R/auto_rep.R
In oriolarques/GEGVIC: A workflow to analyse Gene Expression, Genetic Variations and Immune cell Composition of tumour samples using Next Generation Sequencing data.
Defines functions
auto_rep
Documented in
auto_rep
#' @title auto_rep
#'
#' @description Executes all GEGVIC functions to generate an HTML report with
#' the results.
#'
#' @param ge_module Logical value to determine whether this module is executed or not.
#' @param gv_module Logical value to determine whether this module is executed or not.
#' @param ic_module Logical value to determine whether this module is executed or not.
#' @param out_dir Path to determine the output directory. By default it is set
#' to the working directory.
#' @param counts Data frame that contains gene expression data as raw counts.
#' @param genes_id Name of the column that contains gene identifiers. Should be
#' one of the following:'entrez_gene_id', 'ensemblgene_id' or 'hgnc_symbol'.
#' @param metadata Data frame that contains supporting variables to the data.
#' @param response QUOTED Name of the variable indicating the groups to analyse.
#' @param design Variables in the design formula in the form of: 'Var1 + Var2 + ... Var_n'.
#' @param colors Character vector indicating the colors of the different groups
#' to compare. Default values are two: black and orange.
#' @param ref_level Character vector where the first element is the column name
#' where the reference level is located and a second element indicating the name
#' of level to be used as a reference when calculating differential gene
#' expression.
#' @param shrink Name of the shrinkage method to apply: "apeglm", "ashr",
#' "normal" or "none". Use none to skip shrinkage. Default value is "apeglm".
#' @param biomart Data frame containing a biomaRt query with the following
#' attributes: ensembl_gene_id, hgnc_symbol, entrezgene_id, transcript_length,
#' refseq_mrna. In the case of mus musculus data, external_gene_name must be
#' obtained and then change the column name for hgnc_symbol. Uploaded biomaRt
#' queries in GEGVIC: 'ensembl_biomartGRCh37', ensembl_biomartGRCh38_p13' and
#' 'ensembl_biomartGRCm38_p6', 'ensembl_biomartGRCm39'.
#' @param fold_change An integer to define the fold change value to consider
#' that a gene is differentially expressed.
#' @param p.adj Numeric value to define the maximum adjusted p-value to consider
#' that a gene is differentially expressed.
#' @param gmt A data frame containg the gene sets to analyse using GSEA. This
#' object should be obtained with the read.gmt function from the clusterProfiler
#' package.
#' @param gsea_pvalue Numeric value to define the adjusted pvalue cutoff during
#' GSEA. Set to 0.2 by default.
#' @param gsva_gmt Path to the gmt file that contain the gene sets of interest. By
#' default the parameter is set to 'hallmark' which provides all HALLMARK gene
#' sets from MSigDB (version 7.5.1).
#' @param method Name of the method to perform Gene set variation analysis. The
#' options are: 'gsva', 'ssgea' or 'zscore'. Default value is 'gsva'.
#' @param kcdf Character string denoting the kernel to use during the non-parametric
#' estimation of the cumulative distribution function of expression levels across
#' samples when method="gsva". By default, "Gaussian" since GEGVIC transforms
#' raw counts using the vst transformation. Other options are 'Poisson' or 'none'.
#' @param row.names Logical value to determine if row-names are shown in the
#' heatmap.
#' @param col.names Logical value to determine if column-names are shown in the
#' heatmap.
#' @param muts Data frame containing genetic variations. Necessary columns must
#' have the following names:
#' - Hugo_Symbol: Gene symbol from HGNC.
#' - Chromosome: Affected chromosome.
#' - Start_Position: Mutation start coordinate.
#' - End_Position: Mutation end coordinate.
#' - Reference_Allele: The plus strand reference allele at this position.
#' Includes the deleted sequence for a deletion or "-" for an insertion.
#' - Tumor_Seq_Allele2: Tumor sequencing discovery allele.
#' - Variant_Classification: Translational effect of variant allele. Can be one
#' of the following: Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del,
#' In_Frame_Ins, Missense_Mutation, Nonsense_Mutation, Silent, Splice_Site,
#' Translation_Start_Site, Nonstop_Mutation, RNA, Targeted_Region.
