Question: EdgeR GLMfit issue - only high and negative LFC, please help
0
gravatar for lech.kaczmarczyk
18 months ago by
lech.kaczmarczyk30 wrote:

Hi All, I tried to use EdgeR GLM fit function on my data, but I only get some clearly incorrect results. I spent hours trying to spot my error. Limma voom lmFit worked fine on the same data. Here is the script. I figured that something is wrong with how the comparison are set, but don't know how and what to fix. You are my last hope.

> y_TU_TagGLM <- DGEList(txi_TPMs$counts[,3:8], samples = SampleTable[3:8,1:3], genes = rownames(txi_TPMs))
> colnames(y_TU_TagGLM) <- SampleTable[3:8,1]
> designGLM <- model.matrix(~0 + ~Type, y_TU_TagGLM$samples)
> colnames(designGLM) <- gsub("Type","", colnames(designGLM))
> names(y_TU_TagGLM)
[1] "counts"  "samples"
> designGLM
                   Affinity_Purified_vGlut2 Unbound
vGluT2_TU1                                1       0
vGluT2_TU2                                1       0
Unbound_Gad2_TU1                          0       1
Unbound_Gad2_TU2                          0       1
Unbound_vGluT2_TU1                        0       1
Unbound_vGluT2_TU2                        0       1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$Type
[1] "contr.treatment"

> y_TU_TagGLM <- calcNormFactors(y_TU_TagGLM)
> y_TU_TagGLM <- estimateDisp(y_TU_TagGLM, designGLM)
> 
> fitTU <- glmFit(y_TU_TagGLM, designGLM)
> lrtTU <- glmLRT(fitTU, coef=2)
> ttTU <- topTags(lrtTU, n=nrow(y_TU_TagGLM), p.value=0.1, adjust.method="BH", sort.by="PValue")
> tt10 <- as.data.frame(topTags(lrtTU, n=nrow(y_TU_TagGLM)))
> tt10
                        logFC        logCPM         LR        PValue           FDR
ENSMUSG00000100131 -25.788249  7.6141957137 3246.33983  0.000000e+00  0.000000e+00
ENSMUSG00000020140 -17.153947  3.5624865899 1522.37536  0.000000e+00  0.000000e+00
ENSMUSG00000008333 -16.922668  3.9244238753 1526.39683  0.000000e+00  0.000000e+00
ENSMUSG00000051920 -16.903855  3.8515603204 1679.09047  0.000000e+00  0.000000e+00
ENSMUSG00000025940 -16.880893  3.9362217669 1504.04685  0.000000e+00  0.000000e+00
ENSMUSG00000061559 -16.780391  3.7090760077 1660.99975  0.000000e+00  0.000000e+00
ENSMUSG00000026027 -16.769318  4.0481712621 1557.80775  0.000000e+00  0.000000e+00
ENSMUSG00000043999 -16.732672  3.5288708577 1584.29262  0.000000e+00  0.000000e+00
ENSMUSG00000005802 -16.727090  3.8779942891 1560.42342  0.000000e+00  0.000000e+00


> lrtTU
An object of class "DGELRT"
$coefficients
                   Affinity_Purified_vGlut2   Unbound
ENSMUSG00000000001                -11.35335 -11.54869
ENSMUSG00000000003                -18.50960 -18.50960
ENSMUSG00000000028                -13.05009 -13.57928
ENSMUSG00000000037                -14.73369 -14.83902
ENSMUSG00000000049                -15.92172 -16.37713
34834 more rows ...

$fitted.values
                   vGluT2_TU1 vGluT2_TU2 Unbound_Gad2_TU1 Unbound_Gad2_TU2 Unbound_vGluT2_TU1 Unbound_vGluT2_TU2
ENSMUSG00000000001 340.280203 312.299640      126.5293480       59.4623969         27.2048770         40.2990287
ENSMUSG00000000003   0.000000   0.000000        0.0000000        0.0000000          0.0000000          0.0000000
ENSMUSG00000000028  62.149383  57.038963       16.5101174        7.7589205          3.5498145          5.2583982
ENSMUSG00000000037  11.325269  10.394015        4.5840940        2.1542924          0.9856189          1.4600133
ENSMUSG00000000049   3.267397   2.998726        0.8712465        0.4094418          0.1873253          0.2774881
34834 more rows ...

