catchKallisto results are not counts
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Assa Yeroslaviz ★ 1.5k
@assa-yeroslaviz-1597
Last seen 5 weeks ago
Germany

I have run Kallisto on my samples to try and get transcript-level intensities. I now want to do a transcript-level expression analysis using the catchKallisto command from the edgeR package.

This went quite well.

library(edgeR)
paths <- list.files(path = "kallisto/output/", pattern="Sample", full.names=TRUE)
data <- catchKallisto(paths = paths, verbose = TRUE )

But the resulted list$counts are not counts (Are these TPMs???).

             kallisto/output//L75442_Track-118107
Y110A7A.10.1                         1.439992e+03
Y110A7A.10.2                         8.353787e-03
F27C8.1.1                            1.200000e+01
F27C8.1.2                            0.000000e+00
F07C3.7                              5.300000e+01
             kallisto/output//L75443_Track-118108
Y110A7A.10.1                          1575.499243
Y110A7A.10.2                             1.500757
F27C8.1.1                                0.000000
F27C8.1.2                               11.000000
F07C3.7                                 52.000000
             kallisto/output//L75444_Track-118109
Y110A7A.10.1                         1.546000e+03
Y110A7A.10.2                         1.515167e-04
F27C8.1.1                            5.000000e+00
F27C8.1.2                            0.000000e+00
F07C3.7                              7.000000e+01

Do I need to change them into counts (just by rounding them) or is it ok for edgeR to have them as such?

thanks

Assa

DifferentialSplicingWorkflow catchKallisto edgeR DifferentialSplicing • 195 views
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@gordon-smyth
Last seen 2 hours ago
WEHI, Melbourne, Australia

They are estimated counts from kallisto's EM algorithm. Columns sum to the total number of mapped reads for that sample.

edgeR accepts fractional counts, using a continuous extension of the negative binomial distribution: https://stats.stackexchange.com/questions/310676/continuous-generalization-of-the-negative-binomial-distribution/

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thanks for the fast response

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