#' - Variant_Type: Type of mutation. Can be: 'SNP' (Single nucleotide polymorphism),
#' 'DNP' (Double nucleotide polymorphism), 'INS' (Insertion), 'DEL' (Deletion).
#' - Tumor_Sample_Barcode: Sample name.
#' @param top_genes Number of genes to be analysed in the mutational summary.
#' @param specific_genes Genes that will be plotted in the oncoplot.
#' @param compare A character string indicating which method to be used for
#' comparing means. Options are 't.test' and 'wilcox.test' for two groups or
#' 'anova' and 'kruskal.test' for more groups. Default value is NULL.
#' @param p_label Character string specifying label type. Allowed values include
#' 'p.signif' (shows the significance levels), 'p.format' (shows the formatted
#' p-value).
#' @param gbuild Version of the genome to work with. It can be one of the following:
#' - ‘BSgenome.Hsapiens.UCSC.hg19’
#' - ‘BSgenome.Hsapiens.UCSC.hg38’
#' - ‘BSgenome.Mmusculus.UCSC.mm10’
#' - ‘BSgenome.Mmusculus.UCSC.mm39’
#' @param mut_sigs Mutational signature matrices containing the frequencies of
#' all nucleotide changes per signature need to be indicated. GEGVIC contains the
#' matrices from COSMIC for single and double base substitutions. To choose one,
#' the user has to indicate ’COSMIC_v{XX}_{YY}BS_GRCh{ZZ}’ in the mut_sigs argument.
#' The XX is the version, that can be v2 or v3.2. YY indicates if mutations are
#' single (S) or double (D) base substitutions, while the ZZ is for the genome
#' assembly, either GRCh37 or GRCh38 for human data and mm9 or mm10 for mouse data.
#' @param tri.counts.method Normalization method. Needs to be set to either:
#' - 'default' – no further normalization.
#' - 'exome' – normalized by number of times each trinucleotide context is
#' observed in the exome.
#' - 'genome' – normalized by number of times each trinucleotide context is
#' observed in the genome.
#' - 'exome2genome' – multiplied by a ratio of that trinucleotide's occurence in
#' the genome to the trinucleotide's occurence in the exome.
#' - 'genome2exome' – multiplied by a ratio of that trinucleotide's occurence in
#' the exome to the trinucleotide's occurence in the genome.
#' - data frame containing user defined scaling factor – count data for each
#' trinucleotide context is multiplied by the corresponding value given in the
#' data frame.
#' @param indications Character vector of cancer type codes for each sample in
#' the tpm matrix.This is used by TIMER method. Indications supported
#' can be checked using immunedeconv::timer_available_cancers. Default value is
#' NULL.
#' @param cibersort Path to the CIBERSORT.R and LM22.txt files. Default value is
#' NULL.
#' @param tumor Logical value to define if samples are tumors. If so EPIC and
#' quanTIseq use a signature matrix/procedure optimized for tumor samples.
#' Default value is TRUE.
#' @param rmgenes A character vector of gene symbols. Exclude these genes from
#' the analysis. Use this to exclude e.g. noisy genes.
#' @param scale_mrna Logical. If FALSE, disable correction for mRNA content of
#' different cell types. This is supported by methods that compute an absolute
#' score (EPIC and quanTIseq). Default value is TRUE.
#' @param expected_cell_types Limit the analysis to the cell types given in this
#' list. If the cell types present in the sample are known a priori, setting
#' this can improve results for xCell
#' (see https://github.com/grst/immunedeconv/issues/1).
#' @param points Logical value to decide if points are added to the plot.
#'
#' @return Returns an HTML report.
