Package 'leapR' reference manual

Title:	Layered enrichment analysis of pathways R
Description:	leapR is a package that identifies pathways that are enriched across diverse 'omics experiments. It leverages any tabular expression data (proteomics, transcriptomics) using the `SummarizedExperiment` object. It works with any pathway in the .gct file format.
Authors:	Sara Gosline [aut, cre] (ORCID: <https://orcid.org/0000-0002-6534-4774>), Jason McDermott [aut], Jeremy Jacobson [aut], Vincent Danna [ctb], National Institutes of Health [fnd]
Maintainer:	Sara Gosline <[email protected]>
License:	MIT + file LICENSE
Version:	1.1.0
Built:	2026-05-30 07:44:56 UTC
Source:	https://github.com/bioc/leapR

calcTTest

Description

calculates a t-test for two distributions of data on a per-gene basis append results to ExpressionSet with two extra columns: 'pvalue' and 'difference' for each feature

Usage

calcTTest(eset, assay_name, group1, group2)
calcTTest(eset, assay_name, group1, group2)

Arguments

eset

SummarizedExperiment

assay_name

name of assay

group1

List of samples comprising group 1

group2

List of samples comprising group 2

Value

An Expression set with two columns added to the featureData slot: pvalue, and estimate

Examples


        library(leapR)
        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,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")
        calcTTest(tset, 'transcriptomics', shortlist, longlist)
library(leapR)
        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,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")
        calcTTest(tset, 'transcriptomics', shortlist, longlist)

cluster_enrichment

Description

Cluster enrichment Run enrichment (Fisher's exact) on clusters (lists of identifier groups)

Usage

cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)
cluster_enrichment(eset, assay_name, geneset, clusters, sigfilter = 0.05)

Arguments

eset

is an SummarizedExperiment containing data that is clustered

assay_name

is the name of the assay

geneset

is a GeneSet object for pathway annotation

clusters

is a list of clusters (gene lists) to calculate enrichment on, generally the result of the 'cutree' function

sigfilter

minimum significance threshold default is .05

Details

This function will calculate enrichment (Fisher's exact test for membership overlap) on

a series of lists of genes, such as from a set of clusters. The results are returned as

a list of results matrices in the order of the input clusters.

Value

data frame with enrichment results

Examples

        library(leapR)

        # read in the example transcriptomic data
        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
        load('transData.rda')
        p <- file.remove("transData.rda")

        # read in the pathways
        data("ncipid")

        # for the example we will limit the number of transcripts considered
        #- arbitrarily in this case
        transdata <- SummarizedExperiment::assay(tset,'transcriptomics')
        transdata[which(is.na(transdata),arr.ind=TRUE)]<-0.0
        # perform heirarchical clustering on the  data
        transdata.hc <- hclust(dist(transdata), method="ward.D2")

        transdata.hc.clusters <- cutree(transdata.hc, k=5)
        clust.list <- lapply(seq_len(5), function(x) {
           return(names(which(transdata.hc.clusters==x)))})
        #calculates enrichment for each of the clusters individually a
        #and returns a list of enrichment results
        transdata.hc.enrichment <- leapR::cluster_enrichment(eset=tset,
                assay_name='transcriptomics',
                geneset=ncipid,
                clusters=clust.list)



library(leapR)

        # read in the example transcriptomic data
        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
        load('transData.rda')
        p <- file.remove("transData.rda")

        # read in the pathways
        data("ncipid")

        # for the example we will limit the number of transcripts considered
        #- arbitrarily in this case
        transdata <- SummarizedExperiment::assay(tset,'transcriptomics')
        transdata[which(is.na(transdata),arr.ind=TRUE)]<-0.0
        # perform heirarchical clustering on the  data
        transdata.hc <- hclust(dist(transdata), method="ward.D2")

        transdata.hc.clusters <- cutree(transdata.hc, k=5)
        clust.list <- lapply(seq_len(5), function(x) {
           return(names(which(transdata.hc.clusters==x)))})
        #calculates enrichment for each of the clusters individually a
        #and returns a list of enrichment results
        transdata.hc.enrichment <- leapR::cluster_enrichment(eset=tset,
                assay_name='transcriptomics',
                geneset=ncipid,
                clusters=clust.list)

combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

Description

combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

Usage

combine_omics(omics_list, id_list = rep(NA, length(omics_list)))
combine_omics(omics_list, id_list = rep(NA, length(omics_list)))

Arguments

omics_list

Is a list of SummarizedExperiment each with one assay

id_list

List of identifiers to use, in the same order as the omics_list elements. If an element is 'NA', then rownames are used.

Details

This combines matrices of different omics types together and adds prefix tags to the ids.

