leapR: leapR
In biodataganache/leapR: Layered enrichment analysis of pathways R
leapR R Documentation
leapR
Description
leapR is a wrapper function that consolidates multiple enrichment methods.
Usage
leapR(geneset, enrichment_method, ...)
Arguments
geneset
is a list of four vectors, gene names, gene descriptions, gene sizes and a matrix of genes. It represents .gmt format pathway files.
enrichment_method
is a character string specifying the method of enrichment to be performed, one of: "enrichment_comparison", "enrichment_in_order", "enrichment_in_sets", "enrichment_in_pathway", "correlation_enrichment".
...
further arguments
Details
Further arguments and enrichment method optional argument information:
eset Is an ExpressionSet of expression data, with features as rows and n sample/conditions as columns.
The Annotation field ideally describes the data type (i.e. proteomics, phosphoproteomics), the featureData field describes any mapping
identifiers and the phenoData field describes any phenotyptic data. We recommend that the `Annotation` slot contain the omics data type for when using with `combine_omics`
This is an required for all active enrichment methods with the exception of 'enrichment_in_relationships'.
id_column Is a character string, present in the featureData slot, that is used to specify a column for identifiers to map to enrichment libraries.
If missing, the rownames of the ExpressionSet will be used.
primary_columns Is a character vector composed of column names from eset (either in the `exprs` or in the `featureData`),
that specifies a set of primary columns to calculate enrichment on.
The meaning of this varies according to the enrichment method used - see the descriptions for each method below.
This is an optional argument used with 'enrichment_in_order', 'enrichment_in_sets', and 'enrichment_comparison' methods.
secondary_columns Is a character vector of column names for comparison, pulled from the `exprs` of the ExpressionSet. This is an optional argument used with 'enrichment_comparison' methods.
threshold Is a numeric value, an optional argument used with 'enrichment_in sets' method which filters out abundance values or p-values (depending on what `primary_columns` is used)
either above or below it.
greaterthan Is a logical value that defaults to TRUE, it's used with 'enrichment_in_sets' method.
When set to TRUE, genes with `primary_columns` value above the threshold argument are kept.
When set to FALSE genes with `primary_columns` value below the threshold argument are kept.
This is an optional argument used with 'enrichment_in_sets' method.
minsize Is a numeric value, an optional argument used with 'enrichment_in_sets' and 'enrichment_in_order".
idmap Is...??. This is an optional argument used with 'enrichment_in_relationships' method.
fdr A numerical value which specifies how many times to randomly sample genes to calculate an empirical false discovery rate, is an optional argument used with 'enrichment_comparison' method.
min_p_threshold Is a numeric value, a lower p-value threshold and is an optional argument used with 'enrichment_comparison' method.
sample_n Is a way to subsample the number of components considered for each calculation randomly. This is an optional argument used with 'enrichment_comparison' method.
Enrichment Methods:
enrichment_comparison
Compares the distribution of abundances between two sets of conditions for each pathway using a t test. For each pathway in geneset
uses a t test to compare the distribution of abundance values/numbers in eset primary_columns with those in
eset secondary_columns. Lower p-values for pathways indicate that the expression of the pathway is
significantly different between the set of conditions in primary_columns and the set of conditions in secondary_columns.
Optionally, users can specify fdr which will calculate an empirical p-value by randomizing abdunances
fdr number of times. If the min_p_threshold is specified the method will only return pathways with an
adjusted p-value lower than the specified threshold. If sample_n is specified the method will subsample the
pathway members to the specified number of components.
enrichment_in_order
Calculates enrichment of pathways based on a ranked list using the Kologmorov-Smirnov test.
For each pathway in geneset uses a Kolgmorov-Smirnov test for rank order to test if the distribution
of ranked abundance values in the eset primary_columns is significant relative to a random
distribution. Note that currently primary_columns only accepts a single column for this method.
enrichment_in_sets
Calculates enrichment in pathway membership in a list (e.g. highly differential proteins) relative to background using Fisher's exact test.
For each pathway in geneset uses a Fisher's exact test over- or under- representation of a list
of components specified. If targets are specified this must be a vector of identifiers to serve
as the target list for comparison. If eset and primary_columns are specified then threshold specifies
a threshold value for determining the target list of components to test. Specifying greaterthan to be False
will result in components with values lower than the specified threshold. If eset is
a data frame or matrix, the background used for calculation will be taken as the rownames of eset
enrichment_in_pathway
Compares the distribution of abundances in a pathway with the background distribution of abundances using a t test
For each pathway in geneset calculates the signficance of the difference between the abundances
from pathway members versus abundance of non-pathway members in the set of conditions specified by primary_columns.
Optionally, users can specify fdr which will calculate an empirical p-value by randomizing abdunances
fdr number of times. If the min_p_threshold is specified the method will only return pathways with an
adjusted p-value lower than the specified threshold. If sample_n is specified the method will subsample the
pathway members to the specified number of components.
correlation_enrichment
Calculates the enrichment of a pathway based on correlation between pathway members across conditions versus correlation between members not in the pathway.
