Package 'corto' reference manual

Title:	Inference of Gene Regulatory Networks
Description:	We present 'corto' (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.
Authors:	Federico M. Giorgi [aut, cre], Daniele Mercatelli [ctb], Gonzalo Lopez-Garcia [ctb]
Maintainer:	Federico M. Giorgi <[email protected]>
License:	LGPL-3
Version:	1.2.2
Built:	2024-09-18 03:38:03 UTC
Source:	https://github.com/federicogiorgi/corto

barplot2 - Bar plot with upper error bars

Description

barplot2 - Bar plot with upper error bars

Usage

barplot2(values, errors, ...)
barplot2(values, errors, ...)

Arguments

`values`	A matrix of values
`errors`	A matrix of values for upper error bar
`...`	Arguments to be passed to the core _barplot_ function

Value

A plot

Examples

values<-matrix(rnorm(10*4,mean=10),nrow=4,ncol=10)
errors<-matrix(runif(10*4),nrow=4,ncol=10)
colnames(values)<-colnames(errors)<-LETTERS[1:10]
barplot2(values,errors,main="Bar plot with error bars")
values<-matrix(rnorm(10*4,mean=10),nrow=4,ncol=10)
errors<-matrix(runif(10*4),nrow=4,ncol=10)
colnames(values)<-colnames(errors)<-LETTERS[1:10]
barplot2(values,errors,main="Bar plot with error bars")

Calculate a regulon from a data matrix

Description

This function applies Correlation and DPI to generate a robust regulon object based on the input data matrix and the selected centroids.

Usage

corto(
  inmat,
  centroids,
  nbootstraps = 100,
  p = 1e-30,
  nthreads = 1,
  verbose = FALSE,
  cnvmat = NULL,
  boot_threshold = 0
)
corto(
  inmat,
  centroids,
  nbootstraps = 100,
  p = 1e-30,
  nthreads = 1,
  verbose = FALSE,
  cnvmat = NULL,
  boot_threshold = 0
)

Arguments

`inmat`	Input matrix, with features (e.g. genes) as rows and samples as columns
`centroids`	A character vector indicating which features (e.g. genes) to consider as centroids (a.k.a. Master Regulators) for DPI
`nbootstraps`	Number of bootstraps to be performed. Default is 100
`p`	The p-value threshold for correlation significance (by default 1E-30)
`nthreads`	The number of threads to use for bootstrapping. Default is 1
`verbose`	Logical. Whether to print progress messages. Default is FALSE
`cnvmat`	An optional matrix with copy-number variation data. If specified, the program will calculate linear regression between the gene expression data in the input matrix (exp) and the cnv data, and target profiles will be transformed to the residuals of each linear model exp~cnv. Default is NULL
`boot_threshold`	The fraction of bootstraps in which the edge should appear to be included in the final network. It can be any number between 0.0 and 1.0. Default is 0.0.

Value

A list (object of class regulon), where each element is a centroid

tfmode: a named vector containing correlation coefficients between features and the centroid
likelihood: a numeric vector indicating the likelihood of interaction

Examples

# Load data matrix inmat (from TCGA mesothelioma project)
load(system.file("extdata","inmat.rda",package="corto",mustWork=TRUE))
# Load centroids
load(system.file("extdata","centroids.rda",package="corto",mustWork=TRUE))
# Run corto
regulon <- corto(inmat,centroids=centroids,nthreads=2,nbootstraps=10,verbose=TRUE)

# In a second example, a CNV matrix is provided. The analysis will be run only
# for the features (rows) and samples (columns) present in both matrices
load(system.file("extdata","cnvmat.rda",package="corto",mustWork=TRUE))
regulon <- corto(inmat,centroids=centroids,nthreads=2,nbootstraps=6,verbose=TRUE,cnvmat=cnvmat,
p=1e-8)
# Load data matrix inmat (from TCGA mesothelioma project)
load(system.file("extdata","inmat.rda",package="corto",mustWork=TRUE))
# Load centroids
load(system.file("extdata","centroids.rda",package="corto",mustWork=TRUE))
# Run corto
regulon <- corto(inmat,centroids=centroids,nthreads=2,nbootstraps=10,verbose=TRUE)

