R/utils.R
In sigven/oncoEnrichR: Cancer-dedicated gene set interpretation
Defines functions
file_is_writable
add_excel_sheet
validate_db_df
validate_db
validate_query_genes
Documented in
validate_db
validate_db_df
validate_query_genes <- function(qgenes,
q_id_type = "symbol",
qtype = "target",
ignore_id_err = F,
genedb = NULL,
transcript_xref = NULL) {
lgr::lgr$appenders$console$set_layout(
lgr::LayoutFormat$new(timestamp_fmt = "%Y-%m-%d %T"))
stopifnot(!is.null(q_id_type))
stopifnot(is.character(qgenes))
stopifnot(q_id_type == "symbol" |
q_id_type == "entrezgene" |
q_id_type == "ensembl_mrna" |
q_id_type == "ensembl_protein" |
q_id_type == "refseq_protein" |
q_id_type == "refseq_mrna" |
q_id_type == "uniprot_acc" |
q_id_type == "ensembl_gene")
stopifnot(is.character(qgenes))
qgenes <- qgenes[!is.na(qgenes)]
stopifnot(!is.null(genedb))
stopifnot(!is.null(transcript_xref))
validate_db_df(genedb, dbtype = "genedb")
validate_db_df(transcript_xref, dbtype = "transcript_xref")
alias2entrez <-
transcript_xref |>
dplyr::filter(.data$property == "alias") |>
dplyr::rename(alias = "value") |>
dplyr::select(-c("property"))
target_genes <- data.frame(
'qid' = unique(qgenes),
stringsAsFactors = F)
target_genes$qid <- stringr::str_trim(
target_genes$qid
)
if (q_id_type == "entrezgene") {
target_genes$qid <- as.integer(target_genes$qid)
}
if (q_id_type == "ensembl_gene" |
q_id_type == "ensembl_protein" |
q_id_type == "ensembl_mrna" |
q_id_type == "refseq_mrna" |
q_id_type == "refseq_peptide") {
target_genes$qid <- stringr::str_replace(
target_genes$qid, "\\.[0-9]{1,}","")
}
gdb <- genedb |>
dplyr::select(c("symbol",
"entrezgene",
"name")) |>
dplyr::distinct()
queryset <- list()
queryset[['found']] <- data.frame()
queryset[['not_found']] <- data.frame()
queryset[['all']] <- data.frame()
queryset[['match_status']] <- "perfect_go"
qtype_id <- 'symbol'
if (q_id_type == 'entrezgene' |
q_id_type == 'symbol'){
qtype_id <- q_id_type
target_genes <- target_genes |>
dplyr::left_join(gdb,
by = c("qid" = qtype_id), relationship = "many-to-many") |>
dplyr::mutate(!!rlang::sym(qtype_id) := qid) |>
dplyr::distinct()
} else {
qtype_id <- q_id_type
if (q_id_type == "refseq_protein") {
qtype_id <- "refseq_peptide"
}
if (q_id_type == "ensembl_mrna") {
qtype_id <- "ensembl_transcript_id"
}
if (q_id_type == "ensembl_protein") {
qtype_id <- "ensembl_protein_id"
}
if (q_id_type == "ensembl_gene") {
qtype_id <- "ensembl_gene_id"
}
gene_xref_map <-
transcript_xref |>
dplyr::filter(.data$property == qtype_id) |>
dplyr::rename(!!rlang::sym(qtype_id) := value) |>
dplyr::select(c("entrezgene"), rlang::sym(qtype_id))
target_genes <- as.data.frame(
target_genes |>
## map query to entrezgene
dplyr::left_join(
gene_xref_map,
by = c("qid" = qtype_id), relationship = "many-to-many") |>
dplyr::mutate(!!rlang::sym(qtype_id) := qid) |>
## append other gene annotations
dplyr::left_join(gdb, by = c("entrezgene"), relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::group_by(
.data$symbol,
.data$entrezgene,
.data$name) |>
dplyr::summarise(
!!rlang::sym(qtype_id) := paste(
!!rlang::sym(qtype_id), collapse = ","),
qid = paste(.data$qid, collapse = ","),
.groups = "drop")
)
}
queryset[['found']] <- target_genes |>
dplyr::filter(!is.na(.data$symbol) &
!is.na(.data$entrezgene)) |>
dplyr::mutate(alias = F)
queryset[['not_found']] <- target_genes |>
dplyr::filter(is.na(.data$symbol) |
is.na(.data$entrezgene))
if (nrow(queryset[['not_found']]) > 0) {
if (ignore_id_err == T) {
queryset[['match_status']] <- "imperfect_go"
if (q_id_type == 'symbol') {
lgr::lgr$info( paste0("WARNING: ", qtype, " gene identifiers NOT found as primary symbols: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
lgr::lgr$info( paste0("Trying to map ", qtype, " gene identifiers as gene aliases/synonyms: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
query_as_alias <-
dplyr::inner_join(
dplyr::select(queryset[['not_found']], c("qid")),
alias2entrez,
by = c("qid" = "alias"), relationship = "many-to-many")
## Check that alias is not an alias for existing query entries (found)
## anti_join against found entries
if (nrow(query_as_alias) > 0) {
if (nrow(queryset[['found']]) > 0) {
query_as_alias <- query_as_alias |>
dplyr::anti_join(queryset[['found']],
by = "entrezgene") |>
dplyr::distinct()
}
if (nrow(query_as_alias) > 0) {
query_as_alias <- query_as_alias |>
dplyr::left_join(gdb,
by = "entrezgene", relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::mutate(alias = T)
lgr::lgr$info(
paste0("Mapped query identifiers as gene aliases ",
paste0(query_as_alias$qid, collapse = ", ")," ---> ",
paste0(query_as_alias$symbol, collapse = ", ")))
queryset[['found']] <-
dplyr::bind_rows(queryset[['found']], query_as_alias)
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::anti_join(query_as_alias, by = "qid")
if (nrow(queryset[['not_found']]) > 0) {
lgr::lgr$warn(
paste0(stringr::str_to_title(qtype)," gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
} else {
queryset[['match_status']] <- "perfect_go"
}
}
} else {
lgr::lgr$warn(
paste0("Query gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
} else {
lgr::lgr$warn(paste0(
stringr::str_to_title(qtype), " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", ")))
}
## Indicate that processing should stop when encountering invalid query identifiers
} else {
queryset[['match_status']] <- "imperfect_stop"
if (q_id_type == 'symbol') {
lgr::lgr$warn( paste0("WARNING: ", qtype, " gene identifiers NOT found as primary symbols: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
lgr::lgr$info( paste0("Trying to map ", qtype, " gene identifiers as gene aliases/synonyms: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
query_as_alias <-
dplyr::inner_join(
dplyr::select(queryset[['not_found']], c("qid")),
alias2entrez,
by = c("qid" = "alias"), relationship = "many-to-many")
if (nrow(query_as_alias) > 0) {
query_as_alias <- query_as_alias |>
dplyr::left_join(gdb,
by = "entrezgene", relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::mutate(alias = T)
lgr::lgr$info(
paste0("Mapped ", qtype, " gene identifiers as gene aliases ",
paste0(query_as_alias$qid, collapse = ", ")," ---> ",
paste0(query_as_alias$symbol, collapse = ", ")))
queryset[['found']] <-
dplyr::bind_rows(queryset[['found']], query_as_alias)
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::anti_join(query_as_alias, by = "qid")
if (nrow(queryset[['not_found']]) > 0) {
lgr::lgr$info(
paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
} else {
queryset[['match_status']] <- "perfect_go"
}
} else {
lgr::lgr$info( paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
}
else{
lgr::lgr$info( paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
}
}
if (nrow(queryset[['found']]) == length(qgenes)) {
lgr::lgr$info( paste0('SUCCESS: Identified all genes (n = ',
nrow(queryset[['found']]),') in ',qtype,' set'))
} else {
if (nrow(queryset[['found']]) == 0) {
lgr::lgr$info( paste0(
"ERROR: NO ", qtype, " gene identifiers found: ",
paste0(target_genes$qid, collapse = ", "),
" - wrong query_id_type (",q_id_type,")?","\n"))
queryset[['match_status']] <- "imperfect_stop"
} else {
lgr::lgr$info(
paste0('Identified n = ',
nrow(queryset[['found']]),' entries in ', qtype,
' set (n = ',
nrow(queryset[['not_found']]),' invalid entries)'))
}
}
if (nrow(queryset[['found']]) > 0) {
queryset[['found']] <- queryset[['found']] |>
dplyr::mutate(status = 'found') |>
dplyr::mutate(
status = dplyr::if_else(
.data$status == "found" & .data$alias == T,
as.character("found_as_alias"),
as.character(.data$status))
) |>
dplyr::arrange(dplyr::desc(.data$status), .data$symbol) |>
dplyr::mutate(
genename = paste0("<a href='https://www.ncbi.nlm.nih.gov/gene/",
.data$entrezgene,"' target='_blank'>",.data$name,"</a>")
)
}
if (nrow(queryset[['not_found']]) > 0) {
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::mutate(status = 'not_found') |>
dplyr::mutate(genename = NA) |>
dplyr::arrange(.data$qid)
}
if (nrow(queryset[['found']]) > 0 | nrow(queryset[['not_found']]) > 0) {
queryset[['all']] <- as.data.frame(
queryset[['not_found']] |>
dplyr::bind_rows(queryset[['found']]) |>
dplyr::rename(query_id = "qid") |>
dplyr::select(c("query_id",
"status",
"symbol",
"genename")) |>
dplyr::rowwise() |>
dplyr::mutate(
symbol = dplyr::if_else(
.data$status == "not_found",
as.character(NA),
as.character(.data$symbol)))
)
}
queryset[['found']]$qid <- NULL
queryset[['not_found']]$qid <- NULL
return(queryset)
}
#' Function that validates the oncoEnrichR database object
#'
#' @param db list object with annotation data for oncoEnrichR
#'
#' @keywords internal
#'
validate_db <- function(oe_db) {
## check that db is of list type
db_entries <-
c("cancerdrugdb",
"genedb",
"hpa",
"ligandreceptordb",
"otdb",
"pathwaydb",
"pfamdb",
"depmapdb",
"survivaldb",
"release_notes",
"slparalogdb",
"subcelldb",
"tcgadb",
"tftargetdb",
"tissuecelldb")
for (db in db_entries) {
if (!(db %in% names(oe_db))) {
lgr::lgr$info(paste0("ERROR: '",db,"' NOT found in oncoEnrichR db object"))
return(-1)
}
}
return(0)
}
#' Function that validates a particular db object (data.frame) in oncoEnrichR
#'
#' @param df data.frame with annotation data for oncoEnrichR
#' @param dbtype type of oncoEnrichR datasource
#'
#' @keywords internal
#'
validate_db_df <- function(df, dbtype = "genedb") {
val <- assertthat::validate_that(
is.data.frame(df)
)
if (!is.logical(val)) {
stop(val)
}
dbtypes <- c("genedb",
"hpadb",
"tcga_aberration",
"tcga_diagnosis_code",
"tcga_site_code",
"tcga_clinical_strata_code",
"tcga_coexpression",
"tcga_recurrent_variants",
"protein_complex",
"protein_domain",
"dorothea",
"ligand_receptor_db",
"ligand_receptor_xref",
