├── DESCRIPTION ├── ExampleData ├── figures │ ├── 2D_score_plot.png │ ├── Reference_heatmap.png │ ├── Reference_tSNE.png │ └── Target_heatmap.png ├── markers.rds ├── reference_clusters.rds ├── reference_gem.rds └── target_gem.rds ├── LICENSE.txt ├── NAMESPACE ├── R ├── find_markers.R ├── fit_GMM.R ├── normalization_functions.R ├── plots_functions.R ├── rank_signature_genes.R ├── read_data.R └── scID.R ├── README.md ├── _config.yml ├── assets └── images │ └── scID_pipeline.png ├── man ├── choose_training_set.Rd ├── counts_to_cpm.Rd ├── final_populations.Rd ├── find_markers.Rd ├── gmt_to_markers.Rd ├── loadfast.Rd ├── make_heatmap.Rd ├── normalize_gene.Rd ├── plot_score_2D.Rd ├── scID_weight.Rd └── scid_multiclass.Rd └── vignettes ├── Mapping_example.md └── introduction.Rmd /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: scID 2 | Title: geneset-guided identification of cell types using single cell RNA-seq data 3 | Version: 2.2 4 | Authors@R: c(person("Katerina", "Boufea", 5 | email = "katerina.boufea@outlook.com", 6 | role = c("aut", "cre")), 7 | person("Nizar N.", "Batada", 8 | role = c("aut"))) 9 | Description: scID is a method for geneset-guided identification of cell types at the level of individual cells, 10 | using single cell RNA-seq data. Given a gene expression matrix and a list of marker genes that 11 | define the population of interest, scID returns a matching score for each cell and the names of 12 | the identified matching cells. 13 | Depends: R (>= 3.4.0) 14 | License: GPL-3.0 15 | Encoding: UTF-8 16 | LazyData: true 17 | RoxygenNote: 7.1.2 18 | Suggests: knitr, 19 | rmarkdown 20 | VignetteBuilder: knitr 21 | Imports: Seurat, 22 | svMisc, 23 | pheatmap, 24 | ggplot2, 25 | mclust, 26 | scater, 27 | data.table, 28 | biomod2 29 | -------------------------------------------------------------------------------- /ExampleData/figures/2D_score_plot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/figures/2D_score_plot.png -------------------------------------------------------------------------------- /ExampleData/figures/Reference_heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/figures/Reference_heatmap.png -------------------------------------------------------------------------------- /ExampleData/figures/Reference_tSNE.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/figures/Reference_tSNE.png -------------------------------------------------------------------------------- /ExampleData/figures/Target_heatmap.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/figures/Target_heatmap.png -------------------------------------------------------------------------------- /ExampleData/markers.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/markers.rds -------------------------------------------------------------------------------- /ExampleData/reference_clusters.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/reference_clusters.rds -------------------------------------------------------------------------------- /ExampleData/reference_gem.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/reference_gem.rds -------------------------------------------------------------------------------- /ExampleData/target_gem.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/ExampleData/target_gem.rds -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(choose_training_set) 4 | export(counts_to_cpm) 5 | export(final_populations) 6 | export(find_markers) 7 | export(gmt_to_markers) 8 | export(loadfast) 9 | export(make_heatmap) 10 | export(normalize_gene) 11 | export(plot_score_2D) 12 | export(scID_weight) 13 | export(scid_multiclass) 14 | -------------------------------------------------------------------------------- /R/find_markers.R: -------------------------------------------------------------------------------- 1 | #' Function to extract cluster specific genes from reference clusters. 