Description Usage Arguments Details Value See Also Examples

The following function returns fragment counts normalized
per kilobase of feature length per million mapped fragments
(by default using a robust estimate of the library size,
as in `estimateSizeFactors`

).

1 |

`object` |
a |

`robust` |
whether to use size factors to normalize
rather than taking the column sums of the raw counts,
using the |

Note: the kilobase length of the features is calculated from the `rowData`

if a column `basepairs`

is not present in `mcols(dds)`

.
This is the number of basepairs in the union of all `GRanges`

assigned to a given row of `object`

, e.g.,
the union of all basepairs of exons of a given gene.
When the read/fragment counting is interfeature dependent, a strict
normalization would not incorporate the basepairs of a feature which
overlap another feature. This interfeature dependence is not taken into
consideration in the internal union basepair calculation.

a matrix which is normalized per kilobase of the
union of basepairs in the `GRangesList`

or `GRanges`

of the mcols(object), and per million of mapped fragments,
either using the robust median ratio method (robust=TRUE, default)
or using raw counts (robust=FALSE).
Defining a column `mcols(object)$basepairs`

takes
precedence over internal calculation of the kilobases for each row.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
# create a matrix with 1 million counts for the
# 2nd and 3rd column, the 1st and 4th have
# half and double the counts, respectively.
m <- matrix(1e6 * rep(c(.125, .25, .25, .5), each=4),
ncol=4, dimnames=list(1:4,1:4))
mode(m) <- "integer"
se <- SummarizedExperiment(m, colData=DataFrame(sample=1:4))
dds <- DESeqDataSet(se, ~ 1)
# create 4 GRanges with lengths: 1, 1, 2, 2.5 Kb
gr1 <- GRanges("chr1",IRanges(1,1000))
gr2 <- GRanges("chr1",IRanges(c(1,501),c(500,1000)))
gr3 <- GRanges("chr1",IRanges(c(1,1001),c(1000,2000)))
gr4 <- GRanges("chr1",IRanges(c(1,1001,2001),c(500,3000,3000)))
rowData(dds) <- GRangesList(gr1,gr2,gr3,gr4)
# the raw counts
counts(dds)
# the FPKM values
fpkm(dds)
# held constant per 1 million fragments
counts(dds) <- counts(dds) * 2L
round(fpkm(dds))
``` |

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