ssea.analyze: Marker set enrichment analysis (MSEA)
In Mergeomics: Integrative network analysis of omics data
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
ssea.analyze finds the enrichment of the pathways or
co-expression modules by a marker set (e.g. associated risk variants
-loci- of a relevant disease).
Association study by mapping markers (e.g. SNPs) to genes (e.g. via
expression QTLs).
Enrichment P-values obtained by the MSEA denote the degree of enrichment
of significantly disease-associated (high ranking) markers (e.g. eSNPs)
within these pathways when compared to the null distribution of expected
uniform distribution of all ranks of the markers.
MSEA is performed with either gene-level or marker-level permutations
based on Gaussian distribution.
Usage
1
ssea.analyze(job, trim_start, trim_end)
Arguments
job
the data list including fields: seed for random number generator
(job$seed) (to obtain the same results from permutation when
the same input set is given), random permutation level
(job$permtype) (either gene- or marker-level), maximum number
of permutations (job$nperm) for the permutation test, and the
database that uses indexed identities for modules, genes, and markers
(e.g. loci) (job$database).
trim_start
percentile taken from the beginning for
trimming away a defined proportion of genes with significant trait
association to avoid signal inflation of null background in gene permutation.
Default value is 0.002.
trim_end
percentile taken from the ending point for
trimming away a defined proportion of genes with significant trait
association to avoid signal inflation of null background in gene permutation.
Default value is 0.998.
Details
ssea.analyze associates the gene sets (pathways or
co-expression modules) with relevant disease (e.g. Coronary Artery Disease)
association data by mapping markers (e.g. SNPs) to genes (e.g. via expression
QTLs).
It performs the MSEA by using observed and estimated enrichment scores.
First, the observed enrichment scores of the pathways by markers (e.g.
loci) are calculated. Then, a Gaussian distribution based simulation is
performed, by using the statistics of the observed scores (mean, std.dev.,
etc.), to obtain the estimated enrichment scores, enrichment frequencies,
and other statistics e.g. p-values for the pathways.
ssea.analyze trims away a defined proportion of genes with
significant trait association to avoid signal inflation of null background
in gene permutation by using trim_start and trim_end.
Value
job
the updated data frame including results: indexed module
identity, enrichment P-values, raw frequencies (raw frequency of a gene
set defines the number of the estimated enrichment scores that are larger
than this gene set's enrichment score under the null distribution based
on Gaussian function)
.
Author(s)
Ville-Petteri Makinen
References
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD,
Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X.
Mergeomics: multidimensional data integration to identify pathogenic
perturbations to biological systems. BMC genomics. 2016;17(1):874.
See Also
ssea.control, ssea.finish,
ssea.prepare, ssea.start,
ssea2kda
Examples
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job.msea <- list()
job.msea$label <- "hdlc"
job.msea$folder <- "Results"
job.msea$genfile <- system.file("extdata",
"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$marfile <- system.file("extdata",
"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$modfile <- system.file("extdata",
"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
job.msea$inffile <- system.file("extdata",
"coexpr.info.txt", package="Mergeomics")
job.msea$nperm <- 100 ## default value is 20000
## ssea.start() process takes long time while merging the genes sharing high
## amounts of markers (e.g. loci). it is performed with full module list in
## the vignettes. Here, we used a very subset of the module list (1st 10 mods
## from the original module file) and we collected the corresponding genes
## and markers belonging to these modules:
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
unique(moddata$GENE)))),]
mardata <- mardata[which(!is.na(match(mardata$MARKER,
unique(gendata$MARKER)))),]
## save this to a temporary file and set its path as new job.msea$modfile:
tool.save(moddata, "subsetof.coexpr.modules.txt")
tool.save(gendata, "subsetof.genfile.txt")
tool.save(mardata, "subsetof.marfile.txt")
job.msea$modfile <- "subsetof.coexpr.modules.txt"
job.msea$genfile <- "subsetof.genfile.txt"
job.msea$marfile <- "subsetof.marfile.txt"
## run ssea.start() and prepare for this small set: (due to the huge runtime)
job.msea <- ssea.start(job.msea)
job.msea <- ssea.prepare(job.msea)
job.msea <- ssea.control(job.msea)
job.msea <- ssea.analyze(job.msea)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
Example output
Writing to file...