# In a second example, a CNV matrix is provided. The analysis will be run only
# for the features (rows) and samples (columns) present in both matrices
load(system.file("extdata","cnvmat.rda",package="corto",mustWork=TRUE))
regulon <- corto(inmat,centroids=centroids,nthreads=2,nbootstraps=6,verbose=TRUE,cnvmat=cnvmat,
p=1e-8)

A fast correlation function

Description

A fast correlation function

Usage

fcor(inmat, centroids, r)
fcor(inmat, centroids, r)

Arguments

`inmat`	An input matrix with features as rows and samples as columns
`centroids`	A character vector indicating the centroids
`r`	A numeric correlation threshold

Value

A matrix describing which edges were significant in the input matrix matrix according to the r correlation threshold provided

Fisher integration of p-values

Description

This function applies the Fisher integration of pvalues

Usage

fisherp(ps)
fisherp(ps)

Arguments

`ps`	a vector of p-values

Value

p.val an integrated p-value

Examples

ps<-c(0.01,0.05,0.03,0.2)
fisherp(ps)
ps<-c(0.01,0.05,0.03,0.2)
fisherp(ps)

GSEA

Description

This function performs Gene Set Enrichment Analysis

Usage

gsea(
  reflist,
  set,
  method = c("permutation", "pareto"),
  np = 1000,
  w = 1,
  gsea_null = NULL
)
gsea(
  reflist,
  set,
  method = c("permutation", "pareto"),
  np = 1000,
  w = 1,
  gsea_null = NULL
)

Arguments

`reflist`	named vector of reference scores
`set`	element set
`method`	one of 'permutation' or 'pareto'
`np`	Number of permutations (Default: 1000)
`w`	exponent used to raise the supplied scores. Default is 1 (original scores unchanged)
`gsea_null`	a GSEA null distribution (Optional)

Value

A GSEA object. Basically a list of s components:

ES: The enrichment score
NES: The normalized enrichment socre
ledge: The items in the leading edge
p.value: The permutation-based p-value

Examples

reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set<-paste0('gene',sample(1:200,50))
obj<-gsea(reflist,set,method='pareto',np=1000)
obj$p.value
reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set<-paste0('gene',sample(1:200,50))
obj<-gsea(reflist,set,method='pareto',np=1000)
obj$p.value

2-way GSEA GSEA Gene set enrichment analysis of two complementary gene sets using gsea

Description

2-way GSEA GSEA Gene set enrichment analysis of two complementary gene sets using gsea

Usage

gsea2(
  reflist,
  set1,
  set2,
  method = c("permutation", "pareto"),
  np = 1000,
  w = 1,
  gsea_null = NULL
)
gsea2(
  reflist,
  set1,
  set2,
  method = c("permutation", "pareto"),
  np = 1000,
  w = 1,
  gsea_null = NULL
)

Arguments

`reflist`	named vector of reference scores
`set1`	element set 1
`set2`	element set 1
`method`	one of 'permutation' or 'pareto'
`np`	Number of permutations (Default: 1000)
`w`	exponent used to raise the supplied scores. Default is 1 (original scores unchanged)
`gsea_null`	a GSEA null distribution (Optional)

Value

A list of 2 GSEA objects. Each of which is a list of components:

ES: The enrichment score
NES: The normalized enrichment socre
ledge: The items in the leading edge
p.value: The permutation-based p-value

Examples

reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set1<-paste0('gene',sample(1:200,50))
set2<-paste0('gene',sample(801:1000,50))
obj<-gsea2(reflist,set1,set2,method='pareto',np=1000)
obj$p.value
reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set1<-paste0('gene',sample(1:200,50))
set2<-paste0('gene',sample(801:1000,50))
obj<-gsea2(reflist,set1,set2,method='pareto',np=1000)
obj$p.value

kmgformat - Nice Formatting of Numbers

Description

This function will convert thousand numbers to K, millions to M, billions to G, trillions to T, quadrillions to P

Usage

kmgformat(input, roundParam = 1)
kmgformat(input, roundParam = 1)

Arguments

`input`	A vector of values
`roundParam`	How many decimal digits you want

Value

A character vector of formatted numebr names

Examples

# Thousands
set.seed(1)
a<-runif(1000,0,1e4)
plot(a,yaxt='n')
kmg<-kmgformat(pretty(a))
axis(2,at=pretty(a),labels=kmg)

# Millions to Billions
set.seed(1)
a<-runif(1000,0,1e9)
plot(a,yaxt='n',pch=20,col="black")
kmg<-kmgformat(pretty(a))
axis(2,at=pretty(a),labels=kmg)
# Thousands
set.seed(1)
a<-runif(1000,0,1e4)
plot(a,yaxt='n')
kmg<-kmgformat(pretty(a))
axis(2,at=pretty(a),labels=kmg)

# Millions to Billions
set.seed(1)
a<-runif(1000,0,1e9)
plot(a,yaxt='n',pch=20,col="black")
kmg<-kmgformat(pretty(a))
axis(2,at=pretty(a),labels=kmg)

Perform Master Regulator Analysis (mra).

