Type: Package
Title: Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumours
Version: 1.1.3
Author: Ajit Johnson Nirmal
Maintainer: Ajit Johnson Nirmal <ajitjohnson.n@gmail.com>
Description: Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.
License: GPL-3
URL: https://github.com/ajitjohnson/imsig/
BugReports: https://github.com/ajitjohnson/imsig/issues
Encoding: UTF-8
LazyData: true
Imports: HiClimR (≥ 1.2), RColorBrewer (≥ 1.1), igraph (≥ 1.2), ggplot2 (≥ 2.2), gridExtra (≥ 2.3), survival (≥ 2.4)
RoxygenNote: 7.1.1
Suggests: testthat
NeedsCompilation: no
Packaged: 2021-01-09 03:04:00 UTC; aj
Repository: CRAN
Date/Publication: 2021-01-10 01:00:02 UTC

Correlation matrix

Description

Creates a correlation matrix of ImSig signature genes.

Usage

corr_matrix(exp, r)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

Gene-gene correlation matrix of ImSig genes.

Example clinical data file for survival analysis with ImSig

Description

An example clinical data file. Minimum required informations are the sample name (same as that of the expression matrix), event (dead or alive) and time to event (days, months or years).

Usage

example_cli

Format

dataframe

Example transcriptomics data

Description

Example expression data matrix. The data is preffered to be in natural scale with genes as rows and samples as columns.Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data)

Usage

example_data

Format

dataframe

Feature selection of signature genes

Description

ImSig genes were designed to be co-expressed in tissue transcriptomic data. However, depending on the dataset some of the genes may not co-express with the dominant module. In order to remove such deviant genes, a feature selection can be carried out based on correlation. This function removes genes that exhibit a poor correlation (less than the defined r value) with the dominant ImSig module. This step of feature selection is recommended to enrich the prediction of relative abundance of immune cells.

Usage

feature_select(exp, r = 0.6)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

Returns a list of 'feature selected' genes based on the set r value.

Examples

feature_select (exp = example_data, r = 0.7)

General stastitics of ImSig analysis

Description

[Total genes in ImSig]: The total number of genes in ImSig list. [No. of ImSig genes in user dataset]: The number of ImSig genes found in user's dataset. Like all signatures, ImSig works best when this overlap is high, preferably over 75

Usage

gene_stat(exp, r = 0.6)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

Dataframe of general statistics of ImSig analysis.

See Also

feature_select

Examples

gene_stat (exp = example_data, r = 0.7)

Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data

Description

Estimates the relative abundance of immune cells across patients/samples.

Usage

imsig(exp, r = 0.6, sort = TRUE, sort_by = "T cells")

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

sort

Sort the samples based on abundance of a particular cell type. 'Set sort = FALSE' if you wish not to apply sorting. By default the function sorts by abundance of T cells. The cell type of interest for sorting can be controlled by the 'sort_by' parameter.

sort_by

Can be used to sort the samples by predicted abundance of a particular cell type. All other cell types follow this sorting. By default it is sorted by 'T cells'

Value

Relative abundance of immune cells across samples. Returns a dataframe.

See Also

feature_select, example_data

Examples

cell_abundance = imsig (exp = example_data, r = 0.7, sort=TRUE, sort_by='T cells')
head(cell_abundance)

Survival analysis based on relative abundance of immune infiltration estimated by ImSig

Description

Patients are split into two groups based on their immune cell abundance (median aundance value) and a regular survival analyis is carried out.

Usage

imsig_survival(exp, cli, time = "time", status = "status", r = 0.6)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

cli

Clinical metadata containting the event data (dead or alive) and time to event data. Samples names should be in rownames and same as that in the expression file. Check head() of example_cli for an example clinical data.

time

Column name of time-to-event parameter.

status

Column name of event (dead or alive) parameter.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

Hazard Ratio

See Also

feature_select, example_data, example_cli

Examples


survival = imsig_survival (exp = example_data, cli = example_cli)
head(survival)

Plot relative abundance of immune cells

Description

Barplots of relative abundance of immune cells across samples.The order of the samples are the same as that of imsig.

Usage

plot_abundance(exp, r = 0.6)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

ggplot

See Also

feature_select, example_data

Examples

plot_abundance (exp = example_data, r = 0.7)

Network graph of ImSig genes

Description

A Network visualization displays undirected graph structures and highlights the relationships between entities. The nodes are ImSig genes and the edges represent the correlation between them. The nodes are coloured based on cell type. Try using a correlation cut-off of '0' to get a complete picture.

Usage

plot_network(
  exp,
  r = 0.6,
  pt.cex = 2,
  cex = 1,
  inset = 0,
  x.intersp = 2,
  vertex.size = 3,
  vertex.label = NA,
  layout = layout_with_fr
)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

pt.cex

expansion factor(s) for the points.

cex

character expansion factor relative to current par("cex"). Used for text, and provides the default for pt.cex.

inset

inset distance(s) from the margins as a fraction of the plot region when legend is placed by keyword.

x.intersp

character interspacing factor for horizontal (x) spacing.

vertex.size

Node size of network graph

vertex.label

Add gene names to the network graph. Default set to NA.

layout

Layout algorithm to be used for building network. Default set to force-directed layout algorithm by Fruchterman and Reingold. Read documentation of 'igraph' for other available algorithms.

Value

Network graph

See Also

feature_select

Examples

plot_network (exp = example_data, r = 0.7)

Forest plot of survial analysis by ImSig

Description

Patients are split into two groups based on their immune cell abundance (median aundance value) and a regular survival analyis is carried out. Raw values can be obtained from imsig_survival.

Usage

plot_survival(exp, cli, time = "time", status = "status", r = 0.6)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

cli

Clinical metadata containting the event data (dead or alive) and time to event data. Samples names should be in rownames and same as that in the expression file. Check head() of example_cli for an example clinical data.

time

Column name of time-to-event parameter.

status

Column name of event (dead or alive) parameter.

r

Use a value between 0 and 1. Default is 0.6. This is a user defined correlation cut-off to perform feature selection (feature_select). Feature selection aids to enrich the prediction of relative abundance of immune cells by filtering off poorly correlated ImSig genes. To get an idea of what cut-off to use check the results of (gene_stat) and choose a cut-off that displays high median correlation and maintains a high proportion of genes after feature selection.

Value

Forest plot

See Also

feature_select, example_data, example_cli

Examples

plot_survival (exp = example_data, r = 0.7, cli = example_cli, time = 'time', status= 'status')

Pre-processing expression matrix

Description

Subsets the user's dataset based on the genes that are common to the users dataset and ImSig.

Usage

pp_exp(exp)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

Value

Expression dataframe

Pre-processing ImSig file

Description

Subsets ImSig genes based on the genes that are common to the users dataset and ImSig

Usage

pp_sig(exp)

Arguments

exp

Dataframe of transcriptomic data (natural scale) containing genes as rows and samples as columns. Note: Gene names should be set as row names and duplicates are not allowed. Missing values are not allowed within the expression matrix. Check example- head(example_data): example_data.

Value

ImSig dataframe

ImSig genes

Description

ImSig signature genes and the cell type they represent

Usage

sig

Format

dataframe