$deviance
ENSMUSG00000000001 ENSMUSG00000000003 ENSMUSG00000000028 ENSMUSG00000000037 ENSMUSG00000000049 
          3.094813           0.000000           4.449726           6.071378           4.895752 
34834 more elements ...

$method
[1] "oneway"

$unshrunk.coefficients
                   Affinity_Purified_vGlut2       Unbound
ENSMUSG00000000001            -1.135413e+01 -1.154966e+01
ENSMUSG00000000003            -1.000000e+08 -1.000000e+08
ENSMUSG00000000028            -1.305436e+01 -1.358616e+01
ENSMUSG00000000037            -1.475686e+01 -1.486754e+01
ENSMUSG00000000049            -1.599990e+01 -1.652796e+01
34834 more rows ...

$df.residual
[1] 4 4 4 4 4
34834 more elements ...

$design
                   Affinity_Purified_vGlut2 Unbound
vGluT2_TU1                                1       0
vGluT2_TU2                                1       0
Unbound_Gad2_TU1                          0       1
Unbound_Gad2_TU2                          0       1
Unbound_vGluT2_TU1                        0       1
Unbound_vGluT2_TU2                        0       1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$Type
[1] "contr.treatment"


$offset
     [,1]     [,2]     [,3]   [,4]     [,5]     [,6]
x 17.1839 17.09809 16.39013 15.635 14.85305 15.24598
attr(,"class")
[1] "compressedMatrix"
attr(,"repeat.row")
[1] TRUE
attr(,"repeat.col")
[1] FALSE

$dispersion
[1] 0.08905732 1.05908953 0.35835305 1.31137167 0.80502574
34834 more elements ...

$prior.count
[1] 0.125

$samples
                                group lib.size norm.factors               Name                     Type  Mouse
vGluT2_TU1                 vGluT2_TU1 27893527    1.0408040         vGluT2_TU1 Affinity_Purified_vGlut2 vGluT2
vGluT2_TU2                 vGluT2_TU2 24908681    1.0696863         vGluT2_TU2 Affinity_Purified_vGlut2 vGluT2
Unbound_Gad2_TU1     Unbound_Gad2_TU1 13566535    0.9675523   Unbound_Gad2_TU1                  Unbound   Gad2
Unbound_Gad2_TU2     Unbound_Gad2_TU2  6416577    0.9613711   Unbound_Gad2_TU2                  Unbound   Gad2
Unbound_vGluT2_TU1 Unbound_vGluT2_TU1  2907967    0.9705309 Unbound_vGluT2_TU1                  Unbound vGluT2
Unbound_vGluT2_TU2 Unbound_vGluT2_TU2  4201912    0.9949466 Unbound_vGluT2_TU2                  Unbound vGluT2

$prior.df
[1] 5.966951

$AveLogCPM
[1]  3.4006651 -2.7721353  0.8862272 -1.0586601 -2.0704172
34834 more elements ...

$table
                       logFC     logCPM       LR        PValue
ENSMUSG00000000001 -16.66124  3.4006651 938.6024 3.983953e-206
ENSMUSG00000000003 -26.70370 -2.7721353   0.0000  1.000000e+00
ENSMUSG00000000028 -19.59076  0.8862272 286.6896  2.617393e-64
ENSMUSG00000000037 -21.40817 -1.0586601 135.8326  2.170791e-31
ENSMUSG00000000049 -23.62721 -2.0704172 275.9815  5.640562e-62
34834 more rows ...

$comparison
[1] "Unbound"

$df.test
[1] 1 1 1 1 1
34834 more elements ...
rna-seq • 607 views
ADD COMMENTlink modified 18 months ago • written 18 months ago by lech.kaczmarczyk30
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