Description

The analysis is performed between two groups of samples in the form of expression matrices, with genes/features as rows and samples as columns.

Usage

mra(
  expmat1,
  expmat2 = NULL,
  regulon,
  minsize = 10,
  nperm = NULL,
  nthreads = 2,
  verbose = FALSE,
  atacseq = NULL
)
mra(
  expmat1,
  expmat2 = NULL,
  regulon,
  minsize = 10,
  nperm = NULL,
  nthreads = 2,
  verbose = FALSE,
  atacseq = NULL
)

Arguments

`expmat1`	A numeric expression matrix, with genes/features as rows and samples as columns. If only expmat1 is provided (without expmat2), the function will perform a sample-by-sample master regulator analysis, with the mean of the dataset as a reference. If expmat2 is provided, expmat1 will be considered the "treatment" sample set. If a named vector is provided, with names as genes/features and values as signature values (e.g. T-test statistics), signature master regulator analysis is performed.
`expmat2`	A numeric expression matrix, with genes/features as rows and samples as columns. If provided, it will be considered as the "control" or "reference" sample set for expmat1.
`regulon`	A _regulon_ object, output of the _corto_ function.
`minsize`	A minimum network size for each centroid/TF to be analyzed. Default is 10.
`nperm`	The number of times the input data will be permuted to generate null signatures. Default is 1000 if expmat2 is provided, and 10 if expmat2 is not provided (single sample mra).
`nthreads`	The number of threads to use for generating null signatures. Default is 1
`verbose`	Boolean, whether to print full messages on progress analysis. Default is FALSE
`atacseq`	An optional 3 column matrix derived from an ATAC-Seq analysis, indicating 1) gene symbol, 2) -log10(FDR)*sing(log2FC) of an ATAC-Seq design, 3) distance from TSS. If provided, the output will contain an _atacseq_ field.

Value

A list summarizing the master regulator analysis

nes: the normalized enrichment score: positive if the centroid/TF network is upregulated in expmat1 vs expmat2 (or in expmat1 vs the mean of the dataset), negative if downregulated. A vector in multisample mode, a matrix in sample-by-sample mode.
pvalue: the pvalue of the enrichment.
sig: the calculated signature (useful for plotting).
regulon: the original regulon used in the analysis (but filtered for _minsize_)
atac: Optionally present if atacseq data is provided. For each centroid/TF a number ranging from 0 to 1 will indicate the fraction of changes in activity due to promoter effects rather than distal effects.

Plot a master regulator analysis

Description

Plotting function for master regulator analysis performed by the _mra_ function

Usage

mraplot(
  mraobj,
  mrs = 5,
  title = "corto - Master Regulator Analysis",
  pthr = 0.01
)
mraplot(
  mraobj,
  mrs = 5,
  title = "corto - Master Regulator Analysis",
  pthr = 0.01
)

Arguments

`mraobj`	The input object, output of the function mra
`mrs`	Either a numeric value indicating how many MRs to show, sorted by significance, or a character vector specifying which TFs to show. Default is 5
`title`	Title of the plot (optional, default is "corto - Master Regulator Analysis")
`pthr`	The p-value at which the MR is considered significant. Default is 0.01

Value

A plot is generated

p2r Convert a P-value to the corresponding Correlation Coefficient

Description

p2r Convert a P-value to the corresponding Correlation Coefficient

Usage

p2r(p, n)
p2r(p, n)

Arguments

`p`	the p-value
`n`	the number of samples

Value

a correlation coefficient

Examples

p2r(p=0.08,n=20)
p2r(p=0.08,n=20)

p2z

Description

This function gives a gaussian Z-score corresponding to the provided p-value Careful: sign is not provided

Usage

p2z(p)
p2z(p)