For each pathway in geneset calculates the pairwise correlation between all pathway members and non-pathway members
across the specified primary_columns conditions in eset. Note that for large matrices this can take a long
time. A p-value is calculated based on comparing the correlation within the members of a pathway with the correlation
values between members of the pathway and non-members of the pathway.
Value
data frame with results
Examples
library(leapR)
# read in the example abundance data
# read in the example transcriptomic data
tdata <- download.file("https://figshare.com/ndownloader/files/55781153",method='libcurl',destfile='transData.rda')
load('transData.rda')
p <- file.remove("transData.rda")
# read in the pathways
data("ncipid")
# read in the patient groups
data("shortlist")
data("longlist")
# use enrichment_comparison to calculate enrichment in one set of conditions (shortlist) and another
# (longlist)
short_v_long = leapR(geneset=ncipid, enrichment_method='enrichment_comparison',
eset=tset, primary_columns=shortlist, secondary_columns=longlist)
# use enrichment_in_sets to calculate the most enriched pathways from the highest abundance proteins
# from one condition
onept_sets = leapR(geneset=ncipid, enrichment_method='enrichment_in_sets',
eset=tset, primary_columns="TCGA-13-1484", threshold=1.5)
# use enrichment_in_order to calculate the most enriched pathways from the same condition
# Note: that this uses the entire set of abundance values and their order - whereas
# the previous example uses a hard threshold to get a short list of most abundant proteins
# and calculates enrichment based on set overlap. The results are likely to be similar - but
# with some notable differences.
onept_order = leapR(geneset=ncipid, enrichment_method='enrichment_in_order',
eset=tset, primary_columns="TCGA-13-1484")
# use enrichment_in_pathway to calculate the most enriched pathways in a set of conditions
# based on abundance in the pathway members versus abundance in non-pathway members
short_pathways = leapR(geneset=ncipid, enrichment_method='enrichment_in_pathway',
eset=tset, primary_columns=shortlist)
# use correlation_enrichment to calculate the most enriched pathways in correlation across
# the shortlist conditions
short_correlation_pathways = leapR(geneset=ncipid, enrichment_method='correlation_enrichment',
eset=tset, primary_columns=shortlist)
biodataganache/leapR documentation built on July 16, 2025, 11:10 p.m.
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| leapR | R Documentation |
leapR
Description
leapR is a wrapper function that consolidates multiple enrichment methods.
Usage
leapR(geneset, enrichment_method, ...)
Arguments
geneset |
is a list of four vectors, gene names, gene descriptions, gene sizes and a matrix of genes. It represents .gmt format pathway files. |
enrichment_method |
is a character string specifying the method of enrichment to be performed, one of: "enrichment_comparison", "enrichment_in_order", "enrichment_in_sets", "enrichment_in_pathway", "correlation_enrichment". |
... |
further arguments |
Details
Further arguments and enrichment method optional argument information:
| eset | Is an ExpressionSet of expression data, with features as rows and n sample/conditions as columns.
The Annotation field ideally describes the data type (i.e. proteomics, phosphoproteomics), the featureData field describes any mapping
identifiers and the phenoData field describes any phenotyptic data. We recommend that the `Annotation` slot contain the omics data type for when using with `combine_omics`
This is an required for all active enrichment methods with the exception of 'enrichment_in_relationships'. |
| id_column | Is a character string, present in the featureData slot, that is used to specify a column for identifiers to map to enrichment libraries.
If missing, the rownames of the ExpressionSet will be used.
|
| primary_columns | Is a character vector composed of column names from eset (either in the `exprs` or in the `featureData`),
that specifies a set of primary columns to calculate enrichment on.
The meaning of this varies according to the enrichment method used - see the descriptions for each method below.
This is an optional argument used with 'enrichment_in_order', 'enrichment_in_sets', and 'enrichment_comparison' methods. |
| secondary_columns | Is a character vector of column names for comparison, pulled from the `exprs` of the ExpressionSet. This is an optional argument used with 'enrichment_comparison' methods. |
| threshold | Is a numeric value, an optional argument used with 'enrichment_in sets' method which filters out abundance values or p-values (depending on what `primary_columns` is used) either above or below it. |
| greaterthan | Is a logical value that defaults to TRUE, it's used with 'enrichment_in_sets' method.
When set to TRUE, genes with `primary_columns` value above the threshold argument are kept.
When set to FALSE genes with `primary_columns` value below the threshold argument are kept.
