Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/geneset-DGEList.R
Test whether a set of genes is highly ranked relative to other genes in terms of differential expression, accounting for inter-gene correlation.
1 2 3 |
y |
a |
index |
an index vector or a list of index vectors. Can be any vector such that |
design |
design matrix. Defaults to |
contrast |
contrast of the linear model coefficients for which the test is required. Can be an integer specifying a column of |
weights |
numeric matrix of observation weights of same size as |
use.ranks |
do a rank-based test ( |
allow.neg.cor |
should reduced variance inflation factors be allowed for negative correlations? |
inter.gene.cor |
numeric, optional preset value for the inter-gene correlation within tested sets. If |
sort |
logical, should the results be sorted by p-value? |
... |
other arguments are not currently used |
The camera gene set test was proposed by Wu and Smyth (2012) for microarray data.
This function makes the camera test available for digital gene expression data.
The negative binomial count data is converted to approximate normal deviates by computing mid-p quantile residuals (Dunn and Smyth, 1996; Routledge, 1994) under the null hypothesis that the contrast is zero.
See camera
for more description of the test and for a complete list of possible arguments.
A data.frame giving the gene set results, with most significant sets to the top.
See camera
for details.
Yunshun Chen, Gordon Smyth
Dunn, PK, and Smyth, GK (1996). Randomized quantile residuals. J. Comput. Graph. Statist., 5, 236-244. http://www.statsci.org/smyth/pubs/residual.html
Routledge, RD (1994). Practicing safe statistics with the mid-p. Canadian Journal of Statistics 22, 103-110.
Wu, D, and Smyth, GK (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research 40, e133. https://doi.org/10.1093/nar/gks461
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | mu <- matrix(10, 100, 4)
group <- factor(c(0,0,1,1))
design <- model.matrix(~group)
# First set of 10 genes that are genuinely differentially expressed
iset1 <- 1:10
mu[iset1,3:4] <- mu[iset1,3:4]+10
# Second set of 10 genes are not DE
iset2 <- 11:20
# Generate counts and create a DGEList object
y <- matrix(rnbinom(100*4, mu=mu, size=10),100,4)
y <- DGEList(counts=y, group=group)
# Estimate dispersions
y <- estimateDisp(y, design)
camera(y, iset1, design)
camera(y, iset2, design)
camera(y, list(set1=iset1,set2=iset2), design)
|
Loading required package: limma
NGenes Direction PValue
set1 10 Up 2.432363e-05
NGenes Direction PValue
set1 10 Down 0.6678383
NGenes Direction PValue FDR
set1 10 Up 2.432363e-05 4.864726e-05
set2 10 Down 6.678383e-01 6.678383e-01
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