Description Usage Arguments Value See Also Examples

Calculates either a robust version (default) or the traditional matrix of fragments/counts per million mapped fragments (FPM/CPM). Note: this function is written very simply and can be easily altered to produce other behavior by examining the source code.

1 |

`object` |
a |

`robust` |
whether to use size factors to normalize rather than taking the column sums of the raw counts. If TRUE, the size factors and the geometric mean of column sums are multiplied to create a robust library size estimate. Robust normalization is not used if average transcript lengths are present. |

a matrix which is normalized per million of mapped fragments, either using the robust median ratio method (robust=TRUE, default) or using raw counts (robust=FALSE).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
# generate a dataset with size factors: .5, 1, 1, 2
dds <- makeExampleDESeqDataSet(m = 4, n = 1000,
interceptMean=log2(1e3),
interceptSD=0,
sizeFactors=c(.5,1,1,2),
dispMeanRel=function(x) .01)
# add a few rows with very high count
counts(dds)[4:10,] <- 2e5L
# in this robust version, the counts are comparable across samples
round(head(fpm(dds), 3))
# in this column sum version, the counts are still skewed:
# sample1 < sample2 & 3 < sample 4
round(head(fpm(dds, robust=FALSE), 3))
# the column sums of the robust version
# are not equal to 1e6, but the
# column sums of the non-robust version
# are equal to 1e6 by definition
colSums(fpm(dds))/1e6
colSums(fpm(dds, robust=FALSE))/1e6
``` |

DESeq2 documentation built on Feb. 22, 2021, 10 a.m.

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