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", "enrichment_in_relationships".
...
further arguments
Details
Further arguments and enrichment method optional argument information
datamatrix Is a mxn matrix of gene expression data, with m gene names (rows) and n sample/condition (columns). This is an optional argument used with all the active enrichment methods.
id_column Is a character string, a column name of datamatrix, that is used to specify a column for identifiers (if these are not specified as rownames in datamatrix). This is an optional argument used with all active enrichment methods with the exception of 'enrichment_in_relationships'.
primary_columns Is a character vector composed of column names from datamatrix, 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. 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 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 abundance data above the threshold argument are kept. When set to FALSE genes with abundance data 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.
\u 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/expression values in datamatrix primary_columns with those in
datamatrix 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 datamatrix 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 datamatrix 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 datamatrix is
a data frame or matrix, the background used for calculation will be taken as the rownames of datamatrix
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 datamatrix. 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.
enrichment_in_relationships
Calculates the enrichment of a pathway in specified interactions relative to non-pathway members. For each pathway in geneset
calculates the enrichment in relationships as defined by an adjacency matrix provided in datamatrix. An adjacency matrix
is a square matrix to provide pairwise relationships between components (genes, proteins) as derived from e.g. correlation as
correlation_enrichment.
Examples
dontrun{
library(leapr)
# read in the example abundance data
data("protdata")
# 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',
datamatrix=protdata, 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',
datamatrix=protdata, 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',
datamatrix=protdata, primary_columns="TCGA-13-1484")
# use enrichment_in_pathways 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_pathways',
datamatrix=protdata, 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='enrichment_in_correlation',
datamatrix=protdata, primary_columns=shortlist)
}
biodataganache/leapR documentation built on Jan. 20, 2024, 2:55 a.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", "enrichment_in_relationships". |
... |
further arguments |
Details
Further arguments and enrichment method optional argument information
| datamatrix | Is a mxn matrix of gene expression data, with m gene names (rows) and n sample/condition (columns). This is an optional argument used with all the active enrichment methods. |
| id_column | Is a character string, a column name of datamatrix, that is used to specify a column for identifiers (if these are not specified as rownames in datamatrix). This is an optional argument used with all active enrichment methods with the exception of 'enrichment_in_relationships'. |
| primary_columns | Is a character vector composed of column names from datamatrix, 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. 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 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 abundance data above the threshold argument are kept. When set to FALSE genes with abundance data 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/expression values in datamatrix primary_columns with those in
datamatrix 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 datamatrix 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 datamatrix 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 datamatrix is
a data frame or matrix, the background used for calculation will be taken as the rownames of datamatrix
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 datamatrix. 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.
enrichment_in_relationships
Calculates the enrichment of a pathway in specified interactions relative to non-pathway members. For each pathway in geneset
calculates the enrichment in relationships as defined by an adjacency matrix provided in datamatrix. An adjacency matrix
is a square matrix to provide pairwise relationships between components (genes, proteins) as derived from e.g. correlation as
correlation_enrichment.
Examples
dontrun{
library(leapr)
# read in the example abundance data
data("protdata")
# 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',
datamatrix=protdata, 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',
datamatrix=protdata, 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',
datamatrix=protdata, primary_columns="TCGA-13-1484")
# use enrichment_in_pathways 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_pathways',
datamatrix=protdata, 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='enrichment_in_correlation',
datamatrix=protdata, primary_columns=shortlist)
}
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