gs.tTest: Gene set differential expression test

In gage: Generally Applicable Gene-set Enrichment for Pathway Analysis

Description

These functions test for perturbation of gene sets relative to all genes in the microarray data. They are the testing module for gage and single array analysis workflow.

They use different statistical tests: gs.tTest uses two-sample t-test, gs.zTest uses one-sample z-test, gs,KSTest uses Kolmogorov-Smirnov test.

Usage

gs.tTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)
gs.zTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)
gs.KSTest(exprs, gsets, set.size = c(10, 500), same.dir = TRUE, ...)

Arguments

exprs	an expression matrix or matrix-like data structure, with genes as rows and samples as columns.
gsets	a named list, each element contains a gene set that is a character vector of gene IDs or symbols. For example, type head(kegg.gs). A gene set can also be a "smc" object defined in PGSEA package. Make sure that the same gene ID system is used for both gsets and exprs.
set.size	gene set size (number of genes) range to be considered for enrichment test. Tests for too small or too big gene sets are not robust statistically or informative biologically. Default to be set.size = c(10, 500).
same.dir	whether to test for changes in a gene set toward a single direction (all genes up or down regulated) or changes towards both directions simultaneously. For experimentally derived gene sets, GO term groups, etc, coregulation is commonly the case, hence same.dir = TRUE (default); In KEGG, BioCarta pathways, genes frequently are not coregulated, hence it could be informative to let same.dir = FALSE. Although same.dir = TRUE could also be interesting for pathways.
...	other arguments to be passed into the secondary functions, not used currently.

Details

These functions are the gene set test module for gage and single array analysis workflow. When used in gage function, the function names are optional values for saaTest argument. Check help information for gage for details.

These functions may also used independently without calling gage function.

Value

As the raw results of gene set tests, a list of 5 components is returned:

results	matrix of test statistics, gene sets are rows, samp-ref pairs are columns
p.results	matrix of p-values for up-regulation (greater than) tests, gene sets are rows, samp-ref pairs are columns
ps.results	matrix of p-values for down-regulation (less than) tests, gene sets are rows, samp-ref pairs are columns
mstat	vector of test statistics mean for individual gene sets. Normally, its absoluate value measures the magnitude of gene-set level changes, and its sign indicates direction of the changes. For gs.KSTest, mstat is always positive.
setsizes	vector of effective set size (number of genes) individual gene sets

Author(s)

Weijun Luo <[e-mail]>

References

Luo, W., Friedman, M., Shedden K., Hankenson, K. and Woolf, P GAGE: Generally Applicable Gene Set Enrichment for Pathways Analysis. BMC Bioinformatics 2009, 10:161

See Also

gage the main function for GAGE analysis

Examples

data(gse16873)
cn=colnames(gse16873)
hn=grep('HN',cn, ignore.case =TRUE)
dcis=grep('DCIS',cn, ignore.case =TRUE)
data(kegg.gs)

#kegg test
exprs.gage = gagePrep(gse16873, ref = hn, samp = dcis)
str(exprs.gage)
rawRes = gs.tTest(exprs.gage, gsets = kegg.gs)
str(rawRes)
head(rawRes$results)
head(rawRes$p.results)