"transcript_xref",
"biogrid",
"compartments",
"oeDB",
"tf_target_interactions",
"go_gganatogram",
"opentarget_disease_assoc",
"opentarget_disease_site_rank",
"enrichment_db_hpa_singlecell",
"enrichment_db_hpa_tissue",
"ppi_nodes",
"slparalog",
"fitness_scores",
"target_priority_scores",
"survival_km_cshl",
"ppi_edges")
if (!(dbtype %in% dbtypes)) {
stop(
paste0("dbtype '",dbtype,
"' not recognized, possible s are: ",
paste(sort(dbtypes), collapse = ", ")))
}
if (dbtype == "genedb") {
cols <- c('symbol',
'entrezgene',
'oncogene',
'oncogene_confidence_level',
'tumor_suppressor',
'tsg_confidence_level',
'cancer_driver',
'hgnc_id',
'tsg_support',
'oncogene_support',
'driver_support',
'cancergene_evidence',
'go_term_link',
'name',
'gene_summary',
'gene_biotype',
'AB_tractability_category',
'AB_tractability_support',
'SM_tractability_category',
'SM_tractability_support',
'genename',
'targeted_cancer_drugs_lp',
'targeted_cancer_drugs_ep',
'approved_drugs',
'num_go_terms',
'cancer_max_rank',
'unknown_function_rank',
'has_gene_summary')
}
if (dbtype == "opentarget_disease_assoc") {
cols <- c("disease_efo_id",
"direct_ot_association",
"ot_association_score",
"ot_datatype_support",
"ensembl_gene_id",
"efo_name",
"primary_site",
"cancer_phenotype",
"ot_link")
}
if (dbtype == "biogrid") {
cols <- c("entrezgene_A",
"entrezgene_B",
"method",
"pmid",
"throughput")
}
if (dbtype == "tcga_aberration") {
cols <- c("symbol",
"variant_type",
"samples_mutated",
"tot_samples",
"percent_mutated",
"percentile",
"decile",
"clinical_strata_code",
"diagnosis_code",
"site_code")
}
if (dbtype == "slparalog") {
cols <- c("entrezgene_A1",
"symbol_A1",
"entrezgene_A2",
"symbol_A2",
"prediction_score",
"prediction_percentile",
"sequence_identity_pct",
"family_size",
"conservation_score",
"ppi_overlap")
}
if (dbtype == "tcga_coexpression") {
cols <- c("symbol",
"symbol_partner",
"r",
"p_value",
"tumor",
"tumor_suppressor",
"oncogene",
"cancer_driver")
}
if (dbtype == "tcga_diagnosis_code") {
cols <- c("primary_diagnosis",
"diagnosis_code")
}
if (dbtype == "tcga_site_code") {
cols <- c("primary_site",
"site_code")
}
if (dbtype == "tcga_clinical_strata_code") {
cols <- c("clinical_strata",
"clinical_strata_code")
}
if (dbtype == "opentarget_disease_site_rank") {
cols <- c("primary_site",
"ensembl_gene_id",
"tissue_assoc_score",
"tissue_assoc_rank",
"global_assoc_score",
"global_assoc_rank")
}
if (dbtype == "enrichment_db_hpa_singlecell") {
cols <- c("ensembl_gene_id",
"category",
"cell_type")
}
if (dbtype == "enrichment_db_hpa_tissue") {
cols <- c("ensembl_gene_id",
"category",
"tissue")
}
if (dbtype == "hpadb") {
cols <- c("ensembl_gene_id",
"property",
"value")
}
if (dbtype == "tf_target_interactions") {
cols = c("queryset_overlap",
"literature_support",
"interaction_sources",
"regulator_cancer_max_rank",
"target_cancer_max_rank",
"regulator",
"regulator_name",
"target",
"target_name",
"confidence_level",
"mode_of_regulation")
}
if (dbtype == "go_gganatogram") {
cols <- c("ggcompartment",
"go_id")
}
if (dbtype == "protein_complex") {
cols <- c('complex_id',
'complex_name',
'purification_method',
'complex_comment',
'disease_comment',
'sources',
'confidence',
'complex_literature',
'complex_literature_support')
}
if (dbtype == "protein_domain") {
cols <- c('uniprot_acc',
'pfam_id',
'pfam_short_name',
'pfam_long_name',
'domain_freq')
}
if (dbtype == "ligand_receptor_xref") {
cols <- c('interaction_id',
'symbol',
'class')
}
if (dbtype == "ligand_receptor_db") {
cols <- c('interaction_id',
'interaction_name',
'annotation',
'pathway_name',
'interaction_members',
'ligand',
'receptor',
'agonist',
'antagonist',
'co_A_receptor',
'co_I_receptor',
'literature_support')
}
if (dbtype == "dorothea") {
cols <- c('regulator',
'target',
'interaction_sources',
'confidence_level',
'mode_of_regulation',
'tf_target_literature_support',
'tf_target_literature')
}
if (dbtype == "transcript_xref") {
cols <- c('entrezgene',
'property',
'value')
}
if (dbtype == "survival_km_cshl") {
cols <- c('symbol',
'tcga_cohort',
'z_score')
}
if (dbtype == "target_priority_scores") {
cols <- c('symbol',
'gene_id',
'priority_score',
'tumor_type')
}
if (dbtype == "fitness_scores") {
cols <- c('symbol',
'model_name',
'model_id',
'loss_of_fitness',
'scaled_BF',
'tissue',
'cancer_type',
'tissue_status',
'entrezgene',
'sample_site',
'gene_id_project_score')
}
if (dbtype == "comppidb") {
cols <- c('uniprot_acc',
'go_id',
'go_term',
'confidence',
'annotation_source',
'annotation_type')
}
if (dbtype == "compartments") {
cols <- c('entrezgene',
'go_id',
'go_term',
'confidence',
'annotation_source',
'annotation_channel')
}
if (dbtype == "ppi_nodes") {
cols <- c('symbol',
'entrezgene',
'genename',
'query_node',
'cancer_driver',
'id',
'tumor_suppressor',
'oncogene')
}
if (dbtype == "ppi_edges") {
cols <- c('symbol_A',
'symbol_B',
'entrezgene_A',
'entrezgene_B',
'oncogene_A',
'oncogene_B',
'tsgene_A',
'tsgene_B',
'cdriver_A',
'cdriver_B',
'querynode_A',
'querynode_B',
'weight',
'from',
'to',
'interaction_symbol')
}
assertable::assert_colnames(df,
colnames = cols,
only_colnames = F,
quiet = T)
if (dbtype == 'transcript_xref') {
identifiers_expected <- c('alias',
'ensembl_gene_id',
'ensembl_protein_id',
'ensembl_transcript_id',
'refseq_mrna',
'refseq_peptide',
'symbol',
'uniprot_acc')
identifiers_found <- sort(unique(df$property))
assertthat::assert_that(
identical(identifiers_expected, identifiers_found),
msg = paste0("Identifier types present in 'transcript_xref' data frame",
" ('property' column) does not have all necessary s")
)
assertthat::assert_that(
typeof(df$entrezgene) == "integer",
msg = paste0("Type of 'entrezgene' column in 'transcript_xref' ",
"is not of type 'integer'")
)
}
return(0)
}
add_excel_sheet <- function(
report = NULL,
workbook = NULL,
analysis_output = "disease_association",
tableStyle = "TableStyleMedium15") {
invisible(assertthat::assert_that(!is.null(report)))
invisible(assertthat::assert_that(!is.null(report$data)))
invisible(assertthat::assert_that(!is.null(workbook)))
target_df <- data.frame()
lgr::lgr$info(
paste0("Adding Excel sheet to workbook - ", analysis_output))
if (analysis_output == "settings") {
target_df <- data.frame(
category = 'CANCER_ASSOCIATION',
configuration = 'show_disease',
value = as.character(report$config$show$disease),
stringsAsFactors = F
)
target_df <- target_df |>
dplyr::bind_rows(
data.frame(
category = 'CANCER_ASSOCIATION',
configuration = 'show_top_diseases',
value = as.character(report$config$disease$show_top_diseases),
stringsAsFactors = F
),
data.frame(
category = 'CANCER_HALLMARK',
configuration = 'show_hallmark',
value = as.character(report$config$show$cancer_hallmark),
stringsAsFactors = F
),
data.frame(
category = 'UNKNOWN_FUNCTION',
configuration = 'show_unknown_function',
value = as.character(report$config$show$unknown_function),
stringsAsFactors = F
),
data.frame(
category = 'DRUG_KNOWN',
configuration = 'show_drug',
value = as.character(report$config$show$drug),
stringsAsFactors = F
),
data.frame(
category = 'SYNTHETIC_LETHALITY',
configuration = 'show_synleth',
value = as.character(report$config$show$synleth),
stringsAsFactors = F
),
data.frame(
category = 'PROTEIN_COMPLEX',
configuration = 'show_protein_complex',
value = as.character(report$config$show$protein_complex),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'show_enrichment',
value = as.character(report$config$show$enrichment),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_p_value_adj',
value = as.character(report$config$enrichment$p_adjust_method),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_p_value_cutoff',
value = as.character(report$config$enrichment$p_value_cutoff),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_q_value_cutoff',
value = as.character(report$config$enrichment$q_value_cutoff),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_min_geneset_size',
value = as.character(report$config$enrichment$min_gs_size),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_max_geneset_size',
value = as.character(report$config$enrichment$max_gs_size),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_simplify_go',
value = as.character(report$config$enrichment$simplify_go),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'bgset_description',
value = as.character(report$config$enrichment$bgset_description),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'bgset_size',
value = as.character(report$config$enrichment$bgset_size),
stringsAsFactors = F
),
data.frame(
category = 'REGULATORY',
configuration = 'show_regulatory',
value = as.character(report$config$show$regulatory),
stringsAsFactors = F
),
data.frame(
category = 'REGULATORY',
configuration = 'regulatory_min_confidence',
value = as.character(report$config$regulatory$min_confidence),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'show_subcell',
value = as.character(report$config$show$subcellcomp),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_min_confidence',
value = as.character(report$config$subcellcomp$minimum_confidence),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_min_channels',
value = as.character(report$config$subcellcomp$minimum_channels),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_channels',
value = paste(
as.character(
report$config$subcellcomp$channels),
collapse = ", "),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'show_cytosol',
value = as.character(report$config$subcellcomp$show_cytosol),
stringsAsFactors = F
),
data.frame(
category = 'CELL_TISSUE',
configuration = 'show_cell_tissue',
value = as.character(report$config$show$cell_tissue),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'show_ppi',