Arguments

p

a p-value

Value

z a Z score

Examples

p<-0.05
p2z(p)
p<-0.05
p2z(p)

Plot GSEA results

Description

This function generates a GSEA plot from a gsea object

Usage

plot_gsea(
  gsea.obj,
  twoColors = c("red", "blue"),
  plotNames = FALSE,
  colBarcode = "black",
  title = "Running Enrichment Score",
  bottomTitle = "List Values",
  bottomYlabel = "Signature values",
  ext_nes = NULL,
  ext_pvalue = NULL,
  ext_es = NULL,
  omit_middle = FALSE
)
plot_gsea(
  gsea.obj,
  twoColors = c("red", "blue"),
  plotNames = FALSE,
  colBarcode = "black",
  title = "Running Enrichment Score",
  bottomTitle = "List Values",
  bottomYlabel = "Signature values",
  ext_nes = NULL,
  ext_pvalue = NULL,
  ext_es = NULL,
  omit_middle = FALSE
)

Arguments

`gsea.obj`	GSEA object produced by the `gsea` function
`twoColors`	the two colors to use for positive[1] and negative[2] enrichment scores
`plotNames`	Logical. Should the set names be plotted?
`colBarcode`	The color of the barcode
`title`	String to be plotted above the Running Enrichment Score
`bottomTitle`	String for the title of the bottom part of the plot
`bottomYlabel`	String for the Y label of the bottom plot
`ext_nes`	Provide a NES from an external calculation
`ext_pvalue`	Provide a pvalue from an external calculation
`ext_es`	Provide an ES from an external calculation
`omit_middle`	If TRUE, will not plot the running score (FALSE by default)

Value

Nothing, a plot is generated in the default output device

Examples

reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set<-paste0('gene',sample(1:200,50))
obj<-gsea(reflist,set,method='pareto',np=1000)
plot_gsea(obj)
reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set<-paste0('gene',sample(1:200,50))
obj<-gsea(reflist,set,method='pareto',np=1000)
plot_gsea(obj)

Plot 2-way GSEA results

Description

This function generates a GSEA plot from a gsea object

Usage

plot_gsea2(
  gsea.obj,
  twoColors = c("red", "blue"),
  plotNames = FALSE,
  title = "Running Enrichment Score",
  bottomTitle = "List Values",
  bottomYlabel = "Signature values"
)
plot_gsea2(
  gsea.obj,
  twoColors = c("red", "blue"),
  plotNames = FALSE,
  title = "Running Enrichment Score",
  bottomTitle = "List Values",
  bottomYlabel = "Signature values"
)

Arguments

`gsea.obj`	GSEA object produced by the `gsea` function
`twoColors`	the two colors to use for positive[1] and negative[2] enrichment scores, and of the barcodes
`plotNames`	Logical. Should the set names be plotted?
`title`	String to be plotted above the Running Enrichment Score
`bottomTitle`	String for the title of the bottom part of the plot
`bottomYlabel`	String for the Y label of the bottom plot (FALSE by default)

Value

Nothing, a plot is generated in the default output device

Examples

reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set1<-paste0('gene',sample(1:200,50))
set2<-paste0('gene',sample(801:1000,50))
obj<-gsea2(reflist,set1,set2,method='pareto',np=1000)
plot_gsea2(obj)
reflist<-setNames(-sort(rnorm(1000)),paste0('gene',1:1000))
set1<-paste0('gene',sample(1:200,50))
set2<-paste0('gene',sample(801:1000,50))
obj<-gsea2(reflist,set1,set2,method='pareto',np=1000)
plot_gsea2(obj)

r2p Convert Correlation Coefficient to P-value

Description

r2p Convert Correlation Coefficient to P-value

Usage

r2p(r, n)
r2p(r, n)

Arguments

`r`	the correlation coefficient
`n`	the number of samples

Value

a numeric p-value

Examples

r2p(r=0.4,n=20) # 0.08
r2p(r=0.4,n=20) # 0.08

scatter - XY scatter plot with extra information

Description

This function will plot two variables (based on their common names), calculate their Coefficient of Correlation (CC), plot a linear regression line and color the background if the correlation is positive (red), negative (blue) or non-significant (white)

