Package 'MVisAGe'

DNA copy number data from 98 head and neck squamous cell carcinoma (HNSC) patients

Description

Quantitative gene-level DNA copy number measurements for 98 samples from The Cancer Genome Atlas (TCGA) HNSC cohort. The all_data_by_genes.txt dataset from the GISTIC2 output was restricted to the first 100 columns and genes that lie on chromosomes 11 and 12. Genes appear in rows; samples appear in columns (other than the first two columns described below). Gene symbols are used as row names and sample identifiers are used as column names (other than the first two columns).

Usage

cn.mat

Format

A matrix with 2719 rows and 100 columns

Locus.ID

gene identifier

Cytoband

cytoband containing the gene of interest

Remaining Columns

quantitative DNA copy number

Column names

sample identifiers (other than the first two columns)

Row names

gene symbols

Source

https://gdac.broadinstitute.org/

---

A Function for Creating a Heatmap of DNA Copy Number Data

Description

This function creates a heatmap of DNA copy number data for a given chromosomal region.

Usage

cn.region.heatmap(cn.mat, gene.annot, plot.chr, plot.start, plot.stop,
  plot.list, sample.annot = NULL, sample.cluster = F, low.thresh = -2,
  high.thresh = 2, num.cols = 50, collist = c("blue", "white", "red"),
  annot.colors = c("black", "red", "green", "blue", "cyan"),
  plot.sample.annot = F, cytoband.colors = c("gray90", "gray60"))

Arguments

cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). DNA methylation data can also be used. Both row names (gene names) and column names (Sample IDs) must be given.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband.
plot.chr	The chromosome used to define the region of interest.
plot.start	The genomic position (in base pairs) where the region starts.
plot.stop	The genomic position (in base pairs) where the region stops.
plot.list	A list produced by corr.list.compute().
sample.annot	An optional two-column matrix of sample annotation data. Column 1 = sample IDs, Column 2 = sample annotation (e.g. tumor vs. normal). If NULL, sample annot will be created using the common sample IDs and a single group ('1'). Default = NULL.
sample.cluster	Logical values indicating whether the samples should be clustered. Default = FALSE.
low.thresh	Lower threshold for DNA copy number measurements. All values less than low.thresh are set equal to low.thresh. Default = -2.
high.thresh	Upper threshold for DNA copy number measurements. All values greater than high.thresh are set equal to high.thresh. Default = 2.
num.cols	Number of distinct colors in the heatmap. Default = 50.
collist	Color scheme for displaying copy number values. Default = ("blue", "white", "red").
annot.colors	Character vector used to define the color scheme for sample annotation. Default = c("black", "red", "green", "blue", "cyan").
plot.sample.annot	Logical value used to specify whether the sample annotation information should be plotted. Default = FALSE.
cytoband.colors	Character vector of length two used to define the color scheme for annotating the cytoband. Default = c("gray90", "gray60").

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 output.list = corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

 cn.region.heatmap(cn.mat = pd.cn, gene.annot = pd.ga, plot.chr = 11,

plot.start = 0e6, plot.stop = 135e6, sample.annot = pd.sa, plot.list = output.list)

---

A Function for Computing a Vector of Pearson Correlation Coefficients

Description

This function computes Pearson correlation coefficients on a row-by-row basis for two numerical input matrices of the same size.

Usage

corr.compute(exp.mat, cn.mat, gene.annot, method = "pearson", digits = 5,
  alternative = "greater")

Arguments

exp.mat	A matrix of gene-level expression data (rows = genes, columns = samples). Missing values are not permitted.
cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). Both genes and samples should appear in the same order as exp.mat. Missing values are not permitted.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband. Genes should appear in the same order as exp.mat and cn.mat.
method	A character string (either "pearson" or "spearman") specifying the method used to calculate the correlation coefficient (default = "pearson").
digits	Used with signif() to specify the number of significant digits (default = 5).
alternative	A character string ("greater" or "less") that specifies the direction of the alternative hypothesis, either rho > 0 or rho < 0 (default = "greater").