value = as.character(report$config$show$ppi),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_string_min_score',
value = as.character(report$config$ppi$string$minimum_score),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_string_network_type',
value = as.character(report$config$ppi$string$network_type),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_biogrid_min_evidence',
value = as.character(report$config$ppi$biogrid$minimum_evidence),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_show_drugs',
value = as.character(report$config$ppi$string$show_drugs),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_add_nodes',
value = as.character(report$config$ppi$string$add_nodes),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_show_isolated_nodes',
value = as.character(report$config$ppi$string$show_isolated_nodes),
stringsAsFactors = F
),
data.frame(
category = 'FITNESS',
configuration = 'show_fitness',
value = as.character(report$config$show$fitness),
stringsAsFactors = F
),
data.frame(
category = 'FITNESS',
configuration = 'fitness_max_score',
value = as.character(report$config$fitness$max_BF_score),
stringsAsFactors = F
),
data.frame(
category = 'ABERRATION',
configuration = 'show_aberration',
value = as.character(report$config$show$aberration),
stringsAsFactors = F
),
data.frame(
category = 'COEXPRESSION',
configuration = 'show_coexpression',
value = as.character(report$config$show$coexpression),
stringsAsFactors = F
),
data.frame(
category = 'LIGAND_RECEPTOR',
configuration = 'show_ligand_receptor',
value = as.character(report$config$show$ligand_receptor),
stringsAsFactors = F
),
data.frame(
category = 'PROGNOSTIC',
configuration = 'show_prognostic',
value = as.character(report$config$show$cancer_prognosis),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_title',
value = as.character(report$config$project_title),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_description',
value = as.character(report$config$project_description),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_owner',
value = as.character(report$config$project_owner),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'query_ignore_err',
value = as.character(report$config$query$ignore_err),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'query_id_type',
value = as.character(report$config$query$id_type),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'bgset_id_type',
value = as.character(report$config$bgset$id_type),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'floating_toc',
value = as.character(report$config$rmarkdown$floating_toc),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'report_theme',
value = as.character(report$config$rmarkdown$theme),
stringsAsFactors = F
)
)
}
if (analysis_output == "query") {
if (is.data.frame(report$data$query$target)) {
if (NROW(report$data$query$target) > 0) {
target_df <- report$data$query$target |>
dplyr::mutate(
genename =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
)
}
}
}
if (analysis_output == "unknown_function") {
if (is.data.frame(report$data$unknown_function$hits_df)) {
if (NROW(report$data$unknown_function$hits_df) > 0) {
target_df <- report$data$unknown_function$hits_df |>
dplyr::rename(go_terms = .data$go_term_link) |>
dplyr::mutate(
annotation_source = "Gene Ontology (2022-12)/NCBI Gene/UniProt (2022_05)",
version = NA) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
) |>
dplyr::mutate(
go_terms =
stringr::str_trim(
textclean::replace_html(.data$go_terms)
)
) |>
dplyr::mutate(
gene_summary =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$gene_summary)
)
)
)
}
}
}
if (analysis_output == "cancer_association") {
if (is.data.frame(report$data$disease$target)) {
if (NROW(report$data$disease$target) > 0) {
target_df <- report$data$disease$target |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::select(-c("cancer_association_links",
"disease_association_links",
"cancergene_evidence",
"gene_summary")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
if (analysis_output == "cancer_hallmark") {
if (is.data.frame(report$data$cancer_hallmark$target)) {
if (NROW(report$data$cancer_hallmark$target) > 0) {
target_df <- report$data$cancer_hallmark$target |>
dplyr::mutate(
symbol =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
)
) |>
dplyr::select(-c("literature_support"))
}
}
}
if (analysis_output == "drug_known") {
if (is.data.frame(report$data$drug$target_drugs)) {
if (NROW(report$data$drug$target_drugs)) {
target_df <- report$data$drug$target_drugs |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
) |>
dplyr::mutate(
approved_drugs =
stringr::str_trim(
textclean::replace_html(.data$approved_drugs)
)
) |>
dplyr::mutate(
drugs_late_phase =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$drugs_late_phase)
)
),
" , ",
", "
)
) |>
dplyr::mutate(
drugs_early_phase =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$drugs_early_phase)
)
),
" , ",
", "
)
)
}
}
}
if (analysis_output == "drug_tractability") {
if (is.data.frame(report$data$drug$tractability_ab) &
is.data.frame(report$data$drug$tractability_sm)) {
if (NROW(report$data$drug$tractability_ab) |
NROW(report$data$drug$tractability_sm)) {
target_df <- data.frame()
if (NROW(report$data$drug$tractability_sm)) {
df <- report$data$drug$tractability_sm |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::rename(tractability_category = "SM_tractability_category",
tractability_support = "SM_tractability_support") |>
dplyr::mutate(tractability_drugtype = "Small molecules/compounds") |>
dplyr::select(c("annotation_source", "version",
"tractability_drugtype"),
dplyr::everything()) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
) |>
dplyr::mutate(
tractability_support =
stringr::str_trim(
textclean::replace_html(.data$tractability_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
if (NROW(report$data$drug$tractability_ab)) {
df <- report$data$drug$tractability_ab |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::rename(tractability_category = .data$AB_tractability_category,
tractability_support = .data$AB_tractability_support) |>
dplyr::mutate(tractability_drugtype = "Antibody") |>
dplyr::select(c("annotation_source", "version",
"tractability_drugtype"),
dplyr::everything()) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
) |>
dplyr::mutate(
tractability_support =
stringr::str_trim(
textclean::replace_html(.data$tractability_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "ligand_receptor") {
for (c in c('secreted_signaling','cell_cell_contact',
'ecm_receptor')) {
if (is.data.frame(report$data$ligand_receptor[[c]])) {
if (NROW(report$data$ligand_receptor[[c]]) > 0) {
df <- report$data$ligand_receptor[[c]] |>
dplyr::mutate(
annotation_source = report$config$resources[['cellchatdb']]$name,
version = report$config$resources[['cellchatdb']]$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
literature_support =
stringr::str_trim(
textclean::replace_html(.data$literature_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "protein_domain") {
if (is.data.frame(report$data$protein_domain$target)) {
if (NROW(report$data$protein_domain$target) > 0) {
df <- report$data$protein_domain$target |>
dplyr::mutate(
annotation_source = report$config$resources[['pfam']]$name,
version = report$config$resources[['pfam']]$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
protein_domain =
stringr::str_trim(
textclean::replace_html(.data$protein_domain)
),
target_genes =
stringr::str_trim(
textclean::replace_html(.data$target_genes)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (analysis_output == "protein_complex") {
for (c in c('omnipath','humap2')) {
if (is.data.frame(report$data$protein_complex[[c]])) {
if (NROW(report$data$protein_complex[[c]]) > 0) {
res_name <- c
if(c == 'omnipath'){
res_name <- 'omnipathr'
}
df <- report$data$protein_complex[[c]] |>
dplyr::mutate(
annotation_source = report$config$resources[[res_name]]$name,
version = report$config$resources[[res_name]]$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
complex_genes =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$complex_genes)
)
),
" , ",
", "
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (NROW(target_df) > 0 &
"literature" %in% colnames(target_df)) {
target_df <- target_df |>
dplyr::mutate(
literature =
stringr::str_trim(
textclean::replace_html(.data$literature)
)
) |>
dplyr::mutate(
complex_name =
stringr::str_trim(
textclean::replace_html(.data$complex_name)
)
)
}
}
if (analysis_output == "prognostic_association_I") {
if (is.data.frame(report$data$cancer_prognosis$hpa$assocs)) {
if (NROW(report$data$cancer_prognosis$hpa$assocs) > 0) {
target_df <- report$data$cancer_prognosis$hpa$assocs |>
dplyr::mutate(
annotation_source = report$config$resources$hpa$name,
version = report$config$resources$hpa$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
tumor_types =
stringr::str_trim(
textclean::replace_html(.data$tumor_types)
)
)
}
}
}
if (analysis_output == "synthetic_lethality") {
for (t in c('single_pair_member','both_in_pair')) {
if (is.data.frame(report$data$synleth[[t]])) {
if (NROW(report$data$synleth[[t]]) > 0) {
df <- report$data$synleth[[t]] |>
dplyr::mutate(
annotation_source = "De Kegel et al., Cell Systems, 2021",
version = "v1") |>
dplyr::mutate(feature_type = t) |>
dplyr::mutate(
genename_A =
stringr::str_trim(
textclean::replace_html(.data$genename_A)
)
) |>
dplyr::mutate(
genename_B =
stringr::str_trim(
textclean::replace_html(.data$genename_B)
)
) |>
dplyr::arrange(.data$feature_type,
dplyr::desc(.data$prediction_score)) |>
dplyr::select(c("annotation_source",
"version",
"feature_type"), dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "prognostic_association_II") {
