From 9ebc78e58592fa6dc29905358862ebd45ee28c67 Mon Sep 17 00:00:00 2001 From: igor Date: Tue, 14 Jan 2020 19:33:03 -0500 Subject: [PATCH] prepare for initial cran release --- CRAN-RELEASE | 2 + DESCRIPTION | 13 ++- LICENSE | 23 +---- LICENSE.md | 2 +- NAMESPACE | 8 ++ NEWS.md | 3 + R/functions.R | 159 ++++++++++++++++++++++++++------ R/sysdata.rda | Bin 671212 -> 685580 bytes README.md | 25 +++-- cran-comments.md | 19 ++++ data-raw/download-markers.R | 59 ++++++------ man/clustermole_enrichment.Rd | 6 +- man/clustermole_markers.Rd | 14 ++- man/clustermole_overlaps.Rd | 24 +++++ man/read_gmt.Rd | 26 ++++++ tests/testthat.R | 4 +- tests/testthat/test-functions.R | 101 +++++++++++++++----- vignettes/clustermole-intro.Rmd | 100 ++++++++++++++------ 18 files changed, 431 insertions(+), 157 deletions(-) create mode 100644 CRAN-RELEASE create mode 100644 NEWS.md create mode 100644 cran-comments.md create mode 100644 man/clustermole_overlaps.Rd create mode 100644 man/read_gmt.Rd diff --git a/CRAN-RELEASE b/CRAN-RELEASE new file mode 100644 index 0000000..e36eb10 --- /dev/null +++ b/CRAN-RELEASE @@ -0,0 +1,2 @@ +This package was submitted to CRAN on 2020-01-14. +Once it is accepted, delete this file and tag the release (commit f7d2e89092). diff --git a/DESCRIPTION b/DESCRIPTION index 56a7400..97e1937 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,24 +1,27 @@ Type: Package -Package: ClusterMole -Title: A Collection of Cell Type Markers -Version: 0.0.0.9000 +Package: clustermole +Title: Unbiased Cell Type Identification of Single-Cell Transcriptomic Data +Version: 1.0.0 Authors@R: person(given = "Igor", family = "Dolgalev", role = c("aut", "cre"), email = "igor.dolgalev@nyumc.org") -Description: A collection of cell type markers. +Description: A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data involves clustering of cells. Assignment of cell type labels to those clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging if you are not familiar with all the captured subpopulations or have unexpected contaminants. 'clustermole' provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them. License: MIT + file LICENSE URL: https://github.com/igordot/clustermole +BugReports: https://github.com/igordot/clustermole/issues Depends: R (>= 3.4) Imports: dplyr, GSVA (>= 1.26.0), magrittr, + methods, rlang (>= 0.1.2), tibble, - tidyr + tidyr, + utils Suggests: covr, roxygen2, diff --git a/LICENSE b/LICENSE index 15bc72f..dffa260 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,2 @@ -MIT License - -Copyright (c) 2019 - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. +YEAR: 2019-2020 +COPYRIGHT HOLDER: Igor Dolgalev diff --git a/LICENSE.md b/LICENSE.md index 15bc72f..9c39301 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2019 +Copyright (c) 2019-2020 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/NAMESPACE b/NAMESPACE index 78e93f1..1428aac 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,9 +3,17 @@ export("%>%") export(clustermole_enrichment) export(clustermole_markers) +export(clustermole_overlaps) +export(read_gmt) import(dplyr) +import(methods) +import(utils) importFrom(GSVA,gsva) importFrom(magrittr,"%>%") importFrom(rlang,.data) +importFrom(stats,p.adjust) +importFrom(stats,phyper) importFrom(tibble,as_tibble) +importFrom(tibble,enframe) importFrom(tidyr,gather) +importFrom(tidyr,unnest) diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 0000000..a82033a --- /dev/null +++ b/NEWS.md @@ -0,0 +1,3 @@ +# clustermole 1.0.0 + +* Initial CRAN submission. diff --git a/R/functions.R b/R/functions.R index 03f3e92..74d769a 100644 --- a/R/functions.R +++ b/R/functions.R @@ -1,21 +1,104 @@ -#' Retrieve a table of all cell type markers +#' Retrieve the available cell type markers #' -#' @return a data frame of markers with one gene per row +#' @param species Species for the appropriate gene symbol format: "hs" for human or "mm" for mouse. +#' +#' @return A data frame of markers with one gene