Value

SummarizedExperiment with an additional assay called 'combined'

Examples

        library(leapR)
        url <- 'https://api.figshare.com/v2/file/download/56536217'

        pdata <- download.file(url,method='libcurl',destfile='protData.rda')
        load('protData.rda')
        p <- file.remove("protData.rda")

        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
        load('transData.rda')
        p <- file.remove("transData.rda")

        url <- 'https://api.figshare.com/v2/file/download/56536211'
        phdata<-download.file(url,method='libcurl',destfile = 'phosData.rda')
        #phosphodata<-read.csv("phdata",check.names=FALSE,row.names=1)
        load('phosData.rda')
        p <- file.remove('phosData.rda')# read in the example protein data


        # merge the three datasets by rows and add prefix tags for
        # different omics types
        multi_omics <- combine_omics(list(pset, tset, phset),
                    list(NA,NA,'hgnc_id'))


library(leapR)
        url <- 'https://api.figshare.com/v2/file/download/56536217'

        pdata <- download.file(url,method='libcurl',destfile='protData.rda')
        load('protData.rda')
        p <- file.remove("protData.rda")

        url <- "https://api.figshare.com/v2/file/download/56536214"
        tdata <- download.file(url,method='libcurl',destfile='transData.rda')
        load('transData.rda')
        p <- file.remove("transData.rda")

        url <- 'https://api.figshare.com/v2/file/download/56536211'
        phdata<-download.file(url,method='libcurl',destfile = 'phosData.rda')
        #phosphodata<-read.csv("phdata",check.names=FALSE,row.names=1)
        load('phosData.rda')
        p <- file.remove('phosData.rda')# read in the example protein data


        # merge the three datasets by rows and add prefix tags for
        # different omics types
        multi_omics <- combine_omics(list(pset, tset, phset),
                    list(NA,NA,'hgnc_id'))

correlation_comparison_enrichment

Description

# internal function to calculate enrichment in differences in correlation # between two groups # access through the leapr wrapper

Usage

correlation_comparison_enrichment(
  geneset,
  eset,
  assay_name,
  set1,
  set2,
  mapping_column = NA
)
correlation_comparison_enrichment(
  geneset,
  eset,
  assay_name,
  set1,
  set2,
  mapping_column = NA
)

Arguments

geneset

pathway to use for enrichment

eset

SummarizedExperiment with abundance matrix

assay_name

name of assay

set1

first set to use

set2

second set to use

mapping_column

Column to use for id mapping within rowData

Value

data frame with enrichment results

correlation_enrichment

Description

# calculate enrichment in correlation between pathway members # access through leapr wrapper

Usage

correlation_enrichment(geneset, eset, assay_name, mapping_column = NA)
correlation_enrichment(geneset, eset, assay_name, mapping_column = NA)

Arguments

geneset

Geneset list

eset

a SummarizedExperiment object

assay_name

name of assay

mapping_column

Column to use to map identifiers, if not rownames

Value

list of enrichment statistic table and correlation matrix

enrichment_in_abundance

Description

Enrichment in abundance calculates enrichment in pathways by the difference in abundance of the pathway members.

Usage

enrichment_in_abundance(
  geneset,
  eset,
  assay_name,
  mapping_column = NULL,
  abundance_column = NULL,
  fdr = 0,
  matchset = NULL,
  sample_comparison = NULL,
  min_p_threshold = NULL,
  sample_n = NULL,
  silence_try_errors = TRUE
)
enrichment_in_abundance(
  geneset,
  eset,
  assay_name,
  mapping_column = NULL,
  abundance_column = NULL,
  fdr = 0,
  matchset = NULL,
  sample_comparison = NULL,
  min_p_threshold = NULL,
  sample_n = NULL,
  silence_try_errors = TRUE
)

Arguments

geneset

Gene set to calculate enrichment

eset

Molecular abundance data in 'SummarizedExperiment' format

assay_name

Name of assay to compare

mapping_column

Column to use to map identifiers

abundance_column

Columns to use to quantify abundance

fdr

number of times to sample for FDR value

matchset

Name of a set to use for enrichment

sample_comparison

list of samples to use as comparison. if missing background (eset) is used

min_p_threshold

Only include p-values lower than this

sample_n

size of sample to use

silence_try_errors

set to true to silence try errors

Value

data frame of enrichment result

enrichment_in_groups

Description

Calculate the enrichment in pathways using Fisher's exact or Kolmogorov-Smirnov test, using either the abundance column to identify feature or the targets list. access through leapr wrapper