Usage

scatter(
  x,
  y,
  method = "pearson",
  threshold = 0.01,
  showLine = TRUE,
  grid = TRUE,
  bgcol = FALSE,
  pch = 20,
  subtitle = NULL,
  extendXlim = FALSE,
  ci = FALSE,
  ...
)
scatter(
  x,
  y,
  method = "pearson",
  threshold = 0.01,
  showLine = TRUE,
  grid = TRUE,
  bgcol = FALSE,
  pch = 20,
  subtitle = NULL,
  extendXlim = FALSE,
  ci = FALSE,
  ...
)

Arguments

`x`	The first named vector
`y`	The second named vector
`method`	a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
`threshold`	a numeric value indicating the significance threshold (p-value) of the correlation, in order to show a colored background. Default is 0.01.
`showLine`	a boolean indicating if a linear regression line should be plotted. Default is TRUE
`grid`	a boolean indicating whether to show a plot grid. Default is TRUE
`bgcol`	Boolean. Should a background coloring associated to significance and sign of correlation be used? Default is TRUE, and it will color the background in red if the correlation coefficient is positive, in blue if negative, in white if not significant (accordin to the _threshold_ parameter)
`pch`	the _pch_ parameter indicating the points shape. Default is 20
`subtitle`	NULL by default, in which case the function will print as a subtitle the correlation coefficient (CC) and its pvalue. Otherwise, a user-provided string, bypassing the predefined subtitle
`extendXlim`	logical. If TRUE, the x-axis limits are extended by a fraction (useful for labeling points on the margins of the plot area). Default is FALSE
`ci`	logical. If TRUE, confidence intervals of linear regression are shown at 95 percent confidence.
`...`	Arguments to be passed to the core _plot_ function (if a new plot is created)

Value

A plot

Examples

x<-setNames(rnorm(200),paste0("var",1:200))
y<-setNames(rnorm(210),paste0("var",11:220))
scatter(x,y,xlab="Variable x",ylab="Variable y",main="Scatter plot by corto package",ci=TRUE)
x<-setNames(rnorm(200),paste0("var",1:200))
y<-setNames(rnorm(210),paste0("var",11:220))
scatter(x,y,xlab="Variable x",ylab="Variable y",main="Scatter plot by corto package",ci=TRUE)

scinot - Convert a number to a scientific notation expression

Description

This function will convert any numeric vector

Usage

scinot(v, digits = 3)
scinot(v, digits = 3)

Arguments

`v`	The input numeric object. It can be a single value or a vector
`digits`	An integer indicating how many significant digits to show. Default is 3.

Value

An object of class _expression_.

Examples

# Usage on single value
scinot(0.00000543)
# Demonstration on a vector
numbers<-c(3.456e-12,0.00901,5670000,-3.16e18,0.000004522,rnorm(5,sd=0.0000001))
plot(0,xlim=c(0,10),ylim=c(0,10),type="n")
text(c(2,6),c(10,10),labels=c("Before","After"),font=2)
for(i in 10:1){
    text(c(2,6),c(i-1,i-1),labels=c(numbers[i],scinot(numbers)[i]))
}
# Usage on single value
scinot(0.00000543)
# Demonstration on a vector
numbers<-c(3.456e-12,0.00901,5670000,-3.16e18,0.000004522,rnorm(5,sd=0.0000001))
plot(0,xlim=c(0,10),ylim=c(0,10),type="n")
text(c(2,6),c(10,10),labels=c("Before","After"),font=2)
for(i in 10:1){
    text(c(2,6),c(i-1,i-1),labels=c(numbers[i],scinot(numbers)[i]))
}

Slice

Description

This function prints a slice of a matrix

Usage

slice(matrix)
slice(matrix)

Arguments

matrix

A matrix

Value

A visualization of the first 5 rows and columns of the input matrix

Examples

set.seed(1)
example<-matrix(rnorm(1000),nrow=100,ncol=10)
slice(example)
set.seed(1)
example<-matrix(rnorm(1000),nrow=100,ncol=10)
slice(example)

ssGSEA

Description

This function performs single sample GSEA

Usage

ssgsea(inmat, groups, scale = TRUE, minsize = 10)
ssgsea(inmat, groups, scale = TRUE, minsize = 10)

Arguments

`inmat`	A numeric matrix, with rownames/rows as genes or features, and colnames/columns as sample names
`groups`	a named list. Names are names of the groups (e.g. pathways) and elements are character vectors indicating gene or feature names (that should match, at least partially, with the rownames of inmat)
`scale`	Boolean. Wheter the matrix should be row-scaled.
`minsize`	Numeric. Include only groups with at least this many elements Default is 10