Value

Returns a eight-column matrix. The first three columns are the same as gene.annot. The fourth column contains gene-specific Pearson or Spearman correlation coefficients based on the entries in each row of exp.mat and cn.mat, respectively (column name = "R"). The fifth column contains squared Pearson correlation coefficients (column name = "R^2"). The sixth column contains t statistics corresponding to the correlation coefficients (column name = "tStat"). The seventh column contains the right-tailed p-value based on the t statistic (column name = "pValue"). The eighth column contains Benjamini-Hochberg q-values corresponding to the p-values. Genes with constant gene expression or DNA copy number are removed because they have zero variance.

Examples

corr.results = exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 corr.compute(prepped.data[["exp"]], prepped.data[["cn"]], prepped.data[["gene.annot"]])

---

A Function for Creating a List of Pearson Correlation Coefficients

Description

This function uses the corr.compute() function to compute gene-specific Pearson correlation coefficients in each group of samples defined in a sample annotation matrix.

Usage

corr.list.compute(exp.mat, cn.mat, gene.annot, sample.annot = NULL,
  method = "pearson", digits = 5, alternative = "greater")

Arguments

exp.mat	A matrix of gene-level expression data (rows = genes, columns = samples). Missing values are not permitted.
cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). Both genes and samples should appear in the same order as exp.mat. Missing values are not permitted.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband. Genes should appear in the same order as exp.mat and cn.mat.
sample.annot	An optional two-column matrix of sample annotation data. Column 1 = sample IDs, Column 2 = sample annotation (e.g. tumor vs. normal). If NULL, sample annot will be created using the common sample IDs and a single group ('1'). Default = NULL.
method	A character string (either "pearson" or "spearman") specifying the method used to calculate the correlation coefficient (default = "pearson").
digits	Used with signif() to specify the number of significant digits (default = 5).
alternative	A character string ("greater" or "less") that specifies the direction of the alternative hypothesis, either rho > 0 or rho < 0 (default = "greater").

Value

Returns a list whose length is the number of unique groups defined by sample.annot. Each entry in the list is the output of corr.compute.

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

---

A Function for Preparing mRNAseq and Copy Number Data Matrices

Description

This function prepares mRNAseq and copy number data matrices for use in other mVisAGe functions.

Usage

data.prep(exp.mat, cn.mat, gene.annot, sample.annot = NULL, log.exp = FALSE,
  gene.list = NULL)

Arguments

exp.mat	A matrix of gene-level expression data (rows = genes, columns = samples). Both row names (gene names) and column names (sample IDs) must be given.
cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). DNA methylation data can also be used. Both row names (gene names) and column names (Sample IDs) must be given.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband.
sample.annot	An optional two-column matrix of sample annotation data. Column 1 = sample IDs, Column 2 = categorical sample annotation (e.g. tumor vs. normal). If NULL, sample annot will be created using the common sample IDs and a single group ('1'). Default = NULL.
log.exp	A logical value indicating whether or not the expression values have been log-transformed. Default = FALSE.
gene.list	Used to restrict the output to a set of genes of interest, e.g. genes identified by GISTIC as having recurrent copy number alterations. Default = NULL, and in this case all genes are used.

Value

Returns a list with four components: cn, exp, gene.annot, and sample.annot. Each of cn, exp, and gene.annot have been restricted to a common set of genes, and these appear in the same order. Similarly, cn, exp, and sample.annot have been restricted to a common set of subjects that appear in the same order.

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

---

Gene expression data from 100 head and neck squamous cell carcinoma (HNSC) patients

Description

RSEM gene expression measurements for 100 samples from The Cancer Genome Atlas (TCGA) HNSC cohort after restricting to genes that lie in chromosomes 11 and 12. Genes appear in rows; samples appear in columns (other than the first two columns described below). Gene symbols are used as row names and sample identifiers are used as column names (other than the first two columns).

