BRGenomics 1.10.0
DESeq2’s default treatment of data relies on the assumption that genewise
estimates of dispersion are largely unchanged across samples. While this
assumption holds for a typical RNA-seq data, it can be violated if there are
samples within the DESeqDataSet object for which there are meaningful signal
changes across a majority of regions of interest.
The BRGenomics functions getDESeqDataSet() and getDESeqResults() are simple
and flexible wrappers for making pairwise comparisons between individual
samples, without relying on the assumption of globally-similar dispersion
estimates. In particular, getDESeqResults() follows the logic that the
presence of a dataset \(X\) within the DESeqDataSet object will not affect the
comparison of datasets \(Y\) and \(Z\).
While the intuition above is appealing, users should note that if the
globally-similar dispersions assumption does hold, then DESeq2’s default
behavior should be more sensitive in its estimates of genewise dispersion. In
this case, users can still take advantage of the convenience of the BRGenomics
function getDESeqDataSet(), but they should subsequently call
DESeq2::DESeq() and DESeq2::results() directly.
If the globally-similar dispersions assumption is violated, but something beyond simple pairwise comparisons is desired (e.g. group comparisons or additional model terms), we note that, with some prying, DESeq2 can be used without “blind dispersion estimation” (see the DESeq2 manual).
Just like the functions that generate batch-normalized spike-in normalization
factors, the DESeq-oriented functions require that the names of the input
datasets end in "rep1", "rep2", etc.
Let’s first make a toy list of multiple datasets to compare:
library(BRGenomics)
data("PROseq")ps_list <- lapply(1:6, function(i) PROseq[seq(i, length(PROseq), 6)])
names(ps_list) <- c("A_rep1", "A_rep2",
"B_rep1", "B_rep2",
"C_rep1", "C_rep2")ps_list[1:2]## $A_rep1
## GRanges object with 7897 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr4 1295 + | 1
## [2] chr4 42595 + | 1
## [3] chr4 42622 + | 2
## [4] chr4 42718 + | 1
## [5] chr4 42789 + | 1
## ... ... ... ... . ...
## [7893] chr4 1295277 - | 1
## [7894] chr4 1306500 - | 1
## [7895] chr4 1306889 - | 1
## [7896] chr4 1307122 - | 1
## [7897] chr4 1316537 - | 1
## -------
## seqinfo: 7 sequences from an unspecified genome
##
## $A_rep2
## GRanges object with 7897 ranges and 1 metadata column:
## seqnames ranges strand | score
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr4 41428 + | 1
## [2] chr4 42596 + | 1
## [3] chr4 42652 + | 3
## [4] chr4 42733 + | 1
## [5] chr4 42794 + | 2
## ... ... ... ... . ...
## [7893] chr4 1305972 - | 1
## [7894] chr4 1306514 - | 1
## [7895] chr4 1307032 - | 1
## [7896] chr4 1307126 - | 1
## [7897] chr4 1318960 - | 1
## -------
## seqinfo: 7 sequences from an unspecified genomenames(ps_list)## [1] "A_rep1" "A_rep2" "B_rep1" "B_rep2" "C_rep1" "C_rep2"As you can see, the names all end in “repX”, where X indicates the replicate.
Replicates will be grouped by anything that follows “rep”. If the sample names
do not conform to this standard, the sample_names argument can be used to
rename the samples within the call to getDESeqDataSet().
data("txs_dm6_chr4")dds <- getDESeqDataSet(ps_list, txs_dm6_chr4,
gene_names = txs_dm6_chr4$gene_id,
ncores = 1)
dds## class: DESeqDataSet
## dim: 111 6
## metadata(1): version
## assays(1): counts
## rownames(111): FBgn0267363 FBgn0266617 ... FBgn0039924 FBgn0027101
## rowData names(2): tx_name gene_id
## colnames(6): A_rep1 A_rep2 ... C_rep1 C_rep2
## colData names(2): condition replicateNotice that the dim attribute of the DESeqDataSet object is c(111, 6).
There are 6 samples, but length(txs_dm6_chr4) is not 111. This is because we
provided gene names to getDESeqDataSet(), which were non-unique. The feature
being exploited here is for use with discontinuous gene regions, not for
multiple overlapping transcript isoforms.