Value

A matrix of Normalized Enrichment Scores (NES), which can be converted to p-values using the function _corto::z2p_

Examples

# A random matrix
set.seed(1)
inmat<-matrix(rnorm(200*50),nrow=200,ncol=50)
rownames(inmat)<-paste0("gene",1:nrow(inmat))
# A random list of groups
groups<-list()
for(i in 1:10){
    somegenes<-sample(rownames(inmat),30)
    groups[[paste0("pathway_",i)]]<-somegenes
}
# Run ssGSEA
nesmat<-ssgsea(inmat,groups)

# A random matrix
set.seed(1)
inmat<-matrix(rnorm(200*50),nrow=200,ncol=50)
rownames(inmat)<-paste0("gene",1:nrow(inmat))
# A random list of groups
groups<-list()
for(i in 1:10){
    somegenes<-sample(rownames(inmat),30)
    groups[[paste0("pathway_",i)]]<-somegenes
}
# Run ssGSEA
nesmat<-ssgsea(inmat,groups)

Stouffer integration of Z scores

Description

This function gives a gaussian Z-score corresponding to the provided p-value Careful: sign is not provided

Usage

stouffer(x)
stouffer(x)

Arguments

`x`	a vector of Z scores

Value

Z an integrated Z score

Examples

zs<-c(1,3,5,2,3)
stouffer(zs)
zs<-c(1,3,5,2,3)
stouffer(zs)

textrepel - Plot text with non-overlapping labels

Description

This function plots text with x and y coordinates, forcing overlapping labels to not overlap

Usage

textrepel(
  x,
  y,
  labels = NULL,
  padding = " ",
  rstep = 0.1,
  tstep = 0.1,
  vertical = FALSE,
  textSize = 1,
  showLines = TRUE,
  lineColor = "#00000066",
  lineWidth = 2,
  showPoints = TRUE,
  pointColor = "#00000033",
  pointSize = 2,
  pointPch = 16,
  add = FALSE,
  ...
)
textrepel(
  x,
  y,
  labels = NULL,
  padding = " ",
  rstep = 0.1,
  tstep = 0.1,
  vertical = FALSE,
  textSize = 1,
  showLines = TRUE,
  lineColor = "#00000066",
  lineWidth = 2,
  showPoints = TRUE,
  pointColor = "#00000033",
  pointSize = 2,
  pointPch = 16,
  add = FALSE,
  ...
)

Arguments

`x`	A numeric vector of x coordinates
`y`	A numeric vector of y coordinates (must have the same length of x)
`labels`	A vector of labels associated with x and y (must have the same length of x)
`padding`	A character object specifying left and right padding for words. Default is a single whitespace " "
`rstep`	Decimal numeric specifying the lateral step length for label distancing. Default is 0.1
`tstep`	Decimal numeric specifying the theta step length for label distancing. Default is 0.1
`vertical`	Boolean. If FALSE (default), the labels are plotted horizontally. If TRUE, vertically
`textSize`	Numeric. Size of text. Default is 1
`showLines`	Boolean. Whether to show lines connecting displaced labels to their original plot. Default is TRUE
`lineColor`	String indicating the color of the connecting line
`lineWidth`	Numeric indicating the width of the connecting line
`showPoints`	Boolean. Whether to show points over original x-y coordinates
`pointColor`	String indicating the color of the point
`pointSize`	Numeric indicating the size of the point
`pointPch`	Integer applying to shape of points. Default is 16 (filled circle)
`add`	Boolean. If FALSE (default), a new plot is generated. If TRUE, the textrepel labels are plotted over the existing plot
`...`	Arguments to be passed to the core _plot_ function