for (t in c('cna','mut','exp','meth')) {
if (is.data.frame(report$data$cancer_prognosis$km_cshl$assocs[[t]])) {
if (NROW(report$data$cancer_prognosis$km_cshl$assocs[[t]]) > 0) {
df <- report$data$cancer_prognosis$km_cshl$assocs[[t]] |>
dplyr::mutate(
annotation_source = report$config$resources$tcga_survival$name,
version = report$config$resources$tcga_survival$version) |>
dplyr::mutate(feature_type = t) |>
dplyr::arrange(.data$feature_type, .data$z_score) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "coexpression") {
## co-expression
if (is.data.frame(report$data$tcga$coexpression)) {
if (NROW(report$data$tcga$coexpression) > 0) {
target_df <- report$data$tcga$coexpression |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::rename(tcga_cohort = "tumor") |>
dplyr::select(-c("entrezgene")) |>
dplyr::rename(partner_oncogene = "oncogene",
partner_tumor_suppressor = "tumor_suppressor",
partner_cancer_driver = "cancer_driver") |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "regulatory") {
## regulatory interactions
for (c in c('pancancer','global')) {
if (is.data.frame(report$data$regulatory$interactions[[c]])) {
if (NROW(report$data$regulatory$interactions[[c]]) > 0) {
df <- report$data$regulatory$interactions[[c]] |>
dplyr::mutate(
dorothea_collection = c,
annotation_source = report$config$resources$dorothea$name,
version = report$config$resources$dorothea$version) |>
dplyr::select(c("annotation_source",
"version",
"dorothea_collection"),
dplyr::everything()) |>
dplyr::mutate(
target_name =
stringr::str_trim(
textclean::replace_html(.data$target_name)
)
) |>
dplyr::mutate(
regulator_name =
stringr::str_trim(
textclean::replace_html(.data$regulator_name)
)
) |>
dplyr::mutate(
literature_support =
stringr::str_trim(
textclean::replace_html(.data$literature_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "recurrent_variants") {
if (is.data.frame(report$data$tcga$recurrent_variants)) {
if (NROW(report$data$tcga$recurrent_variants) > 0) {
df <-
report$data$tcga$recurrent_variants
colnames(df) <- tolower(colnames(df))
df <- as.data.frame(
df |>
dplyr::mutate(
site_recurrence = as.numeric(.data$site_recurrence)
) |>
dplyr::arrange(
dplyr::desc(.data$total_recurrence),
dplyr::desc(.data$site_recurrence)) |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::mutate(
ensembl_gene_id =
stringr::str_trim(
textclean::replace_html(.data$ensembl_gene_id)
)
) |>
dplyr::mutate(
ensembl_transcript_id =
stringr::str_trim(
textclean::replace_html(.data$ensembl_transcript_id)
)
) |>
dplyr::mutate(
protein_domain =
stringr::str_trim(
textclean::replace_html(.data$protein_domain)
)
) |>
dplyr::mutate(
cosmic_mutation_id =
stringr::str_trim(
textclean::replace_html(.data$cosmic_mutation_id)
)
) |>
dplyr::mutate(
site_recurrence = paste(.data$primary_site,
.data$site_recurrence, sep =":")
) |>
dplyr::group_by(
.data$symbol, .data$consequence,
.data$protein_change, .data$protein_domain,
.data$mutation_hotspot,
.data$loss_of_function,
.data$ensembl_gene_id,
.data$ensembl_transcript_id,
.data$total_recurrence,
.data$cosmic_mutation_id
) |>
dplyr::summarise(site_recurrence = paste(
.data$site_recurrence, collapse = ", "
), .groups = "drop") |>
dplyr::arrange(
dplyr::desc(.data$total_recurrence)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (analysis_output == "aberration") {
## cna aberrations
for (t in c('cna_ampl','cna_homdel')) {
if (is.data.frame(report$data$tcga$aberration$table[[t]])) {
if (NROW(report$data$tcga$aberration$table[[t]]) > 0) {
df <-
report$data$tcga$aberration$table[[t]] |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$gene)
)
) |>
dplyr::select(-c("gene")) |>
dplyr::select(c("annotation_source", "version",
"symbol"), dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "subcellcomp") {
if (is.data.frame(report$data$subcellcomp$all)) {
if (NROW(report$data$subcellcomp$all) > 0) {
target_df <- report$data$subcellcomp$all |>
dplyr::mutate(
annotation_source = report$config$resources$compartments$name,
version = report$config$resources$compartments$version) |>
dplyr::select(-c("ggcompartment")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
compartment =
stringr::str_trim(
textclean::replace_html(.data$compartment)
)
) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
if (analysis_output == "enrichment") {
enrichment_df <- data.frame()
for (e in c('go','wikipathway','kegg','msigdb','netpath')) {
if (NROW(report$data$enrichment[[e]]) > 0) {
enrichment_df <- enrichment_df |>
dplyr::bind_rows(
report$data$enrichment[[e]] |>
dplyr::mutate(
annotation_source = report$config$resources[[e]]$name,
version = report$config$resources[[e]]$version) |>
dplyr::rename(category = "db",
entrezgene = "gene_id")
) |>
dplyr::select(-c("gene_symbol_link",
"description_link")) |>
dplyr::select(c("annotation_source", "version",
"category", "description"),
dplyr::everything())
}
}
target_df <- enrichment_df
}
if (analysis_output == "fitness_scores") {
if (is.data.frame(report$data$fitness$fitness_scores$targets)) {
if (NROW(report$data$fitness$fitness_scores$targets) > 0) {
target_df <- report$data$fitness$fitness_scores$targets |>
dplyr::mutate(
annotation_source = report$config$resources$depmap$name,
version = report$config$resources$depmap$version) |>
dplyr::select(-c("symbol_link_ps", "model_link_ps",
"n_gene")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "fitness_prioritized") {
if (is.data.frame(report$data$fitness$target_priority_scores$targets)) {
if (NROW(report$data$fitness$target_priority_scores$targets) > 0) {
target_df <- report$data$fitness$target_priority_scores$targets |>
dplyr::mutate(
annotation_source = report$config$resources$depmap$name,
version = report$config$resources$depmap$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "ppi_string") {
target_df <- data.frame()
if (is.data.frame(report$data$ppi$string$complete_network$edges)) {
if (NROW(report$data$ppi$string$complete_network$edges) > 0) {
target_df <- report$data$ppi$string$complete_network$edges |>
dplyr::filter(!is.na(.data$entrezgene_A) &
!is.na(.data$entrezgene_B)) |>
dplyr::arrange(dplyr::desc(.data$score)) |>
dplyr::rename(is_target_A = "querynode_A",
is_target_B = "querynode_B",
string_score = "score",
string_nscore = "nscore",
string_fscore = "fscore",
string_pscore = "pscore",
string_ascore = "ascore",
string_escore = "escore",
string_dscore = "dscore",
string_tscore = "tscore"
) |>
dplyr::select(c("symbol_A",
"symbol_B",
"is_target_A",
"is_target_B",
"string_score",
"string_nscore",
"string_fscore",
"string_pscore",
"string_ascore",
"string_escore",
"string_dscore",
"string_tscore")) |>
dplyr::mutate(
annotation_source = report$config$resources$string$name,
version = report$config$resources$string$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "ppi_biogrid") {
target_df <- data.frame()
if (is.data.frame(report$data$ppi$biogrid$complete_network$edges)) {
if (NROW(report$data$ppi$biogrid$complete_network$edges) > 0) {
target_df <- report$data$ppi$biogrid$complete_network$edges |>
dplyr::filter(!is.na(.data$entrezgene_A) &
!is.na(.data$entrezgene_B)) |>
dplyr::rename(is_target_A = "querynode_A",
is_target_B = "querynode_B",
biogrid_evidence = "evidence",
biogrid_num_evidence = "num_evidence_items"
) |>
dplyr::select(c("symbol_A",
"symbol_B",
"is_target_A",
"is_target_B",
"biogrid_evidence",
"biogrid_num_evidence")) |>
dplyr::arrange(
dplyr::desc(.data$biogrid_num_evidence)) |>
dplyr::mutate(
annotation_source = report$config$resources$biogrid$name,
version = report$config$resources$biogrid$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "cell_tissue") {
target_df <- data.frame()
for (e in c("tissue_enrichment", "scRNA_enrichment")) {
if (is.data.frame(report$data$cell_tissue[[e]]$per_gene)) {
if (NROW(report$data$cell_tissue[[e]]$per_gene) > 0) {
if (e == "tissue_enrichment") {
df <-
report$data$cell_tissue[[e]]$per_gene |>
dplyr::mutate(
annotation_source = report$config$resources$gtex$name,
version = report$config$resources$gtex$version,
category = stringr::str_replace(e,"_enrichment","")) |>
dplyr::select(c("annotation_source",
"version",
"category"),
dplyr::everything()) |>
dplyr::rename(tissue_or_celltype = "tissue")
} else {
df <-
report$data$cell_tissue[[e]]$per_gene |>
dplyr::mutate(
annotation_source = report$config$resources$hpa$name,
version = report$config$resources$hpa$version,
category = stringr::str_replace(e,"_enrichment","")) |>
dplyr::select(c("annotation_source",
"version",
"category"), dplyr::everything()) |>
dplyr::rename(tissue_or_celltype = "cell_type")
}
target_df <- target_df |>
dplyr::bind_rows(df) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
}
if (nrow(target_df) > 0) {
analysis_output = stringr::str_replace(
analysis_output,"_ps_","_")
openxlsx::addWorksheet(workbook,
sheetName = toupper(analysis_output))
## set automatic column widths
openxlsx::setColWidths(workbook,
sheet = toupper(analysis_output),
cols = 1:ncol(target_df),
widths = "auto")
## write with default Excel Table style
openxlsx::writeDataTable(workbook,
sheet = toupper(analysis_output),
x = target_df,
startRow = 1,
startCol = 1,
colNames = TRUE,
tableStyle = tableStyle)
}
return(workbook)
}
file_is_writable <- function(path) {
assertthat::is.string(path) &&
file.exists(path) &&
assertthat::is.writeable(path)
}
#' Tidy eval helpers
#'
#' <https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html>
#'
#' @name tidyeval
#' @keywords internal
#' @importFrom rlang .data :=
NULL
#' GSEAbase imports
#'
#' @name gseabase
#' @keywords internal
#' @importFrom GSEABase GeneSet SymbolIdentifier ENSEMBLIdentifier EntrezIdentifier
NULL
utils::globalVariables(c("."))