2 | #' This function uses MAST test as implemented in the Seurat package. 3 | #' 4 | #' @param reference_gem Data frame of gene expression (rows) per cell (columns) in reference data 5 | #' @param reference_clusters Named list of cluster IDs of reference cells 6 | #' @param logFC Log2FC threshold for extracting markers from reference clusters 7 | #' @param only.pos Logical to include negative markers in the cluster specific gene sets 8 | #' @param normalize_reference Logical to select if reference data need to be normalized (when raw counts have been provided) 9 | #' 10 | #' @return Data frame of cluster specific genes extracted 11 | #' 12 | #' @export 13 | find_markers <- function(reference_gem, reference_clusters, logFC, only.pos, normalize_reference) { 14 | library(Seurat) 15 | 16 | so_ref <- CreateSeuratObject(reference_gem) 17 | if (normalize_reference) { 18 | so_ref <- suppressMessages(NormalizeData(so_ref)) 19 | } 20 | so_ref <- suppressMessages(ScaleData(so_ref)) 21 | Idents(so_ref) <- as.factor(reference_clusters) 22 | markers <- suppressMessages(FindAllMarkers(so_ref, test.use = "MAST", only.pos = only.pos, logfc.threshold = logFC)) 23 | 24 | markers 25 | } 26 | -------------------------------------------------------------------------------- /R/fit_GMM.R: -------------------------------------------------------------------------------- 1 | #' Function to identify matching population from scID score by fitting 2 | #' a Gaussian finite mixture model from Mclust 3 | #' 4 | #' @param score List of scID scores for target cells 5 | 6 | #' @return List of matching cells 7 | #' @export 8 | final_populations <- function(score) { 9 | 10 | fit <- mclust::densityMclust(score) 11 | 12 | # Calculate average scID score per group of cells 13 | avgScore <- rep(NA, length(unique(fit$classification))) 14 | names(avgScore) <- unique(fit$classification) 15 | for (ID in names(avgScore)) avgScore[ID] <- mean(score[names(which(fit$classification == ID))]) 16 | 17 | matches <- names(fit$classification)[which(fit$classification == names(which(avgScore == max(avgScore))))] 18 | 19 | matches 20 | } 21 | -------------------------------------------------------------------------------- /R/normalization_functions.R: -------------------------------------------------------------------------------- 1 | #' Function to row-normalize a numerical vector by the 99th percentile 2 | #' 3 | #' @param x Numerical vector 4 | #' 5 | #' @return Vector with normalized values 6 | #' @export 7 | normalize_gene <- function(x) { 8 | 9 | x <- x + 1 10 | pctl <- quantile(x, 0.99, na.rm = TRUE) 11 | x_norm <- pmin(x / pctl, 1) 12 | 13 | x_norm 14 | } 15 | 16 | #' Function to convert gene counts CPM-normalized 17 | #' @param gem Data frame of 18 | #' @return CPM-normalized gene expression matrix 19 | #' @export 20 | counts_to_cpm <- function (counts_gem) { 21 | 22 | # Discard genes that are zero across all cells 23 | IDX_ZEROS <- apply(counts_gem, 1, function(row) all(row==0)) 24 | counts_gem <- counts_gem[!IDX_ZEROS, ] 25 | print(sprintf("After discarding all zero rows, left with %s rows.", dim(counts_gem)[1])) 26 | 27 | counts_scater <- SingleCellExperiment::SingleCellExperiment(assays = list(counts = as.matrix(counts_gem))) 28 | gem_cpm = scater::calculateCPM(counts_scater) 29 | 30 | gem_cpm 31 | } 32 | -------------------------------------------------------------------------------- /R/plots_functions.R: -------------------------------------------------------------------------------- 