Saved 1744 rows in 'subsetof.coexpr.modules.txt'.
[1] "subsetof.coexpr.modules.txt"
Writing to file...
Saved 7376 rows in 'subsetof.genfile.txt'.
[1] "subsetof.genfile.txt"
Writing to file...
Saved 7070 rows in 'subsetof.marfile.txt'.
[1] "subsetof.marfile.txt"
MSEA Version:01.04.2016
Parameters:
Permutation type: gene
Permutations: 100
Random seed: 1
Minimum gene count: 10
Maximum gene count: 500
Maximum overlap between genes: 0.33
Importing modules...
MODULE DESCR
Length:20 Length:20
Class :character Class :character
Mode :character Mode :character
MODULE GENE
Length:1744 Length:1744
Class :character Class :character
Mode :character Mode :character
Importing marker values...
MARKER VALUE
Length:7070 Min. : 0.8094
Class :character 1st Qu.: 0.9531
Mode :character Median : 1.1685
Mean : 1.8574
3rd Qu.: 1.5491
Max. :323.0100
Importing mapping data...
GENE MARKER
Length:7376 Length:7376
Class :character Class :character
Mode :character Mode :character
Merging genes containing shared markers...
16110 comparisons
7260 comparisons
7021 comparisons
Job: 0.7925262 Mb
Preparing data structures...
Job: 1.21257 Mb
Adding positive controls...
Job: 1.298004 Mb
Estimating enrichment...
100/100 cycles
Normalizing scores...
[1] TRUE
[1] TRUE
[1] TRUE
Mergeomics documentation built on Nov. 8, 2020, 6:58 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
ssea.analyze finds the enrichment of the pathways or
co-expression modules by a marker set (e.g. associated risk variants
-loci- of a relevant disease).
Association study by mapping markers (e.g. SNPs) to genes (e.g. via
expression QTLs).
Enrichment P-values obtained by the MSEA denote the degree of enrichment
of significantly disease-associated (high ranking) markers (e.g. eSNPs)
within these pathways when compared to the null distribution of expected
uniform distribution of all ranks of the markers.
MSEA is performed with either gene-level or marker-level permutations
based on Gaussian distribution.
Usage
1 | ssea.analyze(job, trim_start, trim_end)
|
Arguments
job |
the data list including fields: seed for random number generator
|
trim_start |
percentile taken from the beginning for trimming away a defined proportion of genes with significant trait association to avoid signal inflation of null background in gene permutation. Default value is 0.002. |
trim_end |
percentile taken from the ending point for trimming away a defined proportion of genes with significant trait association to avoid signal inflation of null background in gene permutation. Default value is 0.998. |
Details
ssea.analyze associates the gene sets (pathways or
co-expression modules) with relevant disease (e.g. Coronary Artery Disease)
association data by mapping markers (e.g. SNPs) to genes (e.g. via expression
QTLs).
It performs the MSEA by using observed and estimated enrichment scores.
First, the observed enrichment scores of the pathways by markers (e.g.
loci) are calculated. Then, a Gaussian distribution based simulation is
performed, by using the statistics of the observed scores (mean, std.dev.,
etc.), to obtain the estimated enrichment scores, enrichment frequencies,
and other statistics e.g. p-values for the pathways.
ssea.analyze trims away a defined proportion of genes with
significant trait association to avoid signal inflation of null background
in gene permutation by using trim_start and trim_end.
Value
job |
the updated data frame including results: indexed module identity, enrichment P-values, raw frequencies (raw frequency of a gene set defines the number of the estimated enrichment scores that are larger than this gene set's enrichment score under the null distribution based on Gaussian function) |
.
Author(s)
Ville-Petteri Makinen
References
Shu L, Zhao Y, Kurt Z, Byars SG, Tukiainen T, Kettunen J, Orozco LD, Pellegrini M, Lusis AJ, Ripatti S, Zhang B, Inouye M, Makinen V-P, Yang X. Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems. BMC genomics. 2016;17(1):874.
