barplot2 - Bar plot with upper error bars

Description

barplot2 - Bar plot with upper error bars

Usage

barplot2(values, errors, lower = FALSE, flat = TRUE, ...)

Arguments

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")

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
)

Arguments

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)

A fast correlation function

Description

A fast correlation function

Usage

fcor(inmat, centroids, r)

Arguments

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)

Arguments

Value

p.val an integrated p-value

Examples

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
)

Arguments

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

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
)

Arguments

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

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)

Arguments

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)

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
)

Arguments

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
)

Arguments

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)

Arguments

Value

a correlation coefficient

Examples

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)

Arguments

Value

z a Z score

Examples

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
)

Arguments

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)

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",
  legside1 = NULL,
  legside2 = NULL
)

Arguments

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)

r2p Convert Correlation Coefficient to P-value

Description

r2p Convert Correlation Coefficient to P-value

Usage

r2p(r, n)

Arguments

Value

a numeric p-value

Examples

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,
  ...
)

Arguments

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)

scinot - Convert a number to a scientific notation expression

Description

This function will convert any numeric vector

Usage

scinot(v, digits = 3)

Arguments

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]))
}

Slice

Description

This function prints a slice of a matrix

Usage

slice(matrix)

Arguments

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)

ssGSEA

Description

This function performs single sample GSEA

Usage

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

Arguments

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)

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)

Arguments

Value

Z an integrated Z score

Examples

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,
  ...
)

Arguments

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")

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
)

Arguments

Value

a vector of colors

Examples

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)

Arguments

Value

Z an integrated Z score

Examples

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)

Arguments

Value

a p-value

Examples

z<-1.96
z2p(z)