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. |

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 25 26 27 28 29 30 31 | ```
# generate a dataset with size factors: .5, 1, 1, 2
dds <- makeExampleDESeqDataSet(m = 4, n = 1e3,
interceptMean=log2(1e3),
interceptSD=0,
sizeFactors=c(.5,1,1,2),
dispMeanRel=function(x) .01)
# examine the column sums
# then add 1 million counts over rows 11:15 for each sample
colSums(counts(dds))/1e6
counts(dds)[11:15,] <- 2e5L
colSums(counts(dds))/1e6
# the robust FPM treats the samples
# relatively equally
head(fpm(dds), 3)
# the non-robust version is thrown
# off by the 5 rows with large counts
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
# the total sum is equal for both methods
sum(fpm(dds))
sum(fpm(dds, robust=FALSE))
``` |

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