Usage

enrichment_in_groups(
  geneset,
  targets = c(),
  background = NULL,
  assay_name = NULL,
  method = "fishers",
  minsize = 5,
  mapping_column = NULL,
  log_transformed = FALSE,
  abundance_column = NULL,
  randomize = FALSE,
  silence_try_errors = TRUE
)
enrichment_in_groups(
  geneset,
  targets = c(),
  background = NULL,
  assay_name = NULL,
  method = "fishers",
  minsize = 5,
  mapping_column = NULL,
  log_transformed = FALSE,
  abundance_column = NULL,
  randomize = FALSE,
  silence_try_errors = TRUE
)

Arguments

geneset

geneset to use for enrichment

targets

targets to use for enrichment

background

'SummarizedExperiment' describing background to use

assay_name

is the name of the assay to use from the background

method

method to use for statistical test, options are 'fishers', 'ks', 'ztest', or 'chisq'. Remember that KS test assumes normality, so it would be good to log your data before calling. NOTE: if you do not call 'suppressWarnings' then the KS test will warn you about ties.

minsize

minimum size of set

mapping_column

column name of mapping identifiers

log_transformed

Set to TRUE if data are log transformed

abundance_column

columns mapping abundance, either in the 'assay' matrix or 'rowData'

randomize

true/false whether to randomize

silence_try_errors

true/false to silence errors

Value

data frame with enrichment results

enrichment_in_relationships

Description

enrichment_in_relationships function description is a general way to determine if a pathway is enriched in relationships (interactions, correlation) between its members # access through leapr wrapper

Usage

enrichment_in_relationships(
  geneset,
  relationships,
  idmap = NA,
  silence_try_errors = TRUE
)
enrichment_in_relationships(
  geneset,
  relationships,
  idmap = NA,
  silence_try_errors = TRUE
)

Arguments

geneset

List of pathways in gmt format

relationships

table of relationship information, e.g. correlation

idmap

list of identifiers to use for mapping, the names of the items should agree with names of features in matrix

silence_try_errors

boolean to silence errors

Value

table of enrichment statistics

get_pathway_information

Description

get_pathway_information extracts information about a pathway from a GeneSet object

Usage

get_pathway_information(geneset, path, remove.tags = FALSE)
get_pathway_information(geneset, path, remove.tags = FALSE)

Arguments

geneset

is a GeneSet object for pathway annotation

path

is the name of the gene set pathway to be return

remove.tags

boolean indicating whether to remove tags

Value

list of pathway information

Examples


     library(leapR)

     # load example gene set
     data("ncipid")

     tnfpathway = get_pathway_information(ncipid, "tnfpathway")


library(leapR)

     # load example gene set
     data("ncipid")

     tnfpathway = get_pathway_information(ncipid, "tnfpathway")

Kinase substrate lists

Description

Kinase substrate lists

Usage

kinasesubstrates
kinasesubstrates

Format

A list with 4 items

names: The names of the kinases
desc: Short description of the kinase
sizes: Length of the substrate list
matrix: Substrate list for the kinase

Source

PhosphositePlus

KEGG, Reactome, BioCarta Pathways

Description

KEGG, Reactome, BioCarta Pathways

Usage

krbpaths
krbpaths

Format

A list with 4 items

names: The names of the pathways
desc: Short description of the pathways
sizes: Number of genes in the signaling pathways
matrix: Matrix containing the genes in the pathways

Source

https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp

leapR

Description

leapR is a wrapper function that consolidates multiple enrichment methods.

Usage

leapR(geneset, enrichment_method, eset, assay_name, ...)
leapR(geneset, enrichment_method, eset, assay_name, ...)

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".

eset

is a 'SummarizedExperiment' object containing expression data, with features as rows and n sample/conditions as columns.

assay_name

is the assay to be analyzed within the 'eset'. Recommended to describe the data type (e.g. transcriptomics, proteomics) so that it can be integrated in 'combine_omics'

...

further arguments

Details

Further arguments and enrichment method optional argument information:

id_column	Is a character string, present in the `rowData` slot, that is used to specify a column for identifiers to map to enrichment libraries. If missing, the rownames of the SummarizedExperiment assay will be used.
primary_columns	Is a character vector composed of column names from `eset` (either in the `assay` or in the `rowData`), 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 `assay` of the SummarizedExperiment. 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".