Usage

exp.mat

Format

A matrix with 2161 rows and 100 columns

Columns

RSEM gene expression measurements

Column names

sample identifiers

Row names

gene symbols

Source

https://gdc.cancer.gov/

---

Gene annotation data (hg38)

Description

Genomic position and cytoband annotation data.

Usage

gene.annot

Format

A matrix with 26417 rows and 3 columns

chr

chromosome containing the gene of interest

pos

genomic position (base pairs) for the gene of interest

cytoband

cytoband containing the gene of interest

Row names

gene symbols

Source

https://usegalaxy.org/

---

A Function for Computing a Vector of Pearson Correlation Coefficients

Description

This function computes Pearson correlation coefficients on a row-by-row basis for two numerical input matrices of the same size.

Usage

perm.significance(exp.mat, cn.mat, gene.annot, method = "pearson",
  digits = 5, num.perms = 100, random.seed = NULL,
  alternative = "greater")

Arguments

exp.mat	A matrix of gene-level expression data (rows = genes, columns = samples). Missing values are not permitted.
cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). Both genes and samples should appear in the same order as exp.mat. Missing values are not permitted.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband. Genes should appear in the same order as exp.mat and cn.mat.
method	A character string (either "pearson" or "spearman") specifying the method used to calculate the correlation coefficient (default = "pearson").
digits	Used with signif() to specify the number of significant digits (default = 5).
num.perms	Number of permutations used to assess significance (default = 1e2).
random.seed	Random seed (default = NULL).
alternative	A character string ("greater" or "less") that specifies the direction of the alternative hypothesis, either rho > 0 or rho < 0 (default = "greater").

Value

Returns a five-column matrix. The first three columns are the same as gene.annot. The fourth column contains gene-specific Pearson or Spearman correlation coefficients based on the entries in each row of exp.mat and cn.mat, respectively (column name = "R"). The fifth column contains squared Pearson correlation coefficients (column name = "R^2"). The sixth column contains the permutation-based right-tailed p-value of the correlation coefficient (column name = "perm_pValue"). The seventh column contains Benjamini-Hochberg q-values corresponding to the p-values. Genes with constant gene expression or DNA copy number are removed because they have zero variance.

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 perm.significance(prepped.data[["exp"]], prepped.data[["cn"]], prepped.data[["gene.annot"]])

---

A Function for Creating a List of Pearson Correlation Coefficients

Description

This function uses the corr.compute() function to compute gene-specific Pearson correlation coefficients in each group of samples defined in a sample annotation matrix.

Usage

perm.significance.list.compute(exp.mat, cn.mat, gene.annot,
  sample.annot = NULL, method = "pearson", digits = 5, num.perms = 100,
  random.seed = NULL, alternative = "greater")

Arguments

exp.mat	A matrix of gene-level expression data (rows = genes, columns = samples). Missing values are not permitted.
cn.mat	A matrix of gene-level DNA copy number data (rows = genes, columns = samples). Both genes and samples should appear in the same order as exp.mat. Missing values are not permitted.
gene.annot	A three-column matrix containing gene position information. Column 1 = chromosome number written in the form 'chr1' (note that chrX and chrY should be written chr23 and chr24), Column 2 = position (in base pairs), Column 3 = cytoband. Genes should appear in the same order as exp.mat and cn.mat.
sample.annot	An optional two-column matrix of sample annotation data. Column 1 = sample IDs, Column 2 = sample annotation (e.g. tumor vs. normal). If NULL, sample annot will be created using the common sample IDs and a single group ('1'). Default = NULL.
method	A character string (either "pearson" or "spearman") specifying the method used to calculate the correlation coefficient (default = "pearson").
digits	Used with signif() to specify the number of significant digits (default = 5).
num.perms	Number of permutations used to assess significance (default = 1e2).
random.seed	Random seed (default = NULL).
alternative	A character string ("greater" or "less") that specifies the direction of the alternative hypothesis, either rho > 0 or rho < 0 (default = "greater").

