NaN values after ComBat analysis on TCGA COAD RNAseq I have FPKM-UQ data from COAD-TCGA.

I generated an expression set of this data using:

&gt; edata = log2(data + 1)
&gt; edata[1:2,1:2]
      X01240896.3f3f.4bf9.9799.55c87bfacf36
1                                  8.540967
10                                13.528968
100                               16.296422
1000                              13.658199
10000                             15.143788
      X01ad5016.f691.4bca.82a0.910429d8d25b
1                                  9.886537
10                                16.719682
100                               17.212312
1000                              10.317842
10000                             13.166767


Row name is entrezid, and column names are case_ids.

The batch data provided by TCGA which indicates a total batch num = 42

&gt; omics[1:5,]
                                      snf    boo order batch
X01240896.3f3f.4bf9.9799.55c87bfacf36   3 192807     1    22
X01ad5016.f691.4bca.82a0.910429d8d25b   2  82213     2     2
X01f493d4.229d.47a6.baa8.32a342c65d01   1 A08907     3    29
X022f39e9.57ee.4b2b.8b3a.8929e3d69a37   2 A16S13     4    32
X02f9668c.71e6.485f.88b1.b37dc8bdd2ab   3 102113     5     7
&gt; batch = omics$batch
&gt; batch
  [1] 22  2 29 32  7  1  8  8  7 41 26 14 26 26  1  5 22  5 15 18 28 12 12  8 19
 [26] 13 38  5 27  7 41  1 22 26 28 22 33 14 13  8 10 13 11 17  5 38  5  7  7 31
 [51] 22 42 11  8  7 17 15 17 12 17  2 19  2 12 18 14 22 11 21 15 17 17 33 15 19
 [76] 15  6  1 10 11  2 28 36 17  9 15 12 17 15 10  6  8 13 25 11 15  1 13 24  8
[101] 13 15 17  8  2 22 17 11 13 37 19 38 13 19  5 16  5  5 22 35 35 19 13 17 22
[126]  7  8  8 41 39 14  6 19 18 38 22 14 30 19 24 11 14 13 16  7 13  8 22 12  8
[151] 22 17  2 40 13 35 15  5 24 19 41 22 22 19 17  8 13 33 10 17 29 12  9 11 24
[176] 27  5 14  7 25 13 35  9 18 16 13 31 25 18 10 35 18 14 33  5 19 17  8 11 11
[201] 42  7 10  8 39 19  8  7 11 19 31 35 28 36 13 37 10 38  3 18 13 34 39 13  3
[226] 40 31 17 19 26  7 19 12 22 19 17 12 17 13 12 28 33 37 17 35 18 40 13 20 35
[251] 17  6 15 14 12 27  5 41 31 41 18 21 19 17 40  8 41  2  5 15  5 15 15  5 39
[276]  1 22 33 13 17  7 11 15  3  7 38 15  7 14 14 22 31 14 18  3 19  7  9  6  5
[301] 22  8 11  1 13 10 19  8 14 15  6 32  1  2 19  4  5 13 18 42 11 17  8 36 19
[326] 15 22 28 15  8 11  8 15 38 19 39  7 11 42 23  5  1 22 17 35 13 40 25  7 41
[351] 38  5  8 19 31  6 38  8 36  4 15 15  7 12 22 38 29 35 15  2 10  2 19 15 25
[376]  5 39 30 19 18 15 10 10 33  1 19 32 19 19 15 20 15  7 22 35 22 30 14 12 16
[401] 21 13 15 18  7  5  6 31 12  7 15 32 33 18 14 15 15 26 31  1 14 19 17 18  1
[426]  5 10 32


Unfortunately, when I run the ComBat analysis it generates all NANs, and no values:

&gt; modcombat = model.matrix(~1, data=omics)
&gt; combat_edata = ComBat(dat=edata, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE)
&gt; combat_edata[1:5,1:2]
      X01240896.3f3f.4bf9.9799.55c87bfacf36
1                                       NaN
10                                      NaN
100                                     NaN
1000                                    NaN
10000                                   NaN
      X01ad5016.f691.4bca.82a0.910429d8d25b
1                                       NaN
10                                      NaN
100                                     NaN
1000                                    NaN
10000                                   NaN
I think I might know what the problem is. 

I plotted the log2 corrected values, and the distribution may not be normal because of the 0 values present in RNASeq data. If I understand correctly, this data is actually missing, whereas with my current analysis it is viewed by R as an actual value of 0. So either the missing data needs to be imputed, or the data needs to be scaled and normalized to mean of 0, then subsequently log2 transformed.

&gt; data &lt;- standardNormalization(data)
&gt; data &lt;- log2(data + 1)
&gt; combat_data = ComBat(dat=data, batch=batch, mod=modcombat, par.prior=TRUE, prior.plots=FALSE)
&gt; combat_data[1:5,1:2]
          X01240896.3f3f.4bf9.9799.55c87bfacf36
100507661                           -0.13857428
57103                               -0.06700506
22838                                0.16295096
55567                               -0.13579991
6147                                 1.64990772
          X01ad5016.f691.4bca.82a0.910429d8d25b
100507661                           -0.13789534
57103                               -0.06210232
22838                                0.01546224
55567                               -0.13443289
6147                                 1.48473072