By default, getDESeqDataSet() will combine counts across all ranges
belonging to a gene, but if they overlap, they will be counted twice. For
addressing issues related to overlaps, see the reduceByGene() and
intersectByGene() functions.
We could have added normalization factors, which DESeq2 calls “size factors”, in
the call to getDESeqDataSet(), or we can supply them to getDESeqResults()
below. (Supplying them twice will overwrite the previous size factors).
Important note on normalization factors: DESeq2 “size factors” are the
inverse of BRGenomics normalization factors. So if you calculate normalization
factors with NF <- getSpikeInNFs(...), set sizeFactors <- 1/NF.
Generating DESeq2 results is simple:
getDESeqResults(dds, contrast.numer = "B", contrast.denom = "A",
quiet = TRUE, ncores = 1)## log2 fold change (MLE): condition B vs A
## Wald test p-value: condition B vs A
## DataFrame with 111 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0267363 0.252443 -1.4201507 4.997269 -0.2841853 0.776268 0.990374
## FBgn0266617 9.648855 -0.4357749 0.983164 -0.4432374 0.657594 0.990374
## FBgn0265633 2.291440 0.3467829 1.882149 0.1842484 0.853819 0.990374
## FBgn0264617 67.131764 0.0245325 0.451334 0.0543556 0.956652 0.990374
## FBgn0085432 4688.232636 -0.0469812 0.270431 -0.1737268 0.862080 0.990374
## ... ... ... ... ... ... ...
## FBgn0039928 653.783 -0.00994936 0.262637 -0.0378825 0.969781 0.990374
## FBgn0052017 114.906 0.02350299 0.369082 0.0636796 0.949225 0.990374
## FBgn0002521 168.778 0.05777885 0.347249 0.1663903 0.867850 0.990374
## FBgn0039924 170.213 -0.20850730 0.409865 -0.5087219 0.610947 0.990374
## FBgn0027101 343.009 -0.21151750 0.290061 -0.7292177 0.465868 0.990374We can also make multiple pairwise-comparisons by ignoring the contrast.numer
and contrast.denom arguments, and instead using the comparisons argument.
The resulting list of results is named according to the comparisons:
comparison_list <- list(c("B", "A"),
c("C", "A"),
c("C", "B"))
dsres <- getDESeqResults(dds, comparisons = comparison_list,
quiet = TRUE, ncores = 1)
names(dsres)## [1] "B_vs_A" "C_vs_A" "C_vs_B"dsres$C_vs_B## log2 fold change (MLE): condition C vs B
## Wald test p-value: condition C vs B
## DataFrame with 111 rows and 6 columns
## baseMean log2FoldChange lfcSE stat pvalue padj
## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
## FBgn0267363 0.00000 NA NA NA NA NA
## FBgn0266617 9.38822 0.3938871 0.901764 0.436796 0.662259 0.998692
## FBgn0265633 2.28968 -0.3206785 1.718554 -0.186598 0.851976 0.998692
## FBgn0264617 65.30695 -0.0773192 0.415908 -0.185905 0.852520 0.998692
## FBgn0085432 4281.29059 -0.1902162 0.229704 -0.828094 0.407617 0.998692
## ... ... ... ... ... ... ...
## FBgn0039928 696.482 0.2150507 0.248408 0.8657159 0.386646 0.998692
## FBgn0052017 133.743 0.4160583 0.333948 1.2458762 0.212810 0.998692
## FBgn0002521 171.141 0.0159143 0.323995 0.0491188 0.960825 0.998692
## FBgn0039924 138.749 -0.3685073 0.327200 -1.1262461 0.260061 0.998692
## FBgn0027101 347.104 0.2699312 0.271542 0.9940675 0.320190 0.998692