This is an optional argument used with 'enrichment_in_sets' method. |
| minsize | Is a numeric value, an optional argument used with 'enrichment_in_sets' and 'enrichment_in_order". |
| idmap | Is...??. This is an optional argument used with 'enrichment_in_relationships' method. |
| fdr | A numerical value which specifies how many times to randomly sample genes to calculate an empirical false discovery rate, is an optional argument used with 'enrichment_comparison' method. |
| min_p_threshold | Is a numeric value, a lower p-value threshold and is an optional argument used with 'enrichment_comparison' method. |
| sample_n | Is a way to subsample the number of components considered for each calculation randomly. This is an optional argument used with 'enrichment_comparison' method. |
Enrichment Methods:
enrichment_comparison
Compares the distribution of abundances between two sets of conditions for each pathway using a t test. For each pathway in geneset
uses a t test to compare the distribution of abundance values/numbers in eset primary_columns with those in
eset secondary_columns. Lower p-values for pathways indicate that the expression of the pathway is
significantly different between the set of conditions in primary_columns and the set of conditions in secondary_columns.
Optionally, users can specify fdr which will calculate an empirical p-value by randomizing abdunances
fdr number of times. If the min_p_threshold is specified the method will only return pathways with an
adjusted p-value lower than the specified threshold. If sample_n is specified the method will subsample the
pathway members to the specified number of components.
enrichment_in_order
Calculates enrichment of pathways based on a ranked list using the Kologmorov-Smirnov test.
For each pathway in geneset uses a Kolgmorov-Smirnov test for rank order to test if the distribution
of ranked abundance values in the eset primary_columns is significant relative to a random
distribution. Note that currently primary_columns only accepts a single column for this method.
enrichment_in_sets
Calculates enrichment in pathway membership in a list (e.g. highly differential proteins) relative to background using Fisher's exact test.
For each pathway in geneset uses a Fisher's exact test over- or under- representation of a list
of components specified. If targets are specified this must be a vector of identifiers to serve
as the target list for comparison. If eset and primary_columns are specified then threshold specifies
a threshold value for determining the target list of components to test. Specifying greaterthan to be False
will result in components with values lower than the specified threshold. If eset is
a data frame or matrix, the background used for calculation will be taken as the rownames of eset
enrichment_in_pathway
Compares the distribution of abundances in a pathway with the background distribution of abundances using a t test
For each pathway in geneset calculates the signficance of the difference between the abundances
from pathway members versus abundance of non-pathway members in the set of conditions specified by primary_columns.
Optionally, users can specify fdr which will calculate an empirical p-value by randomizing abdunances
fdr number of times. If the min_p_threshold is specified the method will only return pathways with an
adjusted p-value lower than the specified threshold. If sample_n is specified the method will subsample the
pathway members to the specified number of components.
correlation_enrichment
Calculates the enrichment of a pathway based on correlation between pathway members across conditions versus correlation between members not in the pathway.
For each pathway in geneset calculates the pairwise correlation between all pathway members and non-pathway members
across the specified primary_columns conditions in eset. Note that for large matrices this can take a long
time. A p-value is calculated based on comparing the correlation within the members of a pathway with the correlation
values between members of the pathway and non-members of the pathway.
Value
data frame with results
Examples
library(leapR)
# read in the example abundance data
# read in the example transcriptomic data
tdata <- download.file("https://figshare.com/ndownloader/files/55781153",method='libcurl',destfile='transData.rda')
load('transData.rda')
p <- file.remove("transData.rda")
# read in the pathways
data("ncipid")
# read in the patient groups
data("shortlist")
data("longlist")
# use enrichment_comparison to calculate enrichment in one set of conditions (shortlist) and another
# (longlist)
short_v_long = leapR(geneset=ncipid, enrichment_method='enrichment_comparison',
eset=tset, primary_columns=shortlist, secondary_columns=longlist)
# use enrichment_in_sets to calculate the most enriched pathways from the highest abundance proteins
# from one condition
onept_sets = leapR(geneset=ncipid, enrichment_method='enrichment_in_sets',
eset=tset, primary_columns="TCGA-13-1484", threshold=1.5)
# use enrichment_in_order to calculate the most enriched pathways from the same condition
# Note: that this uses the entire set of abundance values and their order - whereas
# the previous example uses a hard threshold to get a short list of most abundant proteins
# and calculates enrichment based on set overlap. The results are likely to be similar - but
# with some notable differences.
onept_order = leapR(geneset=ncipid, enrichment_method='enrichment_in_order',
eset=tset, primary_columns="TCGA-13-1484")
# use enrichment_in_pathway to calculate the most enriched pathways in a set of conditions
# based on abundance in the pathway members versus abundance in non-pathway members
short_pathways = leapR(geneset=ncipid, enrichment_method='enrichment_in_pathway',
eset=tset, primary_columns=shortlist)
# use correlation_enrichment to calculate the most enriched pathways in correlation across
# the shortlist conditions
short_correlation_pathways = leapR(geneset=ncipid, enrichment_method='correlation_enrichment',
eset=tset, primary_columns=shortlist)
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