per row. +#' @import dplyr #' @export #' #' @examples -#' markers_tbl <- clustermole_markers() -clustermole_markers <- function() { - markers +#' markers <- clustermole_markers() +#' head(markers) +clustermole_markers <- function(species = "hs") { + m_tbl <- clustermole_markers_tbl + if (species == "hs") { + m_tbl %>% + dplyr::select(-.data$gene_mm) %>% + dplyr::rename(gene = .data$gene_hs) + } else if (species == "mm") { + m_tbl %>% + dplyr::select(-.data$gene_hs) %>% + dplyr::rename(gene = .data$gene_mm) + } else { + stop("unknown species") + } +} + +#' Perform cell type overrepresentation analysis for a set of genes +#' +#' @param genes A vector of genes. +#' @param species Species: "hs" for human or "mm" for mouse. +#' +#' @return A data frame of enrichment results with hypergeometric test p-values. +#' @import methods +#' @import dplyr +#' @importFrom tibble as_tibble +#' @importFrom tidyr gather +#' @importFrom stats phyper p.adjust +#' @export +#' +#' @examples +#' my_genes <- c("CD2", "CD3D", "CD3E", "CD3G", "TRAC", "TRBC2", "LTB") +#' my_overlaps <- clustermole_overlaps(genes = my_genes, species = "hs") +#' head(my_overlaps) +clustermole_overlaps <- function(genes, species) { + + # check that the genes vector seems reasonable + if (!is(genes, "character")) { + stop("genes is not a character vector") + } + genes <- unique(genes) + if (length(genes) < 5) { + stop("too few genes") + } + if (length(genes) > 5000) { + stop("too many genes") + } + + # retrieve markers + markers_tbl <- clustermole_markers(species = species) + markers_list <- split(x = markers_tbl$gene, f = markers_tbl$celltype_full) + celltypes_tbl <- + markers_tbl %>% + dplyr::select(-dplyr::starts_with("gene")) %>% + dplyr::distinct() + + # check that input genes overlap species genes + all_genes <- unique(markers_tbl$gene) + genes <- intersect(genes, all_genes) + if (length(genes) < 5) { + stop("the genes do not appear to correspond to the given species") + } + + # run the enrichment analysis + overlaps_mat <- + sapply(markers_list, function(celltype_genes) { + n_overlap <- length(intersect(genes, celltype_genes)) + n_query <- length(genes) + n_celltype <- length(celltype_genes) + n_all <- length(all_genes) + # phyper(success-in-sample, success-in-bg, fail-in-bg, sample-size) + p_val <- phyper(n_overlap - 1, n_celltype, n_all - n_celltype, n_query, lower.tail = FALSE) + c("overlap" = n_overlap, "p_value" = p_val, "fdr" = 1) + }) + overlaps_mat <- t(overlaps_mat) + overlaps_mat[, "fdr"] <- p.adjust(overlaps_mat[, "p_value"], method = "fdr") + + # clean up the enrichment table + overlaps_tbl <- tibble::as_tibble(overlaps_mat, rownames = "celltype_full") + overlaps_tbl <- dplyr::inner_join(celltypes_tbl, overlaps_tbl, by = "celltype_full") + overlaps_tbl <- dplyr::arrange(overlaps_tbl, .data$fdr, .data$p_value, .data$celltype_full) + overlaps_tbl } #' Perform cell type enrichment for a given gene expression matrix #' -#' @param expr_mat expression matrix (logCPMs, logFPKMs, or logTPMs) -#' @param species species ("hs" for human or "mm" for mouse) +#' @param expr_mat Expression matrix (logCPMs, logFPKMs, or logTPMs) with genes as rows. +#' @param species Species: "hs" for human or "mm" for mouse. #' -#' @return a data frame of enrichment results +#' @return A data frame of enrichment results. +#' @import methods #' @import dplyr #' @importFrom tibble as_tibble #' @importFrom tidyr gather @@ -38,37 +121,55 @@ clustermole_enrichment <- function(expr_mat, species) { expr_mat <- expr_mat[rowMeans(expr_mat) > 0, ] # retrieve markers - markers_tbl <- clustermole_markers() + markers_tbl <- clustermole_markers(species = species) + markers_list <- split(x = markers_tbl$gene, f = markers_tbl$celltype_full) celltypes_tbl <- markers_tbl %>% - select(.data$db, .data$species, .data$organ, .data$celltype, .data$celltype_long, .data$n_genes) %>% - distinct() - - # create a markers list for gene set enrichment - if (species == "hs") { - markers_list <- split(x = markers_tbl$gene_h, f = markers_tbl$celltype_long) - } - if (species == "mm") { - markers_list <- split(x = markers_tbl$gene_m, f = markers_tbl$celltype_long) - } + dplyr::select(-dplyr::starts_with("gene")) %>% + dplyr::distinct() # run the actual enrichment analysis - gsva_mat <- gsva( + gsva_mat <- GSVA::gsva( expr = expr_mat, gset.idx.list = markers_list, - method = "gsva", kcdf = "Gaussian", verbose = FALSE + method = "gsva", kcdf = "Gaussian", parallel.sz = 1, verbose = FALSE ) # clean up the enrichment table gsva_tbl <- gsva_mat %>% round(8) %>% - as_tibble(rownames = "celltype_long") %>% - gather(key = "cluster", value = "score", -.data$celltype_long) %>% - select(.data$cluster, .data$celltype_long, .data$score) %>% - group_by(.data$cluster) %>% - top_n(n = 50) %>% - ungroup() %>% - inner_join(celltypes_tbl, by = "celltype_long") %>% - arrange(.data$cluster, desc(.data$score)) + tibble::as_tibble(rownames = "celltype_full") %>% + tidyr::gather(key = "cluster", value = "score", -.data$celltype_full) %>% + dplyr::select(.data$cluster, .data$celltype_full, .data$score) %>% + dplyr::group_by(.data$cluster) %>% + dplyr::top_n(n = 50) %>% + dplyr::ungroup() %>% + dplyr::inner_join(celltypes_tbl, by = "celltype_full") %>% + dplyr::arrange(.data$cluster, desc(.data$score)) gsva_tbl } + +#' Read a GMT file into a data frame +#' +#' @param file A connection object or a character string (can be a URL). +#' @param geneset_label Column name for gene sets (first column of the GMT file) in the output data frame. +#' @param gene_label Column name for genes (variable columns of the GMT file) in the output data frame. +#' +#' @return A data frame with gene sets as the first column and genes as the second column (one gene per row). +#' @import utils +#' @importFrom tibble enframe +#' @importFrom tidyr unnest +#' @export +#' +#' @examples +#' gmt <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt" +#' gmt_tbl <- read_gmt(gmt) +#' head(gmt_tbl) +read_gmt <- function(file, geneset_label = "celltype", gene_label = "gene") { + gmt_split <- strsplit(readLines(file), "\t") + gmt_list <- lapply(gmt_split, tail, -2) + names(gmt_list) <- sapply(gmt_split, head, 1) 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zk1y#-3u8rkrnVj&mu^8Xtu25Cj(Zh|i--aa33S0ErHUS55~wE}AQklr{wfL-4~(hnHh0*;LeP9LVP>8gJL$CGDYNnLc1AGoM?-y*9Hkg-Ftfz{wJZ zbt^pCYm+fKT(1><0F#nXZV6NNt@+JB5a8i@ACH6F>$;8R?OCb**8)2O7da#rEtx?` zBM+9E^W~*I5KgYx{$vR-lU@Bx5^-eDgOcm7g`T$Jse#wp`sA9oBBY<+s5pnf=q{BHSW zE6zvAQ See, children, the misguided Mole. > He lives down in a deep, dark hole; @@ -15,29 +18,33 @@ > > -- Oliver Herford -Cell type annotation of single-cell RNA sequencing (scRNA-seq) data typically requires a reference dataset, but finding an appropriate one may be challenging. -ClusterMole is an R package that provides a collection of cell type markers for thousands of human and mouse cell populations sourced from a variety of databases. +A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data involves clustering of cells. Assignment of cell type labels to those clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging if you are not familiar with all the captured subpopulations or have unexpected contaminants. `clustermole` is an R package that provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them. -Install ClusterMole: +Install clustermole (development version): ```r BiocManager::install("igordot/clustermole", update = FALSE) ``` +Load clustermole: + +```r +library(clustermole) +``` + Retrieve a table of all cell type markers: ```r -markers_tbl = clustermole_markers() -head(markers_tbl) +clustermole_markers(genes, species) ``` -See a summary of the available cell types: +Perform cell type overrepresentation analysis for a given set of genes: ```r -markers_tbl %>% distinct(celltype, organ, db) +clustermole_overlaps(expr_mat, species) ``` -Perform cell type enrichment for a given gene expression matrix: +Perform cell type enrichment for a given full