Value

Returns a list whose length is the number of unique groups defined by sample.annot. Each entry in the list is the output of perm.significance.

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 perm.significance.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

---

Sample annotation data

Description

Human papillomavirus (HPV) infection status for the n = 279 patients with head and neck squamous cell carcinoma in the The Cancer Genoma Atlas cohort.

Usage

sample.annot

Format

A matrix with 26417 rows and 3 columns

Barcode

sample identifier

New.HPV.Status

HPV infection status

Source

http://www.nature.com/nature/journal/v517/n7536/full/nature14129.html

---

A Function for Plotting Smoothed Pearson Correlation Coefficients Across Multiple Chromosomes

Description

This function plots smoothed R or R^2 values produced by corr.list.compute() across multiple chromosomes or genomewide.

Usage

smooth.genome.plot(plot.list, plot.column = "R^2", annot.colors = c("black",
  "red", "green", "blue", "cyan"), vert.pad = 0.05, ylim.low = NULL,
  ylim.high = NULL, plot.legend = TRUE, legend.loc = "bottomright",
  lty.vec = NULL, lwd.vec = NULL, loess.span = 250, expand.size = 50,
  rect.colors = c("light gray", "gray"), chr.label = TRUE,
  xaxis.label = "Chromosome", yaxis.label = NULL, main.label = NULL,
  axis.cex = 1, label.cex = 1, xaxis.line = 1.5, yaxis.line = 2.5,
  main.line = 0, margin.vec = rep(1, 4))

Arguments

plot.list	A list produced by corr.list.compute().
plot.column	"R" or "R^2" depending on whether Pearson correlation coefficients or squared Pearson correlation coefficients will be plotted. Default = "R^2".
annot.colors	A vector of colors used for plotting values in different entries of plot.list. Default = c("black", "red", "green", "blue", "cyan").
vert.pad	Amount of vertical white space in the plot. Default = 0.
ylim.low	Smallest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
ylim.high	Largest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
plot.legend	Logical value specifying whether a legend should be included. Default = FALSE.
legend.loc	Character value specifing the location of the legend. Default = "topright". See See legend.
lty.vec	Vector specifying line types for plotting values in different entries of plot.list. Default = NULL. See par.
lwd.vec	Vector specifying line widths for plotting values in different entries of plot.list. Default = NULL. See par.
loess.span	A numerical value used to control the level of smoothing. Smoothing is performed separately for each chromosome, and loess.span effectively defines the number of genes in each smoothing window. Default = 250.
expand.size	A numerical value used to control smoothing at the ends of chromosomes. Both ends of each chromosome are artificially extended by expand.size genes, smoothing is performed on the expanded chromosome, and then the smoothed values are restricted to the size of the original chromosome. Default = 50.
rect.colors	A character vector of length two that controls the background color for each alternating chromosome. Default = c("light gray", "gray").
chr.label	Logical value specifying whether chromosome numbers should appear on the plot. Default = FALSE.
xaxis.label	Text used to label the x-axis of the plot. Default = "Chromosome". See plot.
yaxis.label	Text used to label the y-axis of the plot. Default = NULL. See plot.
main.label	Text used to label the plot header. Default = NULL. See par.
axis.cex	Numerical value used to specify the font size on the axes. Default = 1. See par.
label.cex	Numerical value used to specify the font size for the axis labels. Default = 1. See par.
xaxis.line	Numerical value used to specify location of xaxis.label. Default = 0. See mtext.
yaxis.line	Numerical value used to specify location of yaxis.label. Default = 0. See mtext.
main.line	Numerical value used to specify location of main.label. Default = 0. See mtext.
margin.vec	Numerical vector specifying margin sizes. Default = rep(1, 4). See par.