#'
#' @export
#'
#' @import rlang
#' @import DESeq2
#' @import SummarizedExperiment
#' @import dplyr
#' @import ggplot2
#' @import tibble
#' @import apeglm
#' @import ggrepel
#' @import clusterProfiler
#' @import GSEAmining
#' @import GSEABase
#' @import GSVA
#' @import pheatmap
#' @importFrom maftools read.maf
#' @importFrom maftools plotmafSummary
#' @importFrom maftools oncoplot
#' @import ggpubr
#' @import tidyr
#' @import deconstructSigs
#' @import ggplotify
#' @import immunedeconv
#' @import patchwork
#' @importFrom gridExtra marrangeGrob
#'
#' @examples
#' auto_rep(ge_module = TRUE,
#' gv_module = TRUE,
#' ic_module = TRUE,
#' out_dir = NULL,
#' counts = sample_counts,
#' genes_id = 'ensembl_gene_id',
#' metadata = sample_metadata,
#' response = 'MSI_status',
#' design = 'MSI_status',
#' colors = c('orange', 'black'),
#' ref_level = c('MSI_status', 'MSS'),
#' shrink = 'apeglm',
#' biomart = ensembl_biomart_GRCh38_p13,
#' fold_change = 2,
#' p.adj = 0.05,
#' gmt = 'inst/extdata/c2.cp.reactome.v7.5.1.symbols.gmt',
#' gsea_pvalue = 0.2,
#' gsva_gmt = 'hallmark',
#' method = 'gsva',
#' kcdf = 'Gaussian',
#' row.names = TRUE,
#' col.names = TRUE,
#' muts = sample_mutations,
#' top_genes = 10,
#' specific_genes = NULL,
#' compare = 'wilcox.test',
#' p_label = 'p.format',
#' gbuild = 'BSgenome.Hsapiens.UCSC.hg38',
#' mut_sigs = 'COSMIC_v2_SBS_GRCh38',
#' tri.counts.method = 'default',
#' indications = rep('COAD', ncol(sample_counts[-1])),
#' cibersort = NULL,
#' tumor = TRUE,
#' rmgenes = NULL,
#' scale_mrna = TRUE,
#' expected_cell_types = NULL,
#' points = TRUE)
#'
auto_rep <- function(ge_module = TRUE,
gv_module = TRUE,
ic_module = TRUE,
out_dir = NULL,
counts,
genes_id,
metadata,
response,
design,
colors = c('black', 'orange'),
ref_level,
shrink = 'apeglm',
biomart,
fold_change = 2,
p.adj = 0.05,
gmt,
gsea_pvalue = 0.2,
gsva_gmt = 'hallmark',
method = 'gsva',
kcdf = 'Gaussian',
row.names = TRUE,
col.names = TRUE,
muts,
top_genes = 10,
specific_genes = NULL,
compare = 'wilcox.test',
p_label = 'p.format',
gbuild,
mut_sigs,
tri.counts.method = 'default',
indications,
cibersort = NULL,
tumor = TRUE,
rmgenes = NULL,
scale_mrna = TRUE,
expected_cell_types = NULL,
points = TRUE) {
input <- paste0(system.file('extdata/', package = 'GEGVIC'),
'/GEGVIC_auto_report.Rmd')
if(is.null(out_dir) == TRUE){
out_dir <- getwd()
} else {
out_dir <- out_dir
}
rmarkdown::render(input = input,
output_dir = out_dir,
params = list("ge_module" = ge_module,
"gv_module" = gv_module,
"ic_module" = ic_module,
"out_dir" = out_dir,
"counts" = counts,
"genes_id" = genes_id,
"metadata" = metadata,
"response" = response,
"design" = design,
"colors" = colors,
"ref_level" = ref_level,
"shrink" = shrink,
"biomart" = biomart,
"fold_change" = fold_change,
"p.adj" = p.adj,
"gmt" = gmt,
"gsea_pvalue" = gsea_pvalue,
"gsva_gmt" = gsva_gmt,
"method" = method,
"kcdf" = kcdf,
"row.names" = row.names,
"col.names" = col.names,
"muts" = muts,
"top_genes" = top_genes,
"specific_genes" = specific_genes,
"compare" = compare,
"p_label" = p_label,
"gbuild" = gbuild,
"mut_sigs" = mut_sigs,
"tri.counts.method" = tri.counts.method,
"indications" = indications,
"cibersort" = cibersort,
"tumor" = tumor,
"rmgenes" = rmgenes,
"scale_mrna" = scale_mrna,
"expected_cell_types" = expected_cell_types,
"points" = points))
}
oriolarques/GEGVIC documentation built on Oct. 30, 2024, 10:44 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions auto_rep
Documented in auto_rep
#' @title auto_rep
#'
#' @description Executes all GEGVIC functions to generate an HTML report with
#' the results.