gene expression matrix: ```r clustermole_enrichment(expr_mat, species) diff --git a/cran-comments.md b/cran-comments.md new file mode 100644 index 0000000..72f0d9f --- /dev/null +++ b/cran-comments.md @@ -0,0 +1,19 @@ +## Resubmission + +This is a resubmission. In this version I have: + +* Fixed LICENSE based on the CRAN template. + + +## Test environments + +* local OS X install, R 3.6.1 +* travis-ci: R 3.4, R-release, R-devel +* win-builder: R-devel +* rchk: unbuntu-rchk platform on R-hub + +## R CMD check results + +0 errors | 0 warnings | 0 notes + +* This is a new release. diff --git a/data-raw/download-markers.R b/data-raw/download-markers.R index 631bd0f..aa30640 100644 --- a/data-raw/download-markers.R +++ b/data-raw/download-markers.R @@ -10,7 +10,7 @@ library(stringdist) library(usethis) -# orthologs --------------------------------------------------------------- +# HCOP gene orthologs ----- # import orthologs table hcop_source <- "ftp://ftp.ebi.ac.uk/pub/databases/genenames/hcop/human_mouse_hcop_fifteen_column.txt.gz" @@ -24,29 +24,24 @@ valid_genes <- c(hcop_all$human_symbol, hcop_all$mouse_symbol) %>% # format orthologs table hcop_clean <- hcop_all %>% - select(gene_h = human_symbol, gene_m = mouse_symbol, sources = support) %>% + select(gene_hs = human_symbol, gene_mm = mouse_symbol, sources = support) %>% mutate(num_sources = str_count(sources, ",") + 1) %>% - mutate(distance = stringdist(toupper(gene_h), toupper(gene_m), method = "jw")) %>% - group_by(gene_h) %>% + mutate(distance = stringdist(toupper(gene_hs), toupper(gene_mm), method = "jw")) %>% + group_by(gene_hs) %>% top_n(1, num_sources) %>% top_n(-1, distance) %>% ungroup() %>% distinct() %>% - select(gene_h, gene_m) + select(gene_hs, gene_mm) -# SCSig ------------------------------------------------------------------- +# SCSig signatures ----- # link: http://www.gsea-msigdb.org/gsea/msigdb/supplementary_genesets.jsp # download gene signatures scsig_source <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt" -scsig_list <- strsplit(readLines(scsig_source), "\t") -scsig_all <- lapply(scsig_list, tail, -2) -names(scsig_all) <- sapply(scsig_list, head, 1) -scsig_all <- scsig_all %>% - enframe(name = "celltype", value = "gene") %>% - unnest(gene) +scsig_all <- clustermole::read_gmt(scsig_source) # download metadata (contains organism and organ) scsig_metadata_source <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.v1.0.metadata.xls" @@ -67,7 +62,7 @@ scsig_clean <- select(db, species, organ, celltype, gene) -# PanglaoDB --------------------------------------------------------------- +# PanglaoDB signatures ----- # link: https://panglaodb.se/ @@ -85,7 +80,7 @@ panglao_clean <- select(db, species, organ, celltype, gene) -# CellMarker -------------------------------------------------------------- +# CellMarker signatures ----- # link: http://biocc.hrbmu.edu.cn/CellMarker/ @@ -108,7 +103,7 @@ cellmarker_clean <- separate_rows(gene, sep = ",") -# SaVanT ------------------------------------------------------------------ +# SaVanT signatures ----- # link: http://newpathways.mcdb.ucla.edu/savant-dev/ @@ -152,7 +147,7 @@ savant_clean <- select(db, species, organ, celltype, gene) -# xCell ------------------------------------------------------------------- +# xCell signatures ----- # link: http://xcell.ucsf.edu/ @@ -178,7 +173,7 @@ xcell_clean <- select(db, species, organ, celltype, gene) -# combine ----------------------------------------------------------------- +# combine signatures ----- # combine markers <- @@ -207,35 +202,37 @@ markers <- species == "None" ~ "", TRUE ~ "" ), - celltype_long = str_c(celltype, organ, species, db, sep = " | "), - celltype_long = str_replace_all(celltype_long, "\\| \\|", "\\|"), - celltype_long = str_replace_all(celltype_long, "\\| \\|", "\\|") + celltype_full = str_c(celltype, organ, species, db, sep = " | "), + celltype_full = str_replace_all(celltype_full, "\\| \\|", "\\|"), + celltype_full = str_replace_all(celltype_full, "\\| \\|", "\\|") ) %>% - add_count(celltype_long, name = "n_genes") + add_count(celltype_full, name = "n_genes") markers <- markers %>% - filter(n_genes >= 5, n_genes <= 1500) %>% + filter(n_genes >= 5, n_genes <= 2000) %>% select(-gene, gene) %>% - arrange(celltype_long, gene) + arrange(celltype_full, gene) # add human/mouse gene symbols (listed as either in the original table) -markers <- left_join(markers, hcop_clean, by = c("gene" = "gene_m")) -markers <- left_join(markers, hcop_clean, by = c("gene" = "gene_h")) -markers <- markers %>% mutate(gene_h = if_else(is.na(gene_h), gene, gene_h)) -markers <- markers %>% mutate(gene_m = if_else(is.na(gene_m), gene, gene_m)) +markers <- left_join(markers, hcop_clean, by = c("gene" = "gene_mm")) +markers <- left_join(markers, hcop_clean, by = c("gene" = "gene_hs")) +markers <- markers %>% mutate(gene_hs = if_else(is.na(gene_hs), gene, gene_hs)) +markers <- markers %>% mutate(gene_mm = if_else(is.na(gene_mm), gene, gene_mm)) +markers <- markers %>% rename(gene_original = gene) # markers %>% pull(species) %>% table() # hist(markers$n_genes, breaks = 50, col = "gray20") -# markers %>% distinct(celltype_long, n_genes) %>% arrange(-n_genes) -# n_distinct(markers$celltype_long) +# markers %>% distinct(celltype_full, n_genes) %>% arrange(-n_genes) +# n_distinct(markers$celltype_full) -# Prepare package --------------------------------------------------------- +# prepare package ----- # create package data +clustermole_markers_tbl = markers use_data( - markers, + clustermole_markers_tbl, internal = TRUE, overwrite = TRUE, compress = "xz" diff --git a/man/clustermole_enrichment.Rd b/man/clustermole_enrichment.Rd index e900869..a50a970 100644 --- a/man/clustermole_enrichment.Rd +++ b/man/clustermole_enrichment.Rd @@ -7,12 +7,12 @@ clustermole_enrichment(expr_mat, species) } \arguments{ -\item{expr_mat}{expression matrix (logCPMs, logFPKMs, or logTPMs)} +\item{expr_mat}{Expression matrix (logCPMs, logFPKMs, or logTPMs) with genes as rows.} -\item{species}{species ("hs" for human or "mm" for mouse)} +\item{species}{Species: "hs" for human or "mm" for mouse.} } \value{ -a data frame of enrichment results +A data frame of enrichment results. } \description{ Perform cell type enrichment for a given gene expression matrix diff --git a/man/clustermole_markers.Rd b/man/clustermole_markers.Rd index d82a15e..457d290 100644 --- a/man/clustermole_markers.Rd +++ b/man/clustermole_markers.Rd @@ -2,16 +2,20 @@ % Please edit documentation in R/functions.R \name{clustermole_markers} \alias{clustermole_markers} -\title{Retrieve a table of all cell type markers} +\title{Retrieve the available cell type markers} \usage{ -clustermole_markers() +clustermole_markers(species = "hs") +} +\arguments{ +\item{species}{Species for the appropriate gene symbol format: "hs" for human or "mm" for mouse.} } \value{ -a data frame of markers with one gene per row +A data frame of markers with one gene per row. } \description{ -Retrieve a table of all cell type markers +Retrieve the available cell type markers } \examples{ -markers_tbl <- clustermole_markers() +markers <- clustermole_markers() +head(markers) } diff --git a/man/clustermole_overlaps.Rd b/man/clustermole_overlaps.Rd new file mode 100644 index 0000000..293896e --- /dev/null +++ b/man/clustermole_overlaps.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{clustermole_overlaps} +\alias{clustermole_overlaps} +\title{Perform cell type overrepresentation analysis for a set of genes} +\usage{ +clustermole_overlaps(genes, species) +} +\arguments{ +\item{genes}{A vector of genes.} + +\item{species}{Species: "hs" for human or "mm" for mouse.