sigven/oncoEnrichR documentation built on Aug. 31, 2023, 8:05 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions file_is_writable add_excel_sheet validate_db_df validate_db validate_query_genes
Documented in validate_db validate_db_df
validate_query_genes <- function(qgenes,
q_id_type = "symbol",
qtype = "target",
ignore_id_err = F,
genedb = NULL,
transcript_xref = NULL) {
lgr::lgr$appenders$console$set_layout(
lgr::LayoutFormat$new(timestamp_fmt = "%Y-%m-%d %T"))
stopifnot(!is.null(q_id_type))
stopifnot(is.character(qgenes))
stopifnot(q_id_type == "symbol" |
q_id_type == "entrezgene" |
q_id_type == "ensembl_mrna" |
q_id_type == "ensembl_protein" |
q_id_type == "refseq_protein" |
q_id_type == "refseq_mrna" |
q_id_type == "uniprot_acc" |
q_id_type == "ensembl_gene")
stopifnot(is.character(qgenes))
qgenes <- qgenes[!is.na(qgenes)]
stopifnot(!is.null(genedb))
stopifnot(!is.null(transcript_xref))
validate_db_df(genedb, dbtype = "genedb")
validate_db_df(transcript_xref, dbtype = "transcript_xref")
alias2entrez <-
transcript_xref |>
dplyr::filter(.data$property == "alias") |>
dplyr::rename(alias = "value") |>
dplyr::select(-c("property"))
target_genes <- data.frame(
'qid' = unique(qgenes),
stringsAsFactors = F)
target_genes$qid <- stringr::str_trim(
target_genes$qid
)
if (q_id_type == "entrezgene") {
target_genes$qid <- as.integer(target_genes$qid)
}
if (q_id_type == "ensembl_gene" |
q_id_type == "ensembl_protein" |
q_id_type == "ensembl_mrna" |
q_id_type == "refseq_mrna" |
q_id_type == "refseq_peptide") {
target_genes$qid <- stringr::str_replace(
target_genes$qid, "\\.[0-9]{1,}","")
}
gdb <- genedb |>
dplyr::select(c("symbol",
"entrezgene",
"name")) |>
dplyr::distinct()
queryset <- list()
queryset[['found']] <- data.frame()
queryset[['not_found']] <- data.frame()
queryset[['all']] <- data.frame()
queryset[['match_status']] <- "perfect_go"
qtype_id <- 'symbol'
if (q_id_type == 'entrezgene' |
q_id_type == 'symbol'){
qtype_id <- q_id_type
target_genes <- target_genes |>
dplyr::left_join(gdb,
by = c("qid" = qtype_id), relationship = "many-to-many") |>
dplyr::mutate(!!rlang::sym(qtype_id) := qid) |>
dplyr::distinct()
} else {
qtype_id <- q_id_type
if (q_id_type == "refseq_protein") {
qtype_id <- "refseq_peptide"
}
if (q_id_type == "ensembl_mrna") {
qtype_id <- "ensembl_transcript_id"
}
if (q_id_type == "ensembl_protein") {
qtype_id <- "ensembl_protein_id"
}
if (q_id_type == "ensembl_gene") {
qtype_id <- "ensembl_gene_id"
}
gene_xref_map <-
transcript_xref |>
dplyr::filter(.data$property == qtype_id) |>
dplyr::rename(!!rlang::sym(qtype_id) := value) |>
dplyr::select(c("entrezgene"), rlang::sym(qtype_id))
target_genes <- as.data.frame(
target_genes |>
## map query to entrezgene
dplyr::left_join(
gene_xref_map,
by = c("qid" = qtype_id), relationship = "many-to-many") |>
dplyr::mutate(!!rlang::sym(qtype_id) := qid) |>
## append other gene annotations
dplyr::left_join(gdb, by = c("entrezgene"), relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::group_by(
.data$symbol,
.data$entrezgene,
.data$name) |>
dplyr::summarise(
!!rlang::sym(qtype_id) := paste(
!!rlang::sym(qtype_id), collapse = ","),
qid = paste(.data$qid, collapse = ","),
.groups = "drop")
)
}
queryset[['found']] <- target_genes |>
dplyr::filter(!is.na(.data$symbol) &
!is.na(.data$entrezgene)) |>
dplyr::mutate(alias = F)
queryset[['not_found']] <- target_genes |>
dplyr::filter(is.na(.data$symbol) |
is.na(.data$entrezgene))
if (nrow(queryset[['not_found']]) > 0) {
if (ignore_id_err == T) {
queryset[['match_status']] <- "imperfect_go"
if (q_id_type == 'symbol') {
lgr::lgr$info( paste0("WARNING: ", qtype, " gene identifiers NOT found as primary symbols: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
lgr::lgr$info( paste0("Trying to map ", qtype, " gene identifiers as gene aliases/synonyms: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
query_as_alias <-
dplyr::inner_join(
dplyr::select(queryset[['not_found']], c("qid")),
alias2entrez,
by = c("qid" = "alias"), relationship = "many-to-many")
## Check that alias is not an alias for existing query entries (found)
## anti_join against found entries
if (nrow(query_as_alias) > 0) {
if (nrow(queryset[['found']]) > 0) {
query_as_alias <- query_as_alias |>
dplyr::anti_join(queryset[['found']],
by = "entrezgene") |>
dplyr::distinct()
}
if (nrow(query_as_alias) > 0) {
query_as_alias <- query_as_alias |>
dplyr::left_join(gdb,
by = "entrezgene", relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::mutate(alias = T)
lgr::lgr$info(
paste0("Mapped query identifiers as gene aliases ",
paste0(query_as_alias$qid, collapse = ", ")," ---> ",
paste0(query_as_alias$symbol, collapse = ", ")))
queryset[['found']] <-
dplyr::bind_rows(queryset[['found']], query_as_alias)
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::anti_join(query_as_alias, by = "qid")
if (nrow(queryset[['not_found']]) > 0) {
lgr::lgr$warn(
paste0(stringr::str_to_title(qtype)," gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
} else {
queryset[['match_status']] <- "perfect_go"
}
}
} else {
lgr::lgr$warn(
paste0("Query gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
} else {
lgr::lgr$warn(paste0(
stringr::str_to_title(qtype), " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", ")))
}
## Indicate that processing should stop when encountering invalid query identifiers
} else {
queryset[['match_status']] <- "imperfect_stop"
if (q_id_type == 'symbol') {
lgr::lgr$warn( paste0("WARNING: ", qtype, " gene identifiers NOT found as primary symbols: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
lgr::lgr$info( paste0("Trying to map ", qtype, " gene identifiers as gene aliases/synonyms: ",paste0(queryset[['not_found']]$qid, collapse = ", ")))
query_as_alias <-
dplyr::inner_join(
dplyr::select(queryset[['not_found']], c("qid")),
alias2entrez,
by = c("qid" = "alias"), relationship = "many-to-many")
if (nrow(query_as_alias) > 0) {
query_as_alias <- query_as_alias |>
dplyr::left_join(gdb,
by = "entrezgene", relationship = "many-to-many") |>
dplyr::distinct() |>
dplyr::mutate(alias = T)
lgr::lgr$info(
paste0("Mapped ", qtype, " gene identifiers as gene aliases ",
paste0(query_as_alias$qid, collapse = ", ")," ---> ",
paste0(query_as_alias$symbol, collapse = ", ")))
queryset[['found']] <-
dplyr::bind_rows(queryset[['found']], query_as_alias)
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::anti_join(query_as_alias, by = "qid")
if (nrow(queryset[['not_found']]) > 0) {
lgr::lgr$info(
paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
} else {
queryset[['match_status']] <- "perfect_go"
}
} else {
lgr::lgr$info( paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
}
else{
lgr::lgr$info( paste0("ERROR: ", qtype, " gene identifiers NOT found: ",
paste0(queryset[['not_found']]$qid, collapse = ", "),
" (make sure that unambiguous primary identifiers/symbols are used)"))
}
}
}
if (nrow(queryset[['found']]) == length(qgenes)) {
lgr::lgr$info( paste0('SUCCESS: Identified all genes (n = ',
nrow(queryset[['found']]),') in ',qtype,' set'))
} else {
if (nrow(queryset[['found']]) == 0) {
lgr::lgr$info( paste0(
"ERROR: NO ", qtype, " gene identifiers found: ",
paste0(target_genes$qid, collapse = ", "),
" - wrong query_id_type (",q_id_type,")?","\n"))
queryset[['match_status']] <- "imperfect_stop"
} else {
lgr::lgr$info(
paste0('Identified n = ',
nrow(queryset[['found']]),' entries in ', qtype,
' set (n = ',
nrow(queryset[['not_found']]),' invalid entries)'))
}
}
if (nrow(queryset[['found']]) > 0) {
queryset[['found']] <- queryset[['found']] |>
dplyr::mutate(status = 'found') |>
dplyr::mutate(
status = dplyr::if_else(
.data$status == "found" & .data$alias == T,
as.character("found_as_alias"),
as.character(.data$status))
) |>
dplyr::arrange(dplyr::desc(.data$status), .data$symbol) |>
dplyr::mutate(
genename = paste0("<a href='https://www.ncbi.nlm.nih.gov/gene/",
.data$entrezgene,"' target='_blank'>",.data$name,"</a>")
)
}
if (nrow(queryset[['not_found']]) > 0) {
queryset[['not_found']] <- queryset[['not_found']] |>
dplyr::mutate(status = 'not_found') |>
dplyr::mutate(genename = NA) |>
dplyr::arrange(.data$qid)
}
if (nrow(queryset[['found']]) > 0 | nrow(queryset[['not_found']]) > 0) {
queryset[['all']] <- as.data.frame(
queryset[['not_found']] |>
dplyr::bind_rows(queryset[['found']]) |>
dplyr::rename(query_id = "qid") |>
dplyr::select(c("query_id",
"status",
"symbol",
"genename")) |>
dplyr::rowwise() |>
dplyr::mutate(
symbol = dplyr::if_else(
.data$status == "not_found",
as.character(NA),
as.character(.data$symbol)))
)
}
queryset[['found']]$qid <- NULL
queryset[['not_found']]$qid <- NULL
return(queryset)
}
#' Function that validates the oncoEnrichR database object
#'
#' @param db list object with annotation data for oncoEnrichR
#'
#' @keywords internal
#'
validate_db <- function(oe_db) {
## check that db is of list type
db_entries <-
c("cancerdrugdb",
"genedb",
"hpa",
"ligandreceptordb",
"otdb",
"pathwaydb",
"pfamdb",
"depmapdb",
"survivaldb",
"release_notes",
"slparalogdb",
"subcelldb",
"tcgadb",
"tftargetdb",
"tissuecelldb")
for (db in db_entries) {
if (!(db %in% names(oe_db))) {
lgr::lgr$info(paste0("ERROR: '",db,"' NOT found in oncoEnrichR db object"))
return(-1)
}
}
return(0)
}
#' Function that validates a particular db object (data.frame) in oncoEnrichR
#'
#' @param df data.frame with annotation data for oncoEnrichR
#' @param dbtype type of oncoEnrichR datasource
#'
#' @keywords internal
#'
validate_db_df <- function(df, dbtype = "genedb") {
val <- assertthat::validate_that(
is.data.frame(df)
)
if (!is.logical(val)) {
stop(val)
}
dbtypes <- c("genedb",
"hpadb",
"tcga_aberration",
"tcga_diagnosis_code",
"tcga_site_code",
"tcga_clinical_strata_code",
"tcga_coexpression",
"tcga_recurrent_variants",
"protein_complex",
"protein_domain",
"dorothea",
"ligand_receptor_db",
"ligand_receptor_xref",
"transcript_xref",
"biogrid",
"compartments",
"oeDB",