1 | #' Function to plot heatmap of average cluster-specific geneset 2 | #' expression in clusters of cells 3 | #' 4 | #' @param gem Data frame of gene expression (rows) per cell (columns) in target data 5 | #' @param labels List of cluster IDs for cells of gem 6 | #' @param markers Data frame of cluster specific genes with at least a "gene" and a "cluster" column 7 | #' 8 | #' @export 9 | make_heatmap <- function(gem, labels, markers) { 10 | 11 | # Keep only cells with available labels 12 | common_cells <- intersect(colnames(gem), names(labels)) 13 | if (length(common_cells) == 0) { 14 | stop("Cell names between labels and gem do not match! Please make sure you have provided labels for the cells of the gem.") 15 | } else { 16 | gem <- gem[, common_cells] 17 | labels <- labels[common_cells] 18 | } 19 | 20 | rownames(gem) <- make.names(toupper(rownames(gem)), unique=TRUE) 21 | markers$gene <- toupper(markers$gene) 22 | 23 | # Keep positive markers 24 | markers <- markers[which(markers$avg_log2FC > 0), ] 25 | # Keep markers present in gem 26 | markers <- markers[which(markers$gene %in% rownames(gem)), ] 27 | 28 | celltypes <- unique(c(unique(as.character(markers$cluster)), unique(as.character(labels)))) 29 | 30 | gem_avg <- matrix(NA, length(celltypes), length(celltypes)) 31 | for (i in 1:length(celltypes)) { 32 | cells <- na.omit(names(labels)[which(labels == celltypes[i])]) 33 | if (length(cells) >= 1) { 34 | avg_exp <- rowMeans(gem[markers$gene, cells, drop = FALSE]) 35 | } else { 36 | next 37 | } 38 | for (j in 1:length(celltypes)) { 39 | gem_avg[j,i] <- mean(avg_exp[markers$gene[which(markers$cluster == celltypes[j])]], na.rm = TRUE) 40 | } 41 | } 42 | 43 | rownames(gem_avg) <- paste("gs", celltypes, sep = "_") 44 | colnames(gem_avg) <- paste("Cl", celltypes, sep = "_") 45 | # remove columns that are all NA 46 | na.cols = which(apply(gem_avg, 2, function(x) all(is.na(x)))) 47 | if (length(na.cols) > 0) { 48 | gem_avg <- gem_avg[, -na.cols] 49 | } 50 | na.rows = which(apply(gem_avg, 1, function(x) all(is.na(x)))) 51 | if (length(na.rows) > 0) { 52 | # remove rows that are all NA 53 | gem_avg <- gem_avg[-na.rows, ] 54 | } 55 | 56 | pheatmap::pheatmap(gem_avg, border="white", cluster_rows = F, cluster_cols = F, border_color = F, scale = "row") 57 | } 58 | 59 | #' Function to plot heatmap of average cluster-specific geneset 60 | #' expression in clusters of cells 61 | #' 62 | #' @param gem Data frame of gene expression (rows) per cell (columns) in target data 63 | #' @param labels List of cluster IDs for cells of gem 64 | #' @param markers Data frame of cluster specific genes with at least "gene", "cluster" and "avg_logFC" columns 65 | #' @param clusterID cluster ID of cluster of interest 66 | #' @param weights list of weighst of cluster specific genes as returned by scid_multiclass 67 | #' 68 | #' @export 69 | plot_score_2D <- function(gem, labels, markers, clusterID, weights) { 70 | 71 | rownames(gem) <- make.names(toupper(rownames(gem)), unique=TRUE) 72 | markers$gene <- toupper(markers$gene) 73 | 74 | markers <- markers[which(markers$cluster == clusterID), ] 75 | positive_markers <- intersect(markers$gene[which(markers$avg_log2FC > 0)], rownames(gem)) 76 | negative_markers <- intersect(markers$gene[which(markers$avg_log2FC < 0)], rownames(gem)) 77 | 78 | if (length(positive_markers) == 0) { 79 | stop("No positive markers available for this cell type") 80 | } else if (length(negative_markers) == 0) { 81 | stop("No negative markers available for this cell type") 82 | } else { 83 | gem_norm <- t(apply(gem[c(positive_markers, negative_markers), ], 1, function(x) normalize_gene(x))) 84 | gem_norm <- gem_norm[complete.cases(gem_norm), ] 