diffExpressRankOrder | R Documentation |
Orders the results of a differential expression calculation, as a combination of the reported fold change and P-value for each gene.
diffExpressRankOrder(folds, pvalues, wt.folds=1, wt.pvalues=1,
notDE.value=0, two.sided.P.values=TRUE)
diffExpressDistanceRankOrder(folds, pvalues, dists, wt.folds=1, wt.pvalues=1,
wt.dists=1, notDE.value=0, two.sided.P.values=TRUE)
diffExpressRankRankOrder(folds, pvalues, ranks, wt.folds=1, wt.pvalues=1,
wt.ranks=1, notDE.value=0, two.sided.P.values=TRUE)
folds |
numeric vector of fold change values. Must be expresses as log2 ratio values (no expression change equals 0) |
pvalues |
numeric vector of same length as |
dists |
numeric vector of same length as |
wt.folds |
scalar weight for the contribution of fold change rankings to the result |
wt.pvalues |
scalar weight for the contribution of P-value rankings to the result |
wt.dists |
scalar weight for the contribution of distance rankings to the result |
notDE.value |
scalar value of |
two.sided.P.values |
logical specifying whether the P-values came from a 2-sided or 1-sided test |
Fold change is a way of describing the difference between two expression
values as a ratio x1/x2
. For very low magnitudes, the fold
change can be artifically high. For very high magnitudes, the P-value can
be artifically small (especially in NextGen sequencing datasets). This
function calcualates the rank order by each criteria separately, and
then uses a weighted average of those to produce the resulting gene rank order.
A vector of integer indices the same length as folds
, giving the
permutation order of the input from highest differentially expressed
(most up-regulated) to lowest (most down-regulated).
In a usual 2-sided test, both the strongly up-regulated and strongly
down-regulated genes will have good P-values. The function assumes this
about the given P-values. If a 1-sided test that gives poor P-values for
the down-regulated genes was used, set two.sided.P.value=FALSE
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