Value

Creates a plot of gene-level R or R^2 values produced by corr.list.compute(). Values of R

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 output.list = corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

 smooth.genome.plot(plot.list = output.list, lwd.vec = c(3, 3), lty.vec = c(1, 2))

---

A Function for Plotting Smoothed Pearson Correlation Coefficients Genomewide

Description

This function plots smoothed R or R^2 values produced by corr.list.compute() genomewide.

Usage

smooth.region.plot(plot.list, plot.chr, plot.start, plot.stop,
  plot.column = "R^2", annot.colors = c("black", "red", "green", "blue",
  "cyan"), vert.pad = 0.05, ylim.low = NULL, ylim.high = NULL,
  plot.legend = TRUE, legend.loc = "topleft", lty.vec = NULL,
  lwd.vec = NULL, loess.span = 50, expand.size = 50,
  xaxis.label = "Position (Mb)", yaxis.label = NULL, main.label = NULL,
  axis.cex = 1, label.cex = 1, xaxis.line = 1.5, yaxis.line = 2.5,
  main.line = 0)

Arguments

plot.list	A list produced by corr.list.compute().
plot.chr	The chromosome for which gene-level R or R^2 values will be plotted.
plot.start	The genomic position (in base pairs) where the plot will start.
plot.stop	The genomic position (in base pairs) where the plot will stop.
plot.column	"R" or "R^2" depending on whether Pearson correlation coefficients or squared Pearson correlation coefficients will be plotted. Default = "R^2".
annot.colors	A vector of colors used for plotting values in different entries of plot.list. Default = c("black", "red", "green", "blue", "cyan").
vert.pad	Amount of vertical white space in the plot. Default = 0.
ylim.low	Smallest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
ylim.high	Largest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
plot.legend	Logical value specifying whether a legend should be included. Default = FALSE.
legend.loc	Character value specifing the location of the legend. Default = "topright". See See legend.
lty.vec	Vector specifying line types for plotting values in different entries of plot.list. Default = NULL. See par.
lwd.vec	Vector specifying line widths for plotting values in different entries of plot.list. Default = NULL. See par.
loess.span	A numerical value used to control the level of smoothing. Smoothing is performed separately for each chromosome, and loess.span effectively defines the number of genes in each smoothing window. Default = 250.
expand.size	A numerical value used to control smoothing at the ends of the region of interest. Both ends of the region are artificially extended by expand.size genes, smoothing is performed on the expanded region, and then the smoothed values are restricted to the size of the original region. Default = 50.
xaxis.label	Text used to label the x-axis of the plot. Default = "Chromosome". See plot.
yaxis.label	Text used to label the y-axis of the plot. Default = NULL. See plot.
main.label	Text used to label the plot header. Default = NULL. See plot.
axis.cex	Numerical value used to specify the font size on the axes. Default = 1. See par.
label.cex	Numerical value used to specify the font size for the axis labels. Default = 1. See par.
xaxis.line	Numerical value used to specify location of xaxis.label. Default = 0. See mtext.
yaxis.line	Numerical value used to specify location of yaxis.label. Default = 0. See mtext.
main.line	Numerical value used to specify location of main.label. Default = 0. See mtext.

Value

Creates a plot of gene-level R or R^2 values produced by corr.list.compute().

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 output.list = corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

 smooth.region.plot(plot.list = output.list, plot.chr = 11, plot.start = 0e6, plot.stop = 135e6)

---

A Function for Reformatting TCGA DNA Copy Number Matrices

Description

This function reformats DNA copy number matrices obtained from the Broad Institute's Firehose GDAC (https://gdac.broadinstitute.org/) so they can be used as input for mVisAGe functions.

Usage

tcga.cn.convert(cn.mat)

Arguments

cn.mat

A matrix of DNA copy number data included in the GISTIC2 output. Typically all_data_by_genes.txt, or a subset thereof, including the Locus.ID and Cytoband columns.