#'
#' @param ge_module Logical value to determine whether this module is executed or not.
#' @param gv_module Logical value to determine whether this module is executed or not.
#' @param ic_module Logical value to determine whether this module is executed or not.
#' @param out_dir Path to determine the output directory. By default it is set
#' to the working directory.
#' @param counts Data frame that contains gene expression data as raw counts.
#' @param genes_id Name of the column that contains gene identifiers. Should be
#' one of the following:'entrez_gene_id', 'ensemblgene_id' or 'hgnc_symbol'.
#' @param metadata Data frame that contains supporting variables to the data.
#' @param response QUOTED Name of the variable indicating the groups to analyse.
#' @param design Variables in the design formula in the form of: 'Var1 + Var2 + ... Var_n'.
#' @param colors Character vector indicating the colors of the different groups
#' to compare. Default values are two: black and orange.
#' @param ref_level Character vector where the first element is the column name
#' where the reference level is located and a second element indicating the name
#' of level to be used as a reference when calculating differential gene
#' expression.
#' @param shrink Name of the shrinkage method to apply: "apeglm", "ashr",
#' "normal" or "none". Use none to skip shrinkage. Default value is "apeglm".
#' @param biomart Data frame containing a biomaRt query with the following
#' attributes: ensembl_gene_id, hgnc_symbol, entrezgene_id, transcript_length,
#' refseq_mrna. In the case of mus musculus data, external_gene_name must be
#' obtained and then change the column name for hgnc_symbol. Uploaded biomaRt
#' queries in GEGVIC: 'ensembl_biomartGRCh37', ensembl_biomartGRCh38_p13' and
#' 'ensembl_biomartGRCm38_p6', 'ensembl_biomartGRCm39'.
#' @param fold_change An integer to define the fold change value to consider
#' that a gene is differentially expressed.
#' @param p.adj Numeric value to define the maximum adjusted p-value to consider
#' that a gene is differentially expressed.
#' @param gmt A data frame containg the gene sets to analyse using GSEA. This
#' object should be obtained with the read.gmt function from the clusterProfiler
#' package.
#' @param gsea_pvalue Numeric value to define the adjusted pvalue cutoff during
#' GSEA. Set to 0.2 by default.
#' @param gsva_gmt Path to the gmt file that contain the gene sets of interest. By
#' default the parameter is set to 'hallmark' which provides all HALLMARK gene
#' sets from MSigDB (version 7.5.1).
#' @param method Name of the method to perform Gene set variation analysis. The
#' options are: 'gsva', 'ssgea' or 'zscore'. Default value is 'gsva'.
#' @param kcdf Character string denoting the kernel to use during the non-parametric
#' estimation of the cumulative distribution function of expression levels across
#' samples when method="gsva". By default, "Gaussian" since GEGVIC transforms
#' raw counts using the vst transformation. Other options are 'Poisson' or 'none'.
#' @param row.names Logical value to determine if row-names are shown in the
#' heatmap.
#' @param col.names Logical value to determine if column-names are shown in the
#' heatmap.
#' @param muts Data frame containing genetic variations. Necessary columns must
#' have the following names:
#' - Hugo_Symbol: Gene symbol from HGNC.
#' - Chromosome: Affected chromosome.
#' - Start_Position: Mutation start coordinate.
#' - End_Position: Mutation end coordinate.
#' - Reference_Allele: The plus strand reference allele at this position.
#' Includes the deleted sequence for a deletion or "-" for an insertion.
#' - Tumor_Seq_Allele2: Tumor sequencing discovery allele.
#' - Variant_Classification: Translational effect of variant allele. Can be one
#' of the following: Frame_Shift_Del, Frame_Shift_Ins, In_Frame_Del,
#' In_Frame_Ins, Missense_Mutation, Nonsense_Mutation, Silent, Splice_Site,
#' Translation_Start_Site, Nonstop_Mutation, RNA, Targeted_Region.