Value

A plot

Examples

# Simple example, generating a new plot, taking care of some overlapping labels
set.seed(1)
x<-rnorm(100)
y<-abs(x)+rnorm(100)
names(x)<-names(y)<-paste0("OBJ",1:length(x))
labels<-names(x)
textrepel(x,y,labels)
# More advanced example, adding textrepel over an existing plot
set.seed(1)
x<-rnorm(1000)
y<-abs(x)+rnorm(1000)
names(x)<-names(y)<-paste0("GENE",1:length(x))
labels<-names(x)
plot(x,y,pch=16,col="#00000066",xlim=1.3*c(min(x),max(x)))
subset1<-which(x<(-2.2))
textrepel(x[subset1],y[subset1],labels[subset1],add=TRUE,pointCol="cornflowerblue")
subset2<-which(x>(+2.2))
textrepel(x[subset2],y[subset2],labels[subset2],add=TRUE,pointCol="salmon")
# Simple example, generating a new plot, taking care of some overlapping labels
set.seed(1)
x<-rnorm(100)
y<-abs(x)+rnorm(100)
names(x)<-names(y)<-paste0("OBJ",1:length(x))
labels<-names(x)
textrepel(x,y,labels)
# More advanced example, adding textrepel over an existing plot
set.seed(1)
x<-rnorm(1000)
y<-abs(x)+rnorm(1000)
names(x)<-names(y)<-paste0("GENE",1:length(x))
labels<-names(x)
plot(x,y,pch=16,col="#00000066",xlim=1.3*c(min(x),max(x)))
subset1<-which(x<(-2.2))
textrepel(x[subset1],y[subset1],labels[subset1],add=TRUE,pointCol="cornflowerblue")
subset2<-which(x>(+2.2))
textrepel(x[subset2],y[subset2],labels[subset2],add=TRUE,pointCol="salmon")

val2col - Convert a numeric vector into colors

Description

val2col - Convert a numeric vector into colors

Usage

val2col(
  z,
  col1 = "navy",
  col2 = "white",
  col3 = "red3",
  nbreaks = 1000,
  center = TRUE,
  rank = FALSE
)
val2col(
  z,
  col1 = "navy",
  col2 = "white",
  col3 = "red3",
  nbreaks = 1000,
  center = TRUE,
  rank = FALSE
)

Arguments

`z`	a vector of numbers
`col1`	a color name for the min value, default 'navy'
`col2`	a color name for the middle value, default 'white'
`col3`	a color name for the max value, default 'red3'
`nbreaks`	Number of colors to be generated. Default is 30.
`center`	boolean, should the data be centered? Default is TRUE
`rank`	boolean, should the data be ranked? Default is FALSE

Value

a vector of colors

Examples

a<-rnorm(1000)
cols<-val2col(a)
plot(a,col=cols,pch=16)
a<-rnorm(1000)
cols<-val2col(a)
plot(a,col=cols,pch=16)

Weighted Stouffer integration of Z scores

Description

This function gives a gaussian Z-score corresponding to the provided p-value Careful: sign is not provided

Usage

wstouffer(x, w)
wstouffer(x, w)

Arguments

`x`	a vector of Z scores
`w`	weight for each Z score

Value

Z an integrated Z score

Examples

zs<-c(1,-3,5,2,3)
ws<-c(1,10,1,2,1)
wstouffer(zs,ws)
zs<-c(1,-3,5,2,3)
ws<-c(1,10,1,2,1)
wstouffer(zs,ws)

z2p

Description

This function gives a gaussian p-value corresponding to the provided Z-score

Usage

z2p(z)
z2p(z)

Arguments

z

a Z score

Value

a p-value

Examples

z<-1.96
z2p(z)
z<-1.96
z2p(z)

Package 'corto'

Help Index

barplot2 - Bar plot with upper error bars

Description

Usage

Arguments

Value

Examples

Calculate a regulon from a data matrix

Description

Usage

Arguments

Value

Examples

A fast correlation function

Description

Usage

Arguments

Value

Fisher integration of p-values

Description

Usage

Arguments

Value

Examples

GSEA

Description

Usage

Arguments

Value

Examples

2-way GSEA GSEA Gene set enrichment analysis of two complementary gene sets using gsea

Description

Usage

Arguments

Value

Examples

kmgformat - Nice Formatting of Numbers

Description

Usage

Arguments

Value

Examples

Perform Master Regulator Analysis (mra).

Description

Usage

Arguments

Value

Plot a master regulator analysis

Description

Usage

Arguments

Value

p2r Convert a P-value to the corresponding Correlation Coefficient

Description

Usage

Arguments

Value

Examples

p2z

Description

Usage

Arguments

Value

Examples

Plot GSEA results

Description

Usage

Arguments

Value

Examples

Plot 2-way GSEA results

Description

Usage

Arguments

Value

Examples

r2p Convert Correlation Coefficient to P-value

Description

Usage