Package 'genomicInstability'

Average length of human and mouse known genes

Description

A dataset containing the average length for known mouse and human genes

Usage

geneLength

Format

Vector of integers indicating the average length in bp for each gene, indicated with EntrezIDs as name argument. To access this data use:

data(hg38)

Human

data(mm10)

Mouse

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Chromosomal coordinate of human and mouse known genes

Description

A dataset containing the chromosomal coordinate for known human and mouse genes

Usage

genePosition

Format

data.frame with 2 columns: Chromosome and Coordinate. To access this data use:

data(hg38)

Human

data(mm10)

Mouse

---

Topological gene sets

Description

This function generates a list of sets of k genes encoded by neighbor loci

Usage

generateChromosomeGeneSet(species = c("human", "mouse"), k = 100, skip = 25)

Arguments

species	Character string indicating the species, either human or mouse
k	Integer indicating the number of genes per set
skip	Interger indicating the displacement of the window for selecting the k genes

Value

List of topoligically-close gene sets

Examples

chrom_set <- generateChromosomeGeneSet('human')
length(chrom_set)
chrom_set[seq_len(2)]

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Genomic Instability Analysis

Description

This function computes the genomic instability for an object of class inferCNV

Usage

genomicInstabilityScore(cnv, likelihood = FALSE)

Arguments

cnv	Object of class inferCNV generated by inferCNV() function
likelihood	Logical, whether the genomic instability likelihood should be estimated

Value

Object of class inferCNV with updated slots for gis and gisnull

See Also

[inferCNV()] to infer the enrichment of loci-blocks in the gene expression data.

Examples

eh <- ExperimentHub::ExperimentHub()
dset <- eh[["EH5419"]]
tpm_matrix <- SummarizedExperiment::assays(dset)$TPM
set.seed(1)
tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)]
cnv <- inferCNV(tpm_matrix)
cnv <- genomicInstabilityScore(cnv)
plot(density(cnv$gis))

---

Genomic instability plot

Description

This function plot the genomic instability distribution, gaussian fits and null distribution if available

Usage

giDensityPlot(inferCNV, legend = c("topleft", "top", "topright", "none"), ...)

Arguments

inferCNV	Object of class inferCNV
legend	Character string indicating the location of the legend. none to not include it
...	Additional parameters for plot()

Value

None, a figure is created in the default output device

See Also

[giLikelihood()] to estimate the relative likelihood, [genomicInstabilityScore()] to estimate the genomic instability score for each cell in the dataset, and [inferCNV()] to infer the enrichment of loci-blocks in the gene expression data.

Examples

eh <- ExperimentHub::ExperimentHub()
dset <- eh[["EH5419"]]
tpm_matrix <- SummarizedExperiment::assays(dset)$TPM
set.seed(1)
tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)]
cnv <- inferCNV(tpm_matrix)
cnv <- genomicInstabilityScore(cnv)
cnv <- giLikelihood(cnv, distros=c(3, 3), tumor=2:3)
giDensityPlot(cnv)

---

Genomic instability likelihood

Description

This function computes the genomic instability likelihood

Usage

giLikelihood(
  inferCNV,
  recompute = TRUE,
  distros = c(1, 3),
  tumor = NULL,
  normal = NULL
)

Arguments

inferCNV	InferCNV-class object
recompute	Logical, whether the model fits should be re-computed
distros	Vector of 2 integers indicating the minimum and maximum number of Gaussian models to fit
tumor	Optional vector of integers indicating the Gaussians considered as tumors
normal	Optional vector of integers indicating the Gaussians considered as normal. This is only useful when no null model has been provided for the analysis

Value

Updated inferCNV-class object with gi_likelihood slot

See Also

[genomicInstabilityScore()] to estimate the genomic instability score for each cell in the dataset, and [inferCNV()] to infer the enrichment of loci-blocks in the gene expression data.

Examples

eh <- ExperimentHub::ExperimentHub()
dset <- eh[["EH5419"]]
tpm_matrix <- SummarizedExperiment::assays(dset)$TPM
set.seed(1)
tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)]
cnv <- inferCNV(tpm_matrix)
cnv <- genomicInstabilityScore(cnv)
cnv <- giLikelihood(cnv, distros=c(3, 3), tumor=2:3)
print(cnv$gi_fit)
plot(density(cnv$gi_likelihood, from=0, to=1))

---

Inference of CNV from expression data

Description

This function estimates the CNV score based on expression data

Usage

inferCNV(
  expmat,
  nullmat = NULL,
  species = c("human", "mouse"),
  k = 100,
  skip = 25,
  min_geneset = 10,
  verbose = TRUE
)

Arguments

expmat	Matrix of gene expression profiles or signatures with genes '(entrezID) in rows and samples in columns
nullmat	Optional matrix with same number of rows as expmat to be used as null model
species	Character string indicating the species, either human or mouse
k	Integer indicating the number of genes per set
skip	Interger indicating the displacement of the window for selecting the k genes
min_geneset	Integer indicating the minimum size for the genesets
verbose	Logical, whether progress should be reported

Value

Object of class inferCNV, which is a list containing matrix of nes, and parameters (param), including species, window (k) and skip

Examples

eh <- ExperimentHub::ExperimentHub()
dset <- eh[["EH5419"]]
tpm_matrix <- SummarizedExperiment::assays(dset)$TPM
set.seed(1)
tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)]
cnv <- inferCNV(tpm_matrix)
class(cnv)
names(cnv)
cnv$nes[1:5, 1:3]

---

Plot chromosome map

Description

This function generates a chromosomes map plot for the inferred CNVs

Usage

## S3 method for class 'inferCNV'
plot(x, output = NULL, threshold = 0.2, gamma = 1.5, resolution = 150, ...)

Arguments

x	Object of class inferCNV
output	Optional output PDF file name (with extension)
threshold	Likelihood threshold for identifying genomically inestable cells/samples, 0 disables this filter
gamma	Number indicating the gamma transformation for the colors
resolution	Integer indicating the ppi for the png and jpg output files
...	Additional parameters for plot

Value

Nothing, a plot is generated in the default output devise

See Also

[giLikelihood()] to estimate the relative likelihood, [genomicInstabilityScore()] to estimate the genomic instability score for each cell in the dataset, and [inferCNV()] to infer the enrichment of loci-blocks in the gene expression data.

Examples

eh <- ExperimentHub::ExperimentHub()
dset <- eh[["EH5419"]]
tpm_matrix <- SummarizedExperiment::assays(dset)$TPM
set.seed(1)
tpm_matrix <- tpm_matrix[, sample(ncol(tpm_matrix), 500)]
cnv <- inferCNV(tpm_matrix)
cnv <- genomicInstabilityScore(cnv)
cnv <- giLikelihood(cnv, distros=c(3, 3), tumor=2:3)
plot(cnv, output='test.png')

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