} +} +\value{ +A data frame of enrichment results with hypergeometric test p-values. +} +\description{ +Perform cell type overrepresentation analysis for a set of genes +} +\examples{ +my_genes <- c("CD2", "CD3D", "CD3E", "CD3G", "TRAC", "TRBC2", "LTB") +my_overlaps <- clustermole_overlaps(genes = my_genes, species = "hs") +head(my_overlaps) +} diff --git a/man/read_gmt.Rd b/man/read_gmt.Rd new file mode 100644 index 0000000..f714304 --- /dev/null +++ b/man/read_gmt.Rd @@ -0,0 +1,26 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/functions.R +\name{read_gmt} +\alias{read_gmt} +\title{Read a GMT file into a data frame} +\usage{ +read_gmt(file, geneset_label = "celltype", gene_label = "gene") +} +\arguments{ +\item{file}{A connection object or a character string (can be a URL).} + +\item{geneset_label}{Column name for gene sets (first column of the GMT file) in the output data frame.} + +\item{gene_label}{Column name for genes (variable columns of the GMT file) in the output data frame.} +} +\value{ +A data frame with gene sets as the first column and genes as the second column (one gene per row). +} +\description{ +Read a GMT file into a data frame +} +\examples{ +gmt <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt" +gmt_tbl <- read_gmt(gmt) +head(gmt_tbl) +} diff --git a/tests/testthat.R b/tests/testthat.R index cc7069e..0873144 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,4 +1,4 @@ library(testthat) -library(ClusterMole) +library(clustermole) -test_check("ClusterMole") +test_check("clustermole") diff --git a/tests/testthat/test-functions.R b/tests/testthat/test-functions.R index 6660ecf..dbd559c 100644 --- a/tests/testthat/test-functions.R +++ b/tests/testthat/test-functions.R @@ -1,24 +1,75 @@ +# clustermole_markers ----- + markers_tbl <- clustermole_markers() -test_that("clustermole_markers() output", { +test_that("clustermole_markers() default params", { expect_s3_class(markers_tbl, "tbl_df") expect_gt(nrow(markers_tbl), 100000) }) +test_that("clustermole_markers() wrong input", { + expect_error(clustermole_markers(species = "")) + expect_error(clustermole_markers(species = "x")) + expect_error(clustermole_markers(species = "?")) + expect_error(clustermole_markers(species = "*")) +}) + +markers_hs_tbl <- clustermole_markers(species = "hs") +test_that("clustermole_markers() human input", { + expect_s3_class(markers_hs_tbl, "tbl_df") + expect_gt(nrow(markers_hs_tbl), 100000) +}) + +markers_mm_tbl <- clustermole_markers(species = "mm") +test_that("clustermole_markers() mouse input", { + expect_s3_class(markers_mm_tbl, "tbl_df") + expect_gt(nrow(markers_mm_tbl), 100000) +}) + +# clustermole_overlaps ----- + +# gene list for human overrepresentation tests +gene_names <- unique(markers_hs_tbl$gene) +gene_names <- sample(gene_names) + +test_that("clustermole_overlaps() wrong input", { + expect_error(clustermole_overlaps(gene_names[1:3], species = "hs")) + expect_error(clustermole_overlaps(gene_names[1:10000], species = "hs")) + expect_error(clustermole_overlaps(c(gene_names[1:3], "X", "Y", "Z"), species = "hs")) + expect_error(clustermole_overlaps(as.list(gene_names[1:10]), species = "hs")) +}) + +test_that("clustermole_overlaps() human input", { + overlap_tbl <- clustermole_overlaps(genes = gene_names[1:50], species = "hs") + expect_s3_class(overlap_tbl, "tbl_df") + expect_gt(nrow(overlap_tbl), 100) +}) + +# gene list for mouse overrepresentation tests +gene_names <- unique(markers_mm_tbl$gene) +gene_names <- sample(gene_names) + +test_that("clustermole_overlaps() mouse input", { + overlap_tbl <- clustermole_overlaps(genes = gene_names[1:50], species = "mm") + expect_s3_class(overlap_tbl, "tbl_df") + expect_gt(nrow(overlap_tbl), 100) +}) + +# clustermole_enrichment ----- + # expression matrix for enrichment tests n_genes <- 10000 expr_mat <- matrix(rnbinom(n_genes * 5, size = 1, mu = 10), nrow = n_genes, ncol = 5) colnames(expr_mat) <- c("C1", "C2", "C3", "C4", "C5") -cpm_mat <- t(t(expr_mat) / (colSums(expr_mat) / 1e6)) +cpm_mat <- t(t(expr_mat) / colSums(expr_mat)) * 1e6 log_cpm_mat <- log2(cpm_mat + 0.1) # add human gene names to the expression matrix -gene_names <- dplyr::filter(markers_tbl, species == "Human") -gene_names <- unique(gene_names$gene) -gene_names <- sort(sample(gene_names, n_genes)) -rownames(expr_mat) <- gene_names -rownames(cpm_mat) <- gene_names -rownames(log_cpm_mat) <- gene_names +gene_names_hs <- unique(markers_hs_tbl$gene) +gene_names_hs <- sort(sample(gene_names_hs, n_genes)) +rownames(expr_mat) <- gene_names_hs +rownames(cpm_mat) <- gene_names_hs +rownames(log_cpm_mat) <- gene_names_hs test_that("clustermole_enrichment() wrong input", { expect_error(clustermole_enrichment(as.data.frame(log_cpm_mat), species = "hs")) @@ -26,24 +77,30 @@ test_that("clustermole_enrichment() wrong input", { expect_error(clustermole_enrichment(cpm_mat, species = "hs")) }) +enrich_hs_tbl <- clustermole_enrichment(expr_mat = log_cpm_mat, species = "hs") test_that("clustermole_enrichment() human input", { - enrich_tbl <- clustermole_enrichment(expr_mat = log_cpm_mat, species = "hs") - expect_s3_class(enrich_tbl, "tbl_df") - expect_gt(nrow(enrich_tbl), 100) - expect_equal(length(unique(enrich_tbl$cluster)), 5) + expect_s3_class(enrich_hs_tbl, "tbl_df") + expect_gt(nrow(enrich_hs_tbl), 100) + expect_equal(length(unique(enrich_hs_tbl$cluster)), 5) }) # add mouse gene names to the expression matrix -gene_names <- dplyr::filter(markers_tbl, species == "Mouse") -gene_names <- unique(gene_names$gene) -gene_names <- sort(sample(gene_names, n_genes)) -rownames(expr_mat) <- gene_names -rownames(cpm_mat) <- gene_names -rownames(log_cpm_mat) <- gene_names +gene_names_mm <- unique(markers_mm_tbl$gene) +gene_names_mm <- sort(sample(gene_names_mm, n_genes)) +rownames(expr_mat) <- gene_names_mm +rownames(cpm_mat) <- gene_names_mm +rownames(log_cpm_mat) <- gene_names_mm +enrich_mm_tbl <- clustermole_enrichment(expr_mat = log_cpm_mat, species = "mm") test_that("clustermole_enrichment() mouse input", { - enrich_tbl <- clustermole_enrichment(expr_mat = log_cpm_mat, species = "mm") - expect_s3_class(enrich_tbl, "tbl_df") - expect_gt(nrow(enrich_tbl), 100) - expect_equal(length(unique(enrich_tbl$cluster)), 5) + expect_s3_class(enrich_mm_tbl, "tbl_df") + expect_gt(nrow(enrich_mm_tbl), 100) + expect_equal(length(unique(enrich_mm_tbl$cluster)), 5) +}) + +# test read_gmt() +test_that("read_gmt() output", { + gmt_tbl <- read_gmt(file = "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt") + expect_s3_class(gmt_tbl, "tbl_df") + expect_gt(nrow(gmt_tbl), 1000) }) diff --git a/vignettes/clustermole-intro.Rmd b/vignettes/clustermole-intro.Rmd index e9dfe3d..e928ee1 100644 --- a/vignettes/clustermole-intro.Rmd +++ b/vignettes/clustermole-intro.Rmd @@ -1,10 +1,10 @@ --- -title: "Introduction to ClusterMole" +title: "Introduction to clustermole" output: rmarkdown::html_vignette: keep_md: true vignette: > - %\VignetteIndexEntry{Introduction to ClusterMole} + %\VignetteIndexEntry{Introduction to clustermole} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -16,61 +16,103 @@ knitr::opts_chunk$set( ) ``` +*Alternative title: blindly digging for cell types in scRNA-seq clusters with clustermole* + ## Overview -ClusterMole is an R package that provides a comprehensive collection of cell type markers for thousands of human and mouse cell populations sourced from a variety of databases. +A typical computational pipeline to process single-cell RNA sequencing (scRNA-seq) data involves clustering of cells. Assignment of cell type labels to those clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging if you are not familiar with all the captured subpopulations or have unexpected contaminants. `clustermole` is an R package that provides a comprehensive meta collection of cell identity markers for thousands of human and mouse cell types sourced from a variety of databases as well as methods to query them. + +The `clustermole` package includes three primary features: -## Installation +* cell type identification based on a set of marker genes (`clustermole_overlaps`) +* cell type identification based on a full expression matrix (`clustermole_enrichment`) +* a meta collection of cell type markers (`clustermole_markers`) + +## Usage -The package can be installed from its GitHub repo. Since Bioconductor +Install `clustermole` if it is not yet available on your system. ```{r install-package, eval=FALSE} -if (!requireNamespace("BiocManager", quietly = TRUE)) - install.packages("BiocManager") -if (!requireNamespace("remotes", quietly = TRUE)) - install.packages("remotes") +install.packages("clustermole") +``` + +Load `clustermole`. -BiocManager::install("igordot/clustermole", update = FALSE) +```{r load-package, message=FALSE} +library(clustermole) ``` -## Usage +### Overlap a set of genes with cell type markers -Load package. +If you have a set of genes (for example, cluster markers), you can perform overrepresentation analysis to see if they overlap any of the known cell type markers. -```{r load-package, message=FALSE} -library(ClusterMole) +```{r overlaps} +my_genes = c("CD2", "CD3D", "CD3E", "IL7R", "IL32", "LTB", "LDHB", "CCR7") +my_overlaps = clustermole_overlaps(genes = my_genes, species = "hs") +my_overlaps ``` -Retrieve a data frame of all cell type markers in the database. +### Determine relative enrichment of cell type markers in the input expression data + +If you have a table of expression values (for example, average expression across clusters), you can perform cell type enrichment based on a given gene expression matrix (log-transformed CPM/TPM/FPKM values). + +```{r enrichment, eval=FALSE} +clustermole_enrichment(expr_mat = my_expr_mat, species = "hs") +``` + +### Retrieve cell type markers + +You can retrieve a data frame of all cell type markers in the database. ```{r markers} -markers = clustermole_markers() +markers = clustermole_markers(species = "hs") markers ``` -Each row contains a gene (human and mouse versions) and a cell type associated with it. +Each row contains a gene and a cell type associated with it. The `gene` column is the gene symbol (human or mouse versions can be retrieved) and the `celltype_full` column contains the full cell type string, including the species and the original database. -The `celltype_long` column contains the full cell type string, including the species and the original database. +If you need to convert the markers from a data frame to a list format for other applications, you can use `gene` as the values and `celltype_full` as the grouping variable. -```{r celltypes-long-head} -head(unique(markers$celltype_long), 20) +```{r celltypes-list} +markers_list = split(x = markers$gene, f = markers$celltype_full) ``` -```{r celltypes-long-length} -length(unique(markers$celltype_long)) +## Collection details + +We will use `dplyr` to help with summary statistics. + +```{r load-dplyr, message=FALSE} +library(dplyr) ``` -You can use the `celltype_long` column as the grouping variable to convert the markers data frame to a list format for other applications such as gene set enrichment. +Retrieve a data frame of all cell type markers in the database. -```{r} -markers_list = split(x = markers$gene_h, f = markers$celltype_long) -head(markers_list) +```{r get-markers} +markers = clustermole_markers(species = "hs") +markers ``` -If you have a table of expression values, you can perform cell type enrichment based on a given gene expression matrix (log-transformed CPM/TPM/FPKM values). +Check the number of available cell types. -```{r enrichment, eval=FALSE} -clustermole_enrichment(expr_mat, species) +```{r celltypes-length} +markers %>% distinct(celltype_full) %>% nrow() +``` + +Check the number of available cell types per species (not available for every cell type). + +```{r count-species} +markers %>% distinct(celltype_full, species) %>% count(species, sort = TRUE) ``` +Check the number of available cell types per organ (not available for every cell type). + +```{r count-organ} +markers %>% distinct(celltype_full, organ) %>% count(organ, sort = TRUE) +``` + +Check package version. + +```{r package-version} +packageVersion("clustermole") +```

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