Value

A matrix of DNA copy number data (rows = genes, columns = samples) that is suitable for input to mVisAGe functions.

Examples

cn.mat = tcga.cn.convert(cn.mat)

---

A Function for Reformatting TCGA mRNA Expression Matrices

Description

This function reformats mRNA expression matrices obtained from the Broad Institute's Firehose GDAC (https://gdac.broadinstitute.org/) so they can be used as input for mVisAGe functions.

Usage

tcga.exp.convert(exp.mat)

Arguments

exp.mat

A matrix of mRNA expression data. Typically illuminahiseq_rnaseqv2-RSEM_genes_normalized, or a subset thereof, including the header rows.

Value

A matrix of mRNA expression data (rows = genes, columns = samples) that is suitable for input to mVisAGe functions.

Examples

exp.mat = tcga.exp.convert(exp.mat)

---

A Function for Plotting Pearson Correlation Coefficients in a Given Genomic Region

Description

This function plots unsmoothed R or R^2 values produced by corr.list.compute() in a specified genomic region.

Usage

unsmooth.region.plot(plot.list, plot.chr, plot.start, plot.stop,
  plot.column = "R", plot.points = TRUE, plot.lines = TRUE,
  gene.names = TRUE, annot.colors = c("black", "red", "green", "blue",
  "cyan"), vert.pad = 0.05, num.ticks = 5, ylim.low = NULL,
  ylim.high = NULL, pch.vec = NULL, lty.vec = NULL, lwd.vec = NULL,
  plot.legend = TRUE, legend.loc = "topright")

Arguments

plot.list	A list produced by corr.list.compute().
plot.chr	The chromosome for which gene-level R or R^2 values will be plotted.
plot.start	The genomic position (in base pairs) where the plot will start.
plot.stop	The genomic position (in base pairs) where the plot will stop.
plot.column	"R" or "R^2" depending on whether Pearson correlation coefficients or squared Pearson correlation coefficients will be plotted. Default = "R^2".
plot.points	Logical value specifying whether points should be included in the plot. Default = TRUE.
plot.lines	Logical values specifying whether points should be connected by lines. Default = FALSE.
gene.names	Logical value specifying whether gene names should appear on the plot. Default = FALSE.
annot.colors	A vector of colors used for plotting values in different entries of plot.list. Default = c("black", "red", "green", "blue", "cyan").
vert.pad	Amount of vertical white space in the plot. Default = 0.05.
num.ticks	Number of ticks on the x-axis. Default = 5.
ylim.low	Smallest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
ylim.high	Largest value on the y-axis (used to control the range of values on the y-axis). Default = NULL.
pch.vec	Vector specifying point characters for plotting values in different entries of plot.list. Default = NULL. See par.
lty.vec	Vector specifying line types for plotting values in different entries of plot.list. Default = NULL. See par.
lwd.vec	Vector specifying line widths for plotting values in different entries of plot.list. Default = NULL. See par.
plot.legend	Logical value specifying whether a legend should be included. Default = FALSE.
legend.loc	Character value specifing the location of the legend. Default = "topright". See See legend

Value

Creates a plot of gene-level R or R^2 values produced by corr.list.compute().

Examples

exp.mat = tcga.exp.convert(exp.mat)

 cn.mat = tcga.cn.convert(cn.mat)

 prepped.data = data.prep(exp.mat, cn.mat, gene.annot, sample.annot, log.exp = FALSE)

 pd.exp = prepped.data[["exp"]]

 pd.cn = prepped.data[["cn"]]

 pd.ga = prepped.data[["gene.annot"]]

 pd.sa = prepped.data[["sample.annot"]]

 output.list = corr.list.compute(pd.exp, pd.cn, pd.ga, pd.sa)

 unsmooth.region.plot(plot.list = output.list, plot.chr = 11, plot.start = 69e6, plot.stop = 70.5e6)

---