#' - Variant_Type: Type of mutation. Can be: 'SNP' (Single nucleotide polymorphism),
#' 'DNP' (Double nucleotide polymorphism), 'INS' (Insertion), 'DEL' (Deletion).
#' - Tumor_Sample_Barcode: Sample name.
#' @param top_genes Number of genes to be analysed in the mutational summary.
#' @param specific_genes Genes that will be plotted in the oncoplot.
#' @param compare A character string indicating which method to be used for
#' comparing means. Options are 't.test' and 'wilcox.test' for two groups or
#' 'anova' and 'kruskal.test' for more groups. Default value is NULL.
#' @param p_label Character string specifying label type. Allowed values include
#' 'p.signif' (shows the significance levels), 'p.format' (shows the formatted
#' p-value).
#' @param gbuild Version of the genome to work with. It can be one of the following:
#' - ‘BSgenome.Hsapiens.UCSC.hg19’
#' - ‘BSgenome.Hsapiens.UCSC.hg38’
#' - ‘BSgenome.Mmusculus.UCSC.mm10’
#' - ‘BSgenome.Mmusculus.UCSC.mm39’
#' @param mut_sigs Mutational signature matrices containing the frequencies of
#' all nucleotide changes per signature need to be indicated. GEGVIC contains the
#' matrices from COSMIC for single and double base substitutions. To choose one,
#' the user has to indicate ’COSMIC_v{XX}_{YY}BS_GRCh{ZZ}’ in the mut_sigs argument.
#' The XX is the version, that can be v2 or v3.2. YY indicates if mutations are
#' single (S) or double (D) base substitutions, while the ZZ is for the genome
#' assembly, either GRCh37 or GRCh38 for human data and mm9 or mm10 for mouse data.
#' @param tri.counts.method Normalization method. Needs to be set to either:
#' - 'default' – no further normalization.
#' - 'exome' – normalized by number of times each trinucleotide context is
#' observed in the exome.
#' - 'genome' – normalized by number of times each trinucleotide context is
#' observed in the genome.
#' - 'exome2genome' – multiplied by a ratio of that trinucleotide's occurence in
#' the genome to the trinucleotide's occurence in the exome.
#' - 'genome2exome' – multiplied by a ratio of that trinucleotide's occurence in
#' the exome to the trinucleotide's occurence in the genome.
#' - data frame containing user defined scaling factor – count data for each
#' trinucleotide context is multiplied by the corresponding value given in the
#' data frame.
#' @param indications Character vector of cancer type codes for each sample in
#' the tpm matrix.This is used by TIMER method. Indications supported
#' can be checked using immunedeconv::timer_available_cancers. Default value is
#' NULL.
#' @param cibersort Path to the CIBERSORT.R and LM22.txt files. Default value is
#' NULL.
#' @param tumor Logical value to define if samples are tumors. If so EPIC and
#' quanTIseq use a signature matrix/procedure optimized for tumor samples.
#' Default value is TRUE.
#' @param rmgenes A character vector of gene symbols. Exclude these genes from
#' the analysis. Use this to exclude e.g. noisy genes.
#' @param scale_mrna Logical. If FALSE, disable correction for mRNA content of
#' different cell types. This is supported by methods that compute an absolute
#' score (EPIC and quanTIseq). Default value is TRUE.
#' @param expected_cell_types Limit the analysis to the cell types given in this
#' list. If the cell types present in the sample are known a priori, setting
#' this can improve results for xCell
#' (see https://github.com/grst/immunedeconv/issues/1).
#' @param points Logical value to decide if points are added to the plot.
#'
#' @return Returns an HTML report.