"tf_target_interactions",
"go_gganatogram",
"opentarget_disease_assoc",
"opentarget_disease_site_rank",
"enrichment_db_hpa_singlecell",
"enrichment_db_hpa_tissue",
"ppi_nodes",
"slparalog",
"fitness_scores",
"target_priority_scores",
"survival_km_cshl",
"ppi_edges")
if (!(dbtype %in% dbtypes)) {
stop(
paste0("dbtype '",dbtype,
"' not recognized, possible s are: ",
paste(sort(dbtypes), collapse = ", ")))
}
if (dbtype == "genedb") {
cols <- c('symbol',
'entrezgene',
'oncogene',
'oncogene_confidence_level',
'tumor_suppressor',
'tsg_confidence_level',
'cancer_driver',
'hgnc_id',
'tsg_support',
'oncogene_support',
'driver_support',
'cancergene_evidence',
'go_term_link',
'name',
'gene_summary',
'gene_biotype',
'AB_tractability_category',
'AB_tractability_support',
'SM_tractability_category',
'SM_tractability_support',
'genename',
'targeted_cancer_drugs_lp',
'targeted_cancer_drugs_ep',
'approved_drugs',
'num_go_terms',
'cancer_max_rank',
'unknown_function_rank',
'has_gene_summary')
}
if (dbtype == "opentarget_disease_assoc") {
cols <- c("disease_efo_id",
"direct_ot_association",
"ot_association_score",
"ot_datatype_support",
"ensembl_gene_id",
"efo_name",
"primary_site",
"cancer_phenotype",
"ot_link")
}
if (dbtype == "biogrid") {
cols <- c("entrezgene_A",
"entrezgene_B",
"method",
"pmid",
"throughput")
}
if (dbtype == "tcga_aberration") {
cols <- c("symbol",
"variant_type",
"samples_mutated",
"tot_samples",
"percent_mutated",
"percentile",
"decile",
"clinical_strata_code",
"diagnosis_code",
"site_code")
}
if (dbtype == "slparalog") {
cols <- c("entrezgene_A1",
"symbol_A1",
"entrezgene_A2",
"symbol_A2",
"prediction_score",
"prediction_percentile",
"sequence_identity_pct",
"family_size",
"conservation_score",
"ppi_overlap")
}
if (dbtype == "tcga_coexpression") {
cols <- c("symbol",
"symbol_partner",
"r",
"p_value",
"tumor",
"tumor_suppressor",
"oncogene",
"cancer_driver")
}
if (dbtype == "tcga_diagnosis_code") {
cols <- c("primary_diagnosis",
"diagnosis_code")
}
if (dbtype == "tcga_site_code") {
cols <- c("primary_site",
"site_code")
}
if (dbtype == "tcga_clinical_strata_code") {
cols <- c("clinical_strata",
"clinical_strata_code")
}
if (dbtype == "opentarget_disease_site_rank") {
cols <- c("primary_site",
"ensembl_gene_id",
"tissue_assoc_score",
"tissue_assoc_rank",
"global_assoc_score",
"global_assoc_rank")
}
if (dbtype == "enrichment_db_hpa_singlecell") {
cols <- c("ensembl_gene_id",
"category",
"cell_type")
}
if (dbtype == "enrichment_db_hpa_tissue") {
cols <- c("ensembl_gene_id",
"category",
"tissue")
}
if (dbtype == "hpadb") {
cols <- c("ensembl_gene_id",
"property",
"value")
}
if (dbtype == "tf_target_interactions") {
cols = c("queryset_overlap",
"literature_support",
"interaction_sources",
"regulator_cancer_max_rank",
"target_cancer_max_rank",
"regulator",
"regulator_name",
"target",
"target_name",
"confidence_level",
"mode_of_regulation")
}
if (dbtype == "go_gganatogram") {
cols <- c("ggcompartment",
"go_id")
}
if (dbtype == "protein_complex") {
cols <- c('complex_id',
'complex_name',
'purification_method',
'complex_comment',
'disease_comment',
'sources',
'confidence',
'complex_literature',
'complex_literature_support')
}
if (dbtype == "protein_domain") {
cols <- c('uniprot_acc',
'pfam_id',
'pfam_short_name',
'pfam_long_name',
'domain_freq')
}
if (dbtype == "ligand_receptor_xref") {
cols <- c('interaction_id',
'symbol',
'class')
}
if (dbtype == "ligand_receptor_db") {
cols <- c('interaction_id',
'interaction_name',
'annotation',
'pathway_name',
'interaction_members',
'ligand',
'receptor',
'agonist',
'antagonist',
'co_A_receptor',
'co_I_receptor',
'literature_support')
}
if (dbtype == "dorothea") {
cols <- c('regulator',
'target',
'interaction_sources',
'confidence_level',
'mode_of_regulation',
'tf_target_literature_support',
'tf_target_literature')
}
if (dbtype == "transcript_xref") {
cols <- c('entrezgene',
'property',
'value')
}
if (dbtype == "survival_km_cshl") {
cols <- c('symbol',
'tcga_cohort',
'z_score')
}
if (dbtype == "target_priority_scores") {
cols <- c('symbol',
'gene_id',
'priority_score',
'tumor_type')
}
if (dbtype == "fitness_scores") {
cols <- c('symbol',
'model_name',
'model_id',
'loss_of_fitness',
'scaled_BF',
'tissue',
'cancer_type',
'tissue_status',
'entrezgene',
'sample_site',
'gene_id_project_score')
}
if (dbtype == "comppidb") {
cols <- c('uniprot_acc',
'go_id',
'go_term',
'confidence',
'annotation_source',
'annotation_type')
}
if (dbtype == "compartments") {
cols <- c('entrezgene',
'go_id',
'go_term',
'confidence',
'annotation_source',
'annotation_channel')
}
if (dbtype == "ppi_nodes") {
cols <- c('symbol',
'entrezgene',
'genename',
'query_node',
'cancer_driver',
'id',
'tumor_suppressor',
'oncogene')
}
if (dbtype == "ppi_edges") {
cols <- c('symbol_A',
'symbol_B',
'entrezgene_A',
'entrezgene_B',
'oncogene_A',
'oncogene_B',
'tsgene_A',
'tsgene_B',
'cdriver_A',
'cdriver_B',
'querynode_A',
'querynode_B',
'weight',
'from',
'to',
'interaction_symbol')
}
assertable::assert_colnames(df,
colnames = cols,
only_colnames = F,
quiet = T)
if (dbtype == 'transcript_xref') {
identifiers_expected <- c('alias',
'ensembl_gene_id',
'ensembl_protein_id',
'ensembl_transcript_id',
'refseq_mrna',
'refseq_peptide',
'symbol',
'uniprot_acc')
identifiers_found <- sort(unique(df$property))
assertthat::assert_that(
identical(identifiers_expected, identifiers_found),
msg = paste0("Identifier types present in 'transcript_xref' data frame",
" ('property' column) does not have all necessary s")
)
assertthat::assert_that(
typeof(df$entrezgene) == "integer",
msg = paste0("Type of 'entrezgene' column in 'transcript_xref' ",
"is not of type 'integer'")
)
}
return(0)
}
add_excel_sheet <- function(
report = NULL,
workbook = NULL,
analysis_output = "disease_association",
tableStyle = "TableStyleMedium15") {
invisible(assertthat::assert_that(!is.null(report)))
invisible(assertthat::assert_that(!is.null(report$data)))
invisible(assertthat::assert_that(!is.null(workbook)))
target_df <- data.frame()
lgr::lgr$info(
paste0("Adding Excel sheet to workbook - ", analysis_output))
if (analysis_output == "settings") {
target_df <- data.frame(
category = 'CANCER_ASSOCIATION',
configuration = 'show_disease',
value = as.character(report$config$show$disease),
stringsAsFactors = F
)
target_df <- target_df |>
dplyr::bind_rows(
data.frame(
category = 'CANCER_ASSOCIATION',
configuration = 'show_top_diseases',
value = as.character(report$config$disease$show_top_diseases),
stringsAsFactors = F
),
data.frame(
category = 'CANCER_HALLMARK',
configuration = 'show_hallmark',
value = as.character(report$config$show$cancer_hallmark),
stringsAsFactors = F
),
data.frame(
category = 'UNKNOWN_FUNCTION',
configuration = 'show_unknown_function',
value = as.character(report$config$show$unknown_function),
stringsAsFactors = F
),
data.frame(
category = 'DRUG_KNOWN',
configuration = 'show_drug',
value = as.character(report$config$show$drug),
stringsAsFactors = F
),
data.frame(
category = 'SYNTHETIC_LETHALITY',
configuration = 'show_synleth',
value = as.character(report$config$show$synleth),
stringsAsFactors = F
),
data.frame(
category = 'PROTEIN_COMPLEX',
configuration = 'show_protein_complex',
value = as.character(report$config$show$protein_complex),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'show_enrichment',
value = as.character(report$config$show$enrichment),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_p_value_adj',
value = as.character(report$config$enrichment$p_adjust_method),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_p_value_cutoff',
value = as.character(report$config$enrichment$p_value_cutoff),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_q_value_cutoff',
value = as.character(report$config$enrichment$q_value_cutoff),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_min_geneset_size',
value = as.character(report$config$enrichment$min_gs_size),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_max_geneset_size',
value = as.character(report$config$enrichment$max_gs_size),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'enrichment_simplify_go',
value = as.character(report$config$enrichment$simplify_go),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'bgset_description',
value = as.character(report$config$enrichment$bgset_description),
stringsAsFactors = F
),
data.frame(
category = 'ENRICHMENT',
configuration = 'bgset_size',
value = as.character(report$config$enrichment$bgset_size),
stringsAsFactors = F
),
data.frame(
category = 'REGULATORY',
configuration = 'show_regulatory',
value = as.character(report$config$show$regulatory),
stringsAsFactors = F
),
data.frame(
category = 'REGULATORY',
configuration = 'regulatory_min_confidence',
value = as.character(report$config$regulatory$min_confidence),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'show_subcell',
value = as.character(report$config$show$subcellcomp),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_min_confidence',
value = as.character(report$config$subcellcomp$minimum_confidence),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_min_channels',
value = as.character(report$config$subcellcomp$minimum_channels),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'subcellcomp_channels',
value = paste(
as.character(
report$config$subcellcomp$channels),
collapse = ", "),
stringsAsFactors = F
),
data.frame(
category = 'SUBCELL',
configuration = 'show_cytosol',
value = as.character(report$config$subcellcomp$show_cytosol),
stringsAsFactors = F
),
data.frame(
category = 'CELL_TISSUE',
configuration = 'show_cell_tissue',
value = as.character(report$config$show$cell_tissue),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'show_ppi',
value = as.character(report$config$show$ppi),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_string_min_score',
value = as.character(report$config$ppi$string$minimum_score),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_string_network_type',