Example output

 num [1:11979, 1:6] -0.3076 0.4159 0.1985 -0.2316 -0.0449 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:11979] "10000" "10001" "10002" "10003" ...
  ..$ : chr [1:6] "DCIS_1" "DCIS_2" "DCIS_3" "DCIS_4" ...
List of 5
 $ results   : num [1:177, 1:6] -0.1734 1.9946 -0.4334 0.9677 -0.0245 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:177] "hsa00010 Glycolysis / Gluconeogenesis" "hsa00020 Citrate cycle (TCA cycle)" "hsa00030 Pentose phosphate pathway" "hsa00040 Pentose and glucuronate interconversions" ...
  .. ..$ : chr [1:6] "DCIS_1" "DCIS_2" "DCIS_3" "DCIS_4" ...
 $ p.results : num [1:177, 1:6] 0.5687 0.0257 0.6666 0.1713 0.5097 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:177] "hsa00010 Glycolysis / Gluconeogenesis" "hsa00020 Citrate cycle (TCA cycle)" "hsa00030 Pentose phosphate pathway" "hsa00040 Pentose and glucuronate interconversions" ...
  .. ..$ : chr [1:6] "DCIS_1" "DCIS_2" "DCIS_3" "DCIS_4" ...
 $ ps.results: num [1:177, 1:6] 0.431 0.974 0.333 0.829 0.49 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:177] "hsa00010 Glycolysis / Gluconeogenesis" "hsa00020 Citrate cycle (TCA cycle)" "hsa00030 Pentose phosphate pathway" "hsa00040 Pentose and glucuronate interconversions" ...
  .. ..$ : chr [1:6] "DCIS_1" "DCIS_2" "DCIS_3" "DCIS_4" ...
 $ mstat     : Named num [1:177] 0.459 1.039 0.717 0.48 0.709 ...
  ..- attr(*, "names")= chr [1:177] "hsa00010 Glycolysis / Gluconeogenesis" "hsa00020 Citrate cycle (TCA cycle)" "hsa00030 Pentose phosphate pathway" "hsa00040 Pentose and glucuronate interconversions" ...
 $ setsizes  : Named int [1:177] 56 27 24 15 31 25 10 6 39 8 ...
  ..- attr(*, "names")= chr [1:177] "hsa00010 Glycolysis / Gluconeogenesis" "hsa00020 Citrate cycle (TCA cycle)" "hsa00030 Pentose phosphate pathway" "hsa00040 Pentose and glucuronate interconversions" ...
                                                       DCIS_1     DCIS_2
hsa00010 Glycolysis / Gluconeogenesis             -0.17338110  0.2870236
hsa00020 Citrate cycle (TCA cycle)                 1.99455860  1.3901566
hsa00030 Pentose phosphate pathway                -0.43338672  0.9249188
hsa00040 Pentose and glucuronate interconversions  0.96766108 -0.1217962
hsa00051 Fructose and mannose metabolism          -0.02453256  1.2517426
hsa00052 Galactose metabolism                     -0.63031574 -0.3875319
                                                      DCIS_3      DCIS_4
hsa00010 Glycolysis / Gluconeogenesis              1.1776479  0.19783351
hsa00020 Citrate cycle (TCA cycle)                -0.4562623  1.52095824
hsa00030 Pentose phosphate pathway                 0.9296768  1.49548630
hsa00040 Pentose and glucuronate interconversions  0.7073109  0.31414842
hsa00051 Fructose and mannose metabolism           1.3248564 -0.01412957
hsa00052 Galactose metabolism                      0.3365236  0.26750878
                                                       DCIS_5    DCIS_6
hsa00010 Glycolysis / Gluconeogenesis              0.35453672 0.9102139
hsa00020 Citrate cycle (TCA cycle)                 0.83971661 0.9443768
hsa00030 Pentose phosphate pathway                 0.16072897 1.2265214
hsa00040 Pentose and glucuronate interconversions  0.11347352 0.8965622
hsa00051 Fructose and mannose metabolism           0.53844637 1.1764069
hsa00052 Galactose metabolism                     -0.04967126 0.5702118
                                                      DCIS_1     DCIS_2
hsa00010 Glycolysis / Gluconeogenesis             0.56866465 0.38732859
hsa00020 Citrate cycle (TCA cycle)                0.02571559 0.08650252
hsa00030 Pentose phosphate pathway                0.66655939 0.18018116
hsa00040 Pentose and glucuronate interconversions 0.17128191 0.54789334
hsa00051 Fructose and mannose metabolism          0.50974482 0.10776087
hsa00052 Galactose metabolism                     0.73424740 0.64995712
                                                      DCIS_3     DCIS_4
hsa00010 Glycolysis / Gluconeogenesis             0.12074159 0.42177127
hsa00020 Citrate cycle (TCA cycle)                0.67469124 0.06723811
hsa00030 Pentose phosphate pathway                0.17917106 0.07083082
hsa00040 Pentose and glucuronate interconversions 0.24268625 0.37790116
hsa00051 Fructose and mannose metabolism          0.09525001 0.50561325
hsa00052 Galactose metabolism                     0.36909801 0.39511277
                                                     DCIS_5    DCIS_6
hsa00010 Glycolysis / Gluconeogenesis             0.3618164 0.1824904
hsa00020 Citrate cycle (TCA cycle)                0.2032506 0.1751263
hsa00030 Pentose phosphate pathway                0.4366270 0.1142512
hsa00040 Pentose and glucuronate interconversions 0.4554263 0.1901948
hsa00051 Fructose and mannose metabolism          0.2961745 0.1220564
hsa00052 Galactose metabolism                     0.5197036 0.2856843

gage documentation built on Dec. 13, 2020, 2:01 a.m.