#'
#' @export
#'
#' @import rlang
#' @import DESeq2
#' @import SummarizedExperiment
#' @import dplyr
#' @import ggplot2
#' @import tibble
#' @import apeglm
#' @import ggrepel
#' @import clusterProfiler
#' @import GSEAmining
#' @import GSEABase
#' @import GSVA
#' @import pheatmap
#' @importFrom maftools read.maf
#' @importFrom maftools plotmafSummary
#' @importFrom maftools oncoplot
#' @import ggpubr
#' @import tidyr
#' @import deconstructSigs
#' @import ggplotify
#' @import immunedeconv
#' @import patchwork
#' @importFrom gridExtra marrangeGrob
#'
#' @examples
#' auto_rep(ge_module = TRUE,
#' gv_module = TRUE,
#' ic_module = TRUE,
#' out_dir = NULL,
#' counts = sample_counts,
#' genes_id = 'ensembl_gene_id',
#' metadata = sample_metadata,
#' response = 'MSI_status',
#' design = 'MSI_status',
#' colors = c('orange', 'black'),
#' ref_level = c('MSI_status', 'MSS'),
#' shrink = 'apeglm',
#' biomart = ensembl_biomart_GRCh38_p13,
#' fold_change = 2,
#' p.adj = 0.05,
#' gmt = 'inst/extdata/c2.cp.reactome.v7.5.1.symbols.gmt',
#' gsea_pvalue = 0.2,
#' gsva_gmt = 'hallmark',
#' method = 'gsva',
#' kcdf = 'Gaussian',
#' row.names = TRUE,
#' col.names = TRUE,
#' muts = sample_mutations,
#' top_genes = 10,
#' specific_genes = NULL,
#' compare = 'wilcox.test',
#' p_label = 'p.format',
#' gbuild = 'BSgenome.Hsapiens.UCSC.hg38',
#' mut_sigs = 'COSMIC_v2_SBS_GRCh38',
#' tri.counts.method = 'default',
#' indications = rep('COAD', ncol(sample_counts[-1])),
#' cibersort = NULL,
#' tumor = TRUE,
#' rmgenes = NULL,
#' scale_mrna = TRUE,
#' expected_cell_types = NULL,
#' points = TRUE)
#'
auto_rep <- function(ge_module = TRUE,
gv_module = TRUE,
ic_module = TRUE,
out_dir = NULL,
counts,
genes_id,
metadata,
response,
design,
colors = c('black', 'orange'),
ref_level,
shrink = 'apeglm',
biomart,
fold_change = 2,
p.adj = 0.05,
gmt,
gsea_pvalue = 0.2,
gsva_gmt = 'hallmark',
method = 'gsva',
kcdf = 'Gaussian',
row.names = TRUE,
col.names = TRUE,
muts,
top_genes = 10,
specific_genes = NULL,
compare = 'wilcox.test',
p_label = 'p.format',
gbuild,
mut_sigs,
tri.counts.method = 'default',
indications,
cibersort = NULL,
tumor = TRUE,
rmgenes = NULL,
scale_mrna = TRUE,
expected_cell_types = NULL,
points = TRUE) {
input <- paste0(system.file('extdata/', package = 'GEGVIC'),
'/GEGVIC_auto_report.Rmd')
if(is.null(out_dir) == TRUE){
out_dir <- getwd()
} else {
out_dir <- out_dir
}
rmarkdown::render(input = input,
output_dir = out_dir,
params = list("ge_module" = ge_module,
"gv_module" = gv_module,
"ic_module" = ic_module,
"out_dir" = out_dir,
"counts" = counts,
"genes_id" = genes_id,
"metadata" = metadata,
"response" = response,
"design" = design,
"colors" = colors,
"ref_level" = ref_level,
"shrink" = shrink,
"biomart" = biomart,
"fold_change" = fold_change,
"p.adj" = p.adj,
"gmt" = gmt,
"gsea_pvalue" = gsea_pvalue,
"gsva_gmt" = gsva_gmt,
"method" = method,
"kcdf" = kcdf,
"row.names" = row.names,
"col.names" = col.names,
"muts" = muts,
"top_genes" = top_genes,
"specific_genes" = specific_genes,
"compare" = compare,
"p_label" = p_label,
"gbuild" = gbuild,
"mut_sigs" = mut_sigs,
"tri.counts.method" = tri.counts.method,
"indications" = indications,
"cibersort" = cibersort,
"tumor" = tumor,
"rmgenes" = rmgenes,
"scale_mrna" = scale_mrna,
"expected_cell_types" = expected_cell_types,
"points" = points))
}
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