value = as.character(report$config$ppi$string$network_type),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_biogrid_min_evidence',
value = as.character(report$config$ppi$biogrid$minimum_evidence),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_show_drugs',
value = as.character(report$config$ppi$string$show_drugs),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_add_nodes',
value = as.character(report$config$ppi$string$add_nodes),
stringsAsFactors = F
),
data.frame(
category = 'PPI',
configuration = 'ppi_show_isolated_nodes',
value = as.character(report$config$ppi$string$show_isolated_nodes),
stringsAsFactors = F
),
data.frame(
category = 'FITNESS',
configuration = 'show_fitness',
value = as.character(report$config$show$fitness),
stringsAsFactors = F
),
data.frame(
category = 'FITNESS',
configuration = 'fitness_max_score',
value = as.character(report$config$fitness$max_BF_score),
stringsAsFactors = F
),
data.frame(
category = 'ABERRATION',
configuration = 'show_aberration',
value = as.character(report$config$show$aberration),
stringsAsFactors = F
),
data.frame(
category = 'COEXPRESSION',
configuration = 'show_coexpression',
value = as.character(report$config$show$coexpression),
stringsAsFactors = F
),
data.frame(
category = 'LIGAND_RECEPTOR',
configuration = 'show_ligand_receptor',
value = as.character(report$config$show$ligand_receptor),
stringsAsFactors = F
),
data.frame(
category = 'PROGNOSTIC',
configuration = 'show_prognostic',
value = as.character(report$config$show$cancer_prognosis),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_title',
value = as.character(report$config$project_title),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_description',
value = as.character(report$config$project_description),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'project_owner',
value = as.character(report$config$project_owner),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'query_ignore_err',
value = as.character(report$config$query$ignore_err),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'query_id_type',
value = as.character(report$config$query$id_type),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'bgset_id_type',
value = as.character(report$config$bgset$id_type),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'floating_toc',
value = as.character(report$config$rmarkdown$floating_toc),
stringsAsFactors = F
),
data.frame(
category = 'OTHER',
configuration = 'report_theme',
value = as.character(report$config$rmarkdown$theme),
stringsAsFactors = F
)
)
}
if (analysis_output == "query") {
if (is.data.frame(report$data$query$target)) {
if (NROW(report$data$query$target) > 0) {
target_df <- report$data$query$target |>
dplyr::mutate(
genename =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
)
}
}
}
if (analysis_output == "unknown_function") {
if (is.data.frame(report$data$unknown_function$hits_df)) {
if (NROW(report$data$unknown_function$hits_df) > 0) {
target_df <- report$data$unknown_function$hits_df |>
dplyr::rename(go_terms = .data$go_term_link) |>
dplyr::mutate(
annotation_source = "Gene Ontology (2022-12)/NCBI Gene/UniProt (2022_05)",
version = NA) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
) |>
dplyr::mutate(
go_terms =
stringr::str_trim(
textclean::replace_html(.data$go_terms)
)
) |>
dplyr::mutate(
gene_summary =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$gene_summary)
)
)
)
}
}
}
if (analysis_output == "cancer_association") {
if (is.data.frame(report$data$disease$target)) {
if (NROW(report$data$disease$target) > 0) {
target_df <- report$data$disease$target |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::select(-c("cancer_association_links",
"disease_association_links",
"cancergene_evidence",
"gene_summary")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
if (analysis_output == "cancer_hallmark") {
if (is.data.frame(report$data$cancer_hallmark$target)) {
if (NROW(report$data$cancer_hallmark$target) > 0) {
target_df <- report$data$cancer_hallmark$target |>
dplyr::mutate(
symbol =
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
)
) |>
dplyr::select(-c("literature_support"))
}
}
}
if (analysis_output == "drug_known") {
if (is.data.frame(report$data$drug$target_drugs)) {
if (NROW(report$data$drug$target_drugs)) {
target_df <- report$data$drug$target_drugs |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
) |>
dplyr::mutate(
approved_drugs =
stringr::str_trim(
textclean::replace_html(.data$approved_drugs)
)
) |>
dplyr::mutate(
drugs_late_phase =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$drugs_late_phase)
)
),
" , ",
", "
)
) |>
dplyr::mutate(
drugs_early_phase =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$drugs_early_phase)
)
),
" , ",
", "
)
)
}
}
}
if (analysis_output == "drug_tractability") {
if (is.data.frame(report$data$drug$tractability_ab) &
is.data.frame(report$data$drug$tractability_sm)) {
if (NROW(report$data$drug$tractability_ab) |
NROW(report$data$drug$tractability_sm)) {
target_df <- data.frame()
if (NROW(report$data$drug$tractability_sm)) {
df <- report$data$drug$tractability_sm |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::rename(tractability_category = "SM_tractability_category",
tractability_support = "SM_tractability_support") |>
dplyr::mutate(tractability_drugtype = "Small molecules/compounds") |>
dplyr::select(c("annotation_source", "version",
"tractability_drugtype"),
dplyr::everything()) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
) |>
dplyr::mutate(
tractability_support =
stringr::str_trim(
textclean::replace_html(.data$tractability_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
if (NROW(report$data$drug$tractability_ab)) {
df <- report$data$drug$tractability_ab |>
dplyr::mutate(
annotation_source = report$config$resources$opentargets$name,
version = report$config$resources$opentargets$version) |>
dplyr::rename(tractability_category = .data$AB_tractability_category,
tractability_support = .data$AB_tractability_support) |>
dplyr::mutate(tractability_drugtype = "Antibody") |>
dplyr::select(c("annotation_source", "version",
"tractability_drugtype"),
dplyr::everything()) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$symbol)
)
) |>
dplyr::mutate(
tractability_support =
stringr::str_trim(
textclean::replace_html(.data$tractability_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "ligand_receptor") {
for (c in c('secreted_signaling','cell_cell_contact',
'ecm_receptor')) {
if (is.data.frame(report$data$ligand_receptor[[c]])) {
if (NROW(report$data$ligand_receptor[[c]]) > 0) {
df <- report$data$ligand_receptor[[c]] |>
dplyr::mutate(
annotation_source = report$config$resources[['cellchatdb']]$name,
version = report$config$resources[['cellchatdb']]$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
literature_support =
stringr::str_trim(
textclean::replace_html(.data$literature_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "protein_domain") {
if (is.data.frame(report$data$protein_domain$target)) {
if (NROW(report$data$protein_domain$target) > 0) {
df <- report$data$protein_domain$target |>
dplyr::mutate(
annotation_source = report$config$resources[['pfam']]$name,
version = report$config$resources[['pfam']]$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
protein_domain =
stringr::str_trim(
textclean::replace_html(.data$protein_domain)
),
target_genes =
stringr::str_trim(
textclean::replace_html(.data$target_genes)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (analysis_output == "protein_complex") {
for (c in c('omnipath','humap2')) {
if (is.data.frame(report$data$protein_complex[[c]])) {
if (NROW(report$data$protein_complex[[c]]) > 0) {
res_name <- c
if(c == 'omnipath'){
res_name <- 'omnipathr'
}
df <- report$data$protein_complex[[c]] |>
dplyr::mutate(
annotation_source = report$config$resources[[res_name]]$name,
version = report$config$resources[[res_name]]$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
complex_genes =
stringr::str_replace_all(
stringr::str_squish(
stringr::str_trim(
textclean::replace_html(.data$complex_genes)
)
),
" , ",
", "
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (NROW(target_df) > 0 &
"literature" %in% colnames(target_df)) {
target_df <- target_df |>
dplyr::mutate(
literature =
stringr::str_trim(
textclean::replace_html(.data$literature)
)
) |>
dplyr::mutate(
complex_name =
stringr::str_trim(
textclean::replace_html(.data$complex_name)
)
)
}
}
if (analysis_output == "prognostic_association_I") {
if (is.data.frame(report$data$cancer_prognosis$hpa$assocs)) {
if (NROW(report$data$cancer_prognosis$hpa$assocs) > 0) {
target_df <- report$data$cancer_prognosis$hpa$assocs |>
dplyr::mutate(
annotation_source = report$config$resources$hpa$name,
version = report$config$resources$hpa$version) |>
dplyr::select(c("annotation_source",
"version"),
dplyr::everything()) |>
dplyr::mutate(
tumor_types =
stringr::str_trim(
textclean::replace_html(.data$tumor_types)
)
)
}
}
}
if (analysis_output == "synthetic_lethality") {
for (t in c('single_pair_member','both_in_pair')) {
if (is.data.frame(report$data$synleth[[t]])) {
if (NROW(report$data$synleth[[t]]) > 0) {
df <- report$data$synleth[[t]] |>
dplyr::mutate(
annotation_source = "De Kegel et al., Cell Systems, 2021",
version = "v1") |>
dplyr::mutate(feature_type = t) |>
dplyr::mutate(
genename_A =
stringr::str_trim(
textclean::replace_html(.data$genename_A)
)
) |>
dplyr::mutate(
genename_B =
stringr::str_trim(
textclean::replace_html(.data$genename_B)
)
) |>
dplyr::arrange(.data$feature_type,
dplyr::desc(.data$prediction_score)) |>
dplyr::select(c("annotation_source",
"version",
"feature_type"), dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "prognostic_association_II") {
for (t in c('cna','mut','exp','meth')) {
if (is.data.frame(report$data$cancer_prognosis$km_cshl$assocs[[t]])) {