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 abundances 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 Kolmogorov-Smirnov test. For each pathway in geneset uses a Kolmogorov-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 significance 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 abundances 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://api.figshare.com/v2/file/download/56536214",
      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, assay_name='transcriptomics',
              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, assay_name='transcriptomics',
               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, assay_name='transcriptomics',
               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, assay_name='transcriptomics',
               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,
                assay_name='transcriptomics',
                enrichment_method='correlation_enrichment',
                eset=tset, primary_columns=shortlist)


library(leapR)

 # read in the example abundance data
 # read in the example transcriptomic data
 tdata <- download.file("https://api.figshare.com/v2/file/download/56536214",
      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, assay_name='transcriptomics',
              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, assay_name='transcriptomics',
               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, assay_name='transcriptomics',
               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, assay_name='transcriptomics',
               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,
                assay_name='transcriptomics',
                enrichment_method='correlation_enrichment',
                eset=tset, primary_columns=shortlist)

Long list of patient samples

Description

Long list of patient samples

Usage

longlist
longlist

Format

An object of class character of length 37.

NCI Gene lists

Description

A list of pathways and the genes that comprise these pathways

Usage

ncipid
ncipid

Format

A list with 4 items

names: The names of the signaling pathways
desc: Short description of the pathways
sizes: Number of genes in the signaling pathways
matrix: Matrix containing the genes in the pathways

Source

NCIPID

Plot leapR pathway bars (single panel)

Description

This plotting helper expects leapR generated results to plot. It will use BH_pvalue if present, otherwise pvalue.

Usage

plot_leapr_bar(
  res_df,
  title = NULL,
  top_n = 15,
  star_thresholds = c(0.05, 0.01, 0.001),
  wrap = 42,
  max_stars = 5L,
  fill_sig = "#2C7BB6",
  fill_ns = "#BFD7FF",
  outline = NA,
  axis_text_y_size = 8,
  axis_text_x_size = 9
)
plot_leapr_bar(
  res_df,
  title = NULL,
  top_n = 15,
  star_thresholds = c(0.05, 0.01, 0.001),
  wrap = 42,
  max_stars = 5L,
  fill_sig = "#2C7BB6",
  fill_ns = "#BFD7FF",
  outline = NA,
  axis_text_y_size = 8,
  axis_text_x_size = 9
)

Arguments

res_df

A leapR df containing BH_pvalue (or pvalue) and a pathway/term label column.

title

Plot title.

top_n

Number of top pathways/genes to display.

star_thresholds

list of numeric significance thresholds for star annotations.

wrap

Wrap width for pathway labels (helps formatting).

max_stars

Maximum number of stars to draw per bar (default 5).

fill_sig

Fill color for significant bars

fill_ns

Fill color for non-significant bars.

outline

Bar border color.

axis_text_y_size

Font size for y-axis (category) labels.

axis_text_x_size

Font size for x-axis (numeric) labels.

Value

A ggplot2 object (or NULL if nothing to plot).

read_gene_sets

Description

read_gene_sets is a function to import external pathway database files in .gmt format

Usage

read_gene_sets(
  gsfile,
  gene.labels = NA,
  gs.size.threshold.min = 5,
  gs.size.threshold.max = 15000
)
read_gene_sets(
  gsfile,
  gene.labels = NA,
  gs.size.threshold.min = 5,
  gs.size.threshold.max = 15000
)

Arguments

gsfile

is a gene set file, for example a .gmt file (gene matrix transposed file format)

gene.labels

defaults to NA

gs.size.threshold.min

defaults to 5

gs.size.threshold.max

defaults to 15000

Value

gene set object

geneset list

Examples

gfile <- system.file('extdata','h.all.v2024.1.Hs.symbols.gmt',
          package='leapR')
glist <- read_gene_sets(gfile)

gfile <- system.file('extdata','h.all.v2024.1.Hs.symbols.gmt',
          package='leapR')
glist <- read_gene_sets(gfile)

A list of pathways and genes that comprise these pathways from msigdb

Description

A list of pathways and genes that comprise these pathways from msigdb

Usage

shortlist
shortlist

Format

a list with 4 items

Short list of patient samples

Package 'leapR'

Help Index

calcTTest

Description

Usage

Arguments

Value

Examples

cluster_enrichment

Description

Usage

Arguments

Details

Value

Examples

combine_omics Combine two or more omics matrices into one multi-omics matrix with 'tagged' ids.

Description

Usage

Arguments

Details

Value

Examples

correlation_comparison_enrichment

Description

Usage

Arguments

Value

correlation_enrichment

Description

Usage

Arguments

Value

enrichment_in_abundance

Description

Usage

Arguments

Value

enrichment_in_groups

Description

Usage

Arguments

Value

enrichment_in_relationships

Description

Usage

Arguments

Value

get_pathway_information

Description

Usage

Arguments

Value

Examples

Kinase substrate lists

Description

Usage

Format

Source

KEGG, Reactome, BioCarta Pathways

Description

Usage

Format

Source

leapR

Description

Usage

Arguments

Details

Value

Examples

Long list of patient samples

Description

Usage

Format

NCI Gene lists

Description

Usage

Format

Source

Plot leapR pathway bars (single panel)