PostComment
-
Love this - good data definitely starts from good input, and poor input can only give relatively poor data. I particularly like the mention of Nanodrop/absorbance based methods for quantification. It's such a toss up if you'll get an accurate reading or what amounts to a randomly generated number, and a lot of library/sequencing related issues can be traced back to poor quant.07-01-2026, 11:43 AM
-
I’m not a sequencing expert. I’m a purification scientist who uses NGS to evaluate workflows my group develops. With this perspective, we think about the sample first and the NGS workflow second. The sequencer is an exceptionally honest reporter, but it can only report on what you give it, so whether you get clean, interpretable data from an NGS workflow is largely determined before you begin.
Here are nine questions we think about, in roughly the order they matter, before...
Comment