if (NROW(report$data$cancer_prognosis$km_cshl$assocs[[t]]) > 0) {
df <- report$data$cancer_prognosis$km_cshl$assocs[[t]] |>
dplyr::mutate(
annotation_source = report$config$resources$tcga_survival$name,
version = report$config$resources$tcga_survival$version) |>
dplyr::mutate(feature_type = t) |>
dplyr::arrange(.data$feature_type, .data$z_score) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "coexpression") {
## co-expression
if (is.data.frame(report$data$tcga$coexpression)) {
if (NROW(report$data$tcga$coexpression) > 0) {
target_df <- report$data$tcga$coexpression |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::rename(tcga_cohort = "tumor") |>
dplyr::select(-c("entrezgene")) |>
dplyr::rename(partner_oncogene = "oncogene",
partner_tumor_suppressor = "tumor_suppressor",
partner_cancer_driver = "cancer_driver") |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "regulatory") {
## regulatory interactions
for (c in c('pancancer','global')) {
if (is.data.frame(report$data$regulatory$interactions[[c]])) {
if (NROW(report$data$regulatory$interactions[[c]]) > 0) {
df <- report$data$regulatory$interactions[[c]] |>
dplyr::mutate(
dorothea_collection = c,
annotation_source = report$config$resources$dorothea$name,
version = report$config$resources$dorothea$version) |>
dplyr::select(c("annotation_source",
"version",
"dorothea_collection"),
dplyr::everything()) |>
dplyr::mutate(
target_name =
stringr::str_trim(
textclean::replace_html(.data$target_name)
)
) |>
dplyr::mutate(
regulator_name =
stringr::str_trim(
textclean::replace_html(.data$regulator_name)
)
) |>
dplyr::mutate(
literature_support =
stringr::str_trim(
textclean::replace_html(.data$literature_support)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "recurrent_variants") {
if (is.data.frame(report$data$tcga$recurrent_variants)) {
if (NROW(report$data$tcga$recurrent_variants) > 0) {
df <-
report$data$tcga$recurrent_variants
colnames(df) <- tolower(colnames(df))
df <- as.data.frame(
df |>
dplyr::mutate(
site_recurrence = as.numeric(.data$site_recurrence)
) |>
dplyr::arrange(
dplyr::desc(.data$total_recurrence),
dplyr::desc(.data$site_recurrence)) |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::mutate(
ensembl_gene_id =
stringr::str_trim(
textclean::replace_html(.data$ensembl_gene_id)
)
) |>
dplyr::mutate(
ensembl_transcript_id =
stringr::str_trim(
textclean::replace_html(.data$ensembl_transcript_id)
)
) |>
dplyr::mutate(
protein_domain =
stringr::str_trim(
textclean::replace_html(.data$protein_domain)
)
) |>
dplyr::mutate(
cosmic_mutation_id =
stringr::str_trim(
textclean::replace_html(.data$cosmic_mutation_id)
)
) |>
dplyr::mutate(
site_recurrence = paste(.data$primary_site,
.data$site_recurrence, sep =":")
) |>
dplyr::group_by(
.data$symbol, .data$consequence,
.data$protein_change, .data$protein_domain,
.data$mutation_hotspot,
.data$loss_of_function,
.data$ensembl_gene_id,
.data$ensembl_transcript_id,
.data$total_recurrence,
.data$cosmic_mutation_id
) |>
dplyr::summarise(site_recurrence = paste(
.data$site_recurrence, collapse = ", "
), .groups = "drop") |>
dplyr::arrange(
dplyr::desc(.data$total_recurrence)
)
)
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
if (analysis_output == "aberration") {
## cna aberrations
for (t in c('cna_ampl','cna_homdel')) {
if (is.data.frame(report$data$tcga$aberration$table[[t]])) {
if (NROW(report$data$tcga$aberration$table[[t]]) > 0) {
df <-
report$data$tcga$aberration$table[[t]] |>
dplyr::mutate(
annotation_source = report$config$resources$tcga$name,
version = report$config$resources$tcga$version) |>
dplyr::mutate(
symbol =
stringr::str_trim(
textclean::replace_html(.data$gene)
)
) |>
dplyr::select(-c("gene")) |>
dplyr::select(c("annotation_source", "version",
"symbol"), dplyr::everything())
target_df <- target_df |>
dplyr::bind_rows(df)
}
}
}
}
if (analysis_output == "subcellcomp") {
if (is.data.frame(report$data$subcellcomp$all)) {
if (NROW(report$data$subcellcomp$all) > 0) {
target_df <- report$data$subcellcomp$all |>
dplyr::mutate(
annotation_source = report$config$resources$compartments$name,
version = report$config$resources$compartments$version) |>
dplyr::select(-c("ggcompartment")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything()) |>
dplyr::mutate(
compartment =
stringr::str_trim(
textclean::replace_html(.data$compartment)
)
) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
if (analysis_output == "enrichment") {
enrichment_df <- data.frame()
for (e in c('go','wikipathway','kegg','msigdb','netpath')) {
if (NROW(report$data$enrichment[[e]]) > 0) {
enrichment_df <- enrichment_df |>
dplyr::bind_rows(
report$data$enrichment[[e]] |>
dplyr::mutate(
annotation_source = report$config$resources[[e]]$name,
version = report$config$resources[[e]]$version) |>
dplyr::rename(category = "db",
entrezgene = "gene_id")
) |>
dplyr::select(-c("gene_symbol_link",
"description_link")) |>
dplyr::select(c("annotation_source", "version",
"category", "description"),
dplyr::everything())
}
}
target_df <- enrichment_df
}
if (analysis_output == "fitness_scores") {
if (is.data.frame(report$data$fitness$fitness_scores$targets)) {
if (NROW(report$data$fitness$fitness_scores$targets) > 0) {
target_df <- report$data$fitness$fitness_scores$targets |>
dplyr::mutate(
annotation_source = report$config$resources$depmap$name,
version = report$config$resources$depmap$version) |>
dplyr::select(-c("symbol_link_ps", "model_link_ps",
"n_gene")) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "fitness_prioritized") {
if (is.data.frame(report$data$fitness$target_priority_scores$targets)) {
if (NROW(report$data$fitness$target_priority_scores$targets) > 0) {
target_df <- report$data$fitness$target_priority_scores$targets |>
dplyr::mutate(
annotation_source = report$config$resources$depmap$name,
version = report$config$resources$depmap$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "ppi_string") {
target_df <- data.frame()
if (is.data.frame(report$data$ppi$string$complete_network$edges)) {
if (NROW(report$data$ppi$string$complete_network$edges) > 0) {
target_df <- report$data$ppi$string$complete_network$edges |>
dplyr::filter(!is.na(.data$entrezgene_A) &
!is.na(.data$entrezgene_B)) |>
dplyr::arrange(dplyr::desc(.data$score)) |>
dplyr::rename(is_target_A = "querynode_A",
is_target_B = "querynode_B",
string_score = "score",
string_nscore = "nscore",
string_fscore = "fscore",
string_pscore = "pscore",
string_ascore = "ascore",
string_escore = "escore",
string_dscore = "dscore",
string_tscore = "tscore"
) |>
dplyr::select(c("symbol_A",
"symbol_B",
"is_target_A",
"is_target_B",
"string_score",
"string_nscore",
"string_fscore",
"string_pscore",
"string_ascore",
"string_escore",
"string_dscore",
"string_tscore")) |>
dplyr::mutate(
annotation_source = report$config$resources$string$name,
version = report$config$resources$string$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "ppi_biogrid") {
target_df <- data.frame()
if (is.data.frame(report$data$ppi$biogrid$complete_network$edges)) {
if (NROW(report$data$ppi$biogrid$complete_network$edges) > 0) {
target_df <- report$data$ppi$biogrid$complete_network$edges |>
dplyr::filter(!is.na(.data$entrezgene_A) &
!is.na(.data$entrezgene_B)) |>
dplyr::rename(is_target_A = "querynode_A",
is_target_B = "querynode_B",
biogrid_evidence = "evidence",
biogrid_num_evidence = "num_evidence_items"
) |>
dplyr::select(c("symbol_A",
"symbol_B",
"is_target_A",
"is_target_B",
"biogrid_evidence",
"biogrid_num_evidence")) |>
dplyr::arrange(
dplyr::desc(.data$biogrid_num_evidence)) |>
dplyr::mutate(
annotation_source = report$config$resources$biogrid$name,
version = report$config$resources$biogrid$version) |>
dplyr::select(c("annotation_source", "version"),
dplyr::everything())
}
}
}
if (analysis_output == "cell_tissue") {
target_df <- data.frame()
for (e in c("tissue_enrichment", "scRNA_enrichment")) {
if (is.data.frame(report$data$cell_tissue[[e]]$per_gene)) {
if (NROW(report$data$cell_tissue[[e]]$per_gene) > 0) {
if (e == "tissue_enrichment") {
df <-
report$data$cell_tissue[[e]]$per_gene |>
dplyr::mutate(
annotation_source = report$config$resources$gtex$name,
version = report$config$resources$gtex$version,
category = stringr::str_replace(e,"_enrichment","")) |>
dplyr::select(c("annotation_source",
"version",
"category"),
dplyr::everything()) |>
dplyr::rename(tissue_or_celltype = "tissue")
} else {
df <-
report$data$cell_tissue[[e]]$per_gene |>
dplyr::mutate(
annotation_source = report$config$resources$hpa$name,
version = report$config$resources$hpa$version,
category = stringr::str_replace(e,"_enrichment","")) |>
dplyr::select(c("annotation_source",
"version",
"category"), dplyr::everything()) |>
dplyr::rename(tissue_or_celltype = "cell_type")
}
target_df <- target_df |>
dplyr::bind_rows(df) |>
dplyr::mutate(
genename =
stringr::str_trim(
textclean::replace_html(.data$genename)
)
)
}
}
}
}
if (nrow(target_df) > 0) {
analysis_output = stringr::str_replace(
analysis_output,"_ps_","_")
openxlsx::addWorksheet(workbook,
sheetName = toupper(analysis_output))
## set automatic column widths
openxlsx::setColWidths(workbook,
sheet = toupper(analysis_output),
cols = 1:ncol(target_df),
widths = "auto")
## write with default Excel Table style
openxlsx::writeDataTable(workbook,
sheet = toupper(analysis_output),
x = target_df,
startRow = 1,
startCol = 1,
colNames = TRUE,
tableStyle = tableStyle)
}
return(workbook)
}
file_is_writable <- function(path) {
assertthat::is.string(path) &&
file.exists(path) &&
assertthat::is.writeable(path)
}
#' Tidy eval helpers
#'
#' <https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html>
#'
#' @name tidyeval
#' @keywords internal
#' @importFrom rlang .data :=
NULL
#' GSEAbase imports
#'
#' @name gseabase
#' @keywords internal
#' @importFrom GSEABase GeneSet SymbolIdentifier ENSEMBLIdentifier EntrezIdentifier
NULL
utils::globalVariables(c("."))
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