See Also
ssea.control, ssea.finish,
ssea.prepare, ssea.start,
ssea2kda
Examples
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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | job.msea <- list()
job.msea$label <- "hdlc"
job.msea$folder <- "Results"
job.msea$genfile <- system.file("extdata",
"genes.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$marfile <- system.file("extdata",
"marker.hdlc_040kb_ld70.human_eliminated.txt", package="Mergeomics")
job.msea$modfile <- system.file("extdata",
"modules.mousecoexpr.liver.human.txt", package="Mergeomics")
job.msea$inffile <- system.file("extdata",
"coexpr.info.txt", package="Mergeomics")
job.msea$nperm <- 100 ## default value is 20000
## ssea.start() process takes long time while merging the genes sharing high
## amounts of markers (e.g. loci). it is performed with full module list in
## the vignettes. Here, we used a very subset of the module list (1st 10 mods
## from the original module file) and we collected the corresponding genes
## and markers belonging to these modules:
moddata <- tool.read(job.msea$modfile)
gendata <- tool.read(job.msea$genfile)
mardata <- tool.read(job.msea$marfile)
mod.names <- unique(moddata$MODULE)[1:min(length(unique(moddata$MODULE)),
10)]
moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),]
gendata <- gendata[which(!is.na(match(gendata$GENE,
unique(moddata$GENE)))),]
mardata <- mardata[which(!is.na(match(mardata$MARKER,
unique(gendata$MARKER)))),]
## save this to a temporary file and set its path as new job.msea$modfile:
tool.save(moddata, "subsetof.coexpr.modules.txt")
tool.save(gendata, "subsetof.genfile.txt")
tool.save(mardata, "subsetof.marfile.txt")
job.msea$modfile <- "subsetof.coexpr.modules.txt"
job.msea$genfile <- "subsetof.genfile.txt"
job.msea$marfile <- "subsetof.marfile.txt"
## run ssea.start() and prepare for this small set: (due to the huge runtime)
job.msea <- ssea.start(job.msea)
job.msea <- ssea.prepare(job.msea)
job.msea <- ssea.control(job.msea)
job.msea <- ssea.analyze(job.msea)
## Remove the temporary files used for the test:
file.remove("subsetof.coexpr.modules.txt")
file.remove("subsetof.genfile.txt")
file.remove("subsetof.marfile.txt")
|
Example output
Writing to file...
Saved 1744 rows in 'subsetof.coexpr.modules.txt'.
[1] "subsetof.coexpr.modules.txt"
Writing to file...
Saved 7376 rows in 'subsetof.genfile.txt'.
[1] "subsetof.genfile.txt"
Writing to file...
Saved 7070 rows in 'subsetof.marfile.txt'.
[1] "subsetof.marfile.txt"
MSEA Version:01.04.2016
Parameters:
Permutation type: gene
Permutations: 100
Random seed: 1
Minimum gene count: 10
Maximum gene count: 500
Maximum overlap between genes: 0.33
Importing modules...
MODULE DESCR
Length:20 Length:20
Class :character Class :character
Mode :character Mode :character
MODULE GENE
Length:1744 Length:1744
Class :character Class :character
Mode :character Mode :character
Importing marker values...
MARKER VALUE
Length:7070 Min. : 0.8094
Class :character 1st Qu.: 0.9531
Mode :character Median : 1.1685
Mean : 1.8574
3rd Qu.: 1.5491
Max. :323.0100
Importing mapping data...
GENE MARKER
Length:7376 Length:7376
Class :character Class :character
Mode :character Mode :character
Merging genes containing shared markers...
16110 comparisons
7260 comparisons
7021 comparisons
Job: 0.7925262 Mb
Preparing data structures...
Job: 1.21257 Mb
Adding positive controls...
Job: 1.298004 Mb
Estimating enrichment...
100/100 cycles
Normalizing scores...
[1] TRUE
[1] TRUE
[1] TRUE
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