Why does DESeq2 convert numeric columns to factor during differential expression analysis? I'm attempting to perform differential expression analysis using DESeq2 on a dataset of ~90 samples and ~20000 transcripts. I have a number of variables I'd like to test against, some of which are qualitative (factors) and some of which are quantitative (numeric). A sample of my data are below:

&gt; head(data.phenotype.test)
# A tibble: 6 x 7
  Novogene_ID BORIS_ID     treatment queenness ovaries   elo sampletime
  &lt;fct&gt;       &lt;fct&gt;        &lt;fct&gt;         &lt;dbl&gt;   &lt;dbl&gt; &lt;dbl&gt; &lt;fct&gt;     
1 W03BLGR     003W-blu-grn Qr12        0.0202    124.  1000  12        
2 W03GR       003W-grn     Qr12        0.00210    66.8  923. 12        
3 W03SL       003W-sil     Qr12        0.969     316.  1077. 12        
4 W04BLOR     004W-blu-ora Qr12        0.00168    66.4  876. 12        
5 W04GRSL     004W-grn-sil Qr12        0.175     210.   931. 12        
6 W04YLWH     004W-yel-whi Qr12        0.984     302.  1192. 12        


Now, let's say I want to test which transcripts are differentially expressed together with a numeric variable such as 'ovaries': 

dds.transcript.ovaries = DESeqDataSetFromMatrix(countData = data.transcript.count.ovaries,
                                        colData = as.matrix(data.phenotype.ovaries),
                                        design = ~ ovaries)


This should work, but DESeq2 does something strange- it converts 'ovaries' to a factor, with the message "some variables in design formula are characters, converting to factors". But there are no character values in the supplied data frame, and certainly not in the column 'ovaries'! This conversion to factors is a big problem for me because, having converted a numeric column to factors, DESeq2 unsurprisingly reports that differential expression analysis cannot be performed because there are an equal number of factor levels to samples.

I have found a temporary workaround: if I reduce the data frame to just the 'ovaries' column, DESeq2 no longer converts the numeric data to factor levels and I'm able to perform differential expression analysis as normal. The problem is that I'd eventually like to perform multivariate analyses, e.g. with a design like:

~ ovaries + elo + treatment


However, doing so isn't going to be possible if DESeq2 keeps converting the numeric columns to factors, which it persistently does unless the numeric columns are supplied alone (which of course they can't be for a multivariate design).

What's going on here? Why does DESeq2 convert the numeric columns to factors, seemingly having misinterpreted those columns as characters?

NB: I have replicated this issue with different data on a different machine and the result is the same, so I don't believe this an issue with my R setup.

Edit: Here's the structure of the df:

&gt; str(data.phenotype.test)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   64 obs. of  7 variables:
 $ Novogene_ID: Factor w/ 64 levels "W03BLGR","W03GR",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ BORIS_ID   : Factor w/ 64 levels "003W-blu-grn",..: 1 2 3 4 5 6 7 8 9 10 ...
 $ treatment  : Factor w/ 4 levels "Qc","Qr12","Qr3",..: 2 2 2 2 2 2 2 2 2 2 ...
 $ queenness  : num  0.02017 0.0021 0.96853 0.00168 0.17529 ...
 $ ovaries    : num  124.1 66.8 316.1 66.4 209.9 ...
 $ elo        : num  1000 923 1077 876 931 ...
 $ sampletime : Factor w/ 3 levels "0","3","12": 3 3 3 3 3 3 3 3 3 3 ...
 - attr(*, "na.action")= 'omit' Named int  1 2 3 4 5 6 7 8 9 10 ...
  ..- attr(*, "names")= chr  "1" "2" "3" "4" ...
Edited:

It's the "as.matrix" that is causing the problem.  When I use that on my sample annotation data, and make dds with that, it turns my numbers into factors