85 | 86 | weighted_gem <- weights[[clusterID]][c(positive_markers, negative_markers)] * gem_norm[, ,drop=FALSE] 87 | 88 | df <- data.frame(positive_score = colSums(weighted_gem[positive_markers, , drop=FALSE])/sqrt(sum(weights[[clusterID]][positive_markers]^2)), 89 | negative_score = colSums(weighted_gem[negative_markers, , drop=FALSE])/sqrt(sum(weights[[clusterID]][negative_markers]^2))) 90 | df$label <- rep("Other cell type", nrow(df)) 91 | df[names(labels)[which(labels == clusterID)], "label"] <- clusterID 92 | df$label <- factor(df$label, levels = c(clusterID, "Other cell type")) 93 | 94 | library(ggplot2) 95 | ggplot(df, aes(x=positive_score, y=negative_score, color=label)) + geom_point() + 96 | scale_color_manual(values=c("black", "grey")) + theme_classic() 97 | } 98 | } 99 | 100 | 101 | 102 | 103 | 104 | -------------------------------------------------------------------------------- /R/rank_signature_genes.R: -------------------------------------------------------------------------------- 1 | #' Function to choose training IN and OUT populations using precision-recall 2 | #' 3 | #' @param gem Data frame of gene expression of genes (rows) in cells (columns) 4 | #' @param positive_markers List of gene names expected to be upregulated in IN population 5 | #' @param negative_markers List of gene names expected to be downregulated in IN population 6 | #' 7 | #' @return Lists of training IN and OUT cells 8 | #' @export 9 | choose_training_set <- function(gem, positive_markers, negative_markers) { 10 | 11 | positive_markers <- intersect(positive_markers, rownames(gem)) 12 | negative_markers <- intersect(negative_markers, rownames(gem)) 13 | # Bin values to 0 and 1 for present (expressed) and absent genes 14 | sink("aux"); 15 | binned_gem <- apply(gem, 1, function(x) biomod2::BinaryTransformation(x, threshold = quantile(x[which(x>0)], 0.25, na.rm = TRUE))) 16 | sink(NULL); 17 | 18 | # Find total number of expressed genes per cell (n_e) 19 | n_e <- rowSums(binned_gem) 20 | # Find total number of expressed positive marker genes per cell (n_pme) 21 | if (length(positive_markers) >= 1) { 22 | n_pme <- rowSums(binned_gem[, positive_markers, drop = FALSE]) 23 | } else { 24 | n_pme <- rep(0, nrow(binned_gem)) 25 | names(n_pme) <- rownames(binned_gem) 26 | } 27 | # Find total number of expressed negative marker genes per cell (n_nme) 28 | if (length(negative_markers) >= 1) { 29 | n_nme <- rowSums(binned_gem[, negative_markers, drop = FALSE]) 30 | } else { 31 | n_nme <- rep(0, nrow(binned_gem)) 32 | names(n_nme) <- rownames(binned_gem) 33 | } 34 | # Find total number of positive marker genes (n_pm) 35 | n_pm <- max(length(positive_markers), 1) 36 | # Find total number of negative marker genes (n_nm) 37 | n_nm <- max(length(negative_markers), 1) 38 | 39 | data <- data.frame( 40 | recall = (n_pme/n_pm) - (n_nme/n_nm), 41 | precision = (n_pme-n_nme)/n_e 42 | ) 43 | rownames(data) <- colnames(gem) 44 | data <- data[complete.cases(data), ] 45 | 46 | library(mclust) 47 | sink("aux"); 48 | fit <- Mclust(data) 49 | sink(NULL); 50 | 51 | # Get centroids of each cluster 52 | centroids <- data.frame(matrix(NA, length(unique(fit$classification)), 2), row.names = unique(fit$classification)) 53 | colnames(centroids) <- c("precision", "recall") 54 | sds <- data.frame(matrix(NA, length(unique(fit$classification)), 2), row.names = unique(fit$classification)) 55 | colnames(sds) <- c("precision", "recall") 56 | for (ID in rownames(centroids)) { 57 | centroids[ID, "precision"] <- mean(data[which(fit$classification == ID), "precision"]) 58 | sds[ID, "precision"] <- sd(data[which(fit$classification == ID), "precision"]) 59 | centroids[ID, "recall"] <- mean(data[which(fit$classification == ID), "recall"]) 60 | sds[ID, "recall"] <- sd(data[which(fit$classification == ID), "recall"]) 61 | } 62 | 63 | IN_candidates <- unique(c(rownames(centroids)[which(centroids$recall == max(centroids$recall))], rownames(centroids)[which(centroids$precision == max(centroids$precision))])) 64 | 65 | E_dist <- apply(centroids, 1, function(x) sqrt((1-x[1])^2 + (1-x[2])^2)) 66 | 67 | IN_id <- names(E_dist)[which(E_dist == min(E_dist))] 68 | 69 | IN_cells <- colnames(gem)[which(fit$classification %in% IN_id)] 70 | 71 | # If there are two clusters found as candidate IN remove the one that is farthest from (1,1) 72 | other_IN <- setdiff(IN_candidates, IN_id) 73 | if (length(other_IN) == 1) { 74 | NA_cells <- colnames(gem)[which(fit$classification %in% other_IN)] 75 | } else { 76 | NA_cells <- c() 77 | } 78 | 79 | # Get OUT cells removing those that are in the IN radious 80 | OUT_cells <- setdiff(rownames(data), c(IN_cells, NA_cells)) 81 | 82 | list(in_pop=IN_cells, out_pop=OUT_cells) 83 | } 84 | 85 | #' Main function for estimation of gene ranks 86 | #' 87 | #' @param gem Data frame of signature genes in cells 88 | #' @param true_cells List of training IN cells 89 | #' @param false_cells List of training OUT cells 90 | #' 91 | #' @return List of weights for signature genes 92 | #' @export 93 | scID_weight <- function(gem, true_cells, false_cells) { 94 | 95 | weights <- rep(NA, nrow(gem)) 96 | names(weights) <- rownames(gem) 97 | for (gene in rownames(gem)) { 98 | numerator <- mean(as.numeric(gem[gene, true_cells])) - mean(as.numeric(gem[gene, false_cells])) 99 | denominator <- sd(as.numeric(gem[gene, false_cells]))^2 + sd(as.numeric(gem[gene, true_cells]))^2 100 | 101 | weights[gene] <- numerator/denominator 102 | } 103 | weights[which(is.na(weights))] <- 0 104 | 105 | weights 106 | } 107 | -------------------------------------------------------------------------------- /R/read_data.R: -------------------------------------------------------------------------------- 1 | #' Function to read Gene Expression Matrix from file 2 | #' 3 | #' @param filename directory and name of input file 4 | #' @param header specify if first line of file is the field names 5 | #' 6 | #' @return Data frame of GEM with genes in rows and cells in columns 7 | #' @export 8 | loadfast <- function(filename, header = T) { 9 | 10 | df <- data.table::fread(filename, header=header, showProgress=TRUE, data.table=FALSE) 11 | rownames(df) <- toupper(df[,1]) 12 | df <- df[,-1] 13 | 14 | df 15 | } 16 | 17 | #' Function to read markers from .gmt file format (Genomic Cytometry) 18 | #' 19 | #' @param filename .gmt file with markers 20 | #' 21 | #' @return Data frame of cluster specific genes per celltype 22 | #' @export 23 | gmt_to_markers <- function(gmt_file) { 24 | 25 | data <- qusage::read.gmt(gmt_file) 26 | celltypes <- names(data) 27 | markers <- data.frame(gene = NA, cluster = NA, avg_log2FC = NA) 28 | for (ct in celltypes) { 29 | 30 | m <- data.frame(gene = data[[ct]], cluster = ct, avg_log2FC = 1) 31 | markers <- rbind(markers, m) 32 | } 33 | markers <- markers[complete.cases(markers), ] 34 | 35 | markers 36 | } 37 | -------------------------------------------------------------------------------- /R/scID.R: -------------------------------------------------------------------------------- 1 | #' Main function to get Gene expression matrix and signature genes 2 | #' and return matches and scores 3 | #' 4 | #' @param target_gem Data frame of gene expression (rows) per cell (columns) in target data 5 | #' @param reference_gem Data frame of gene expression (rows) per cell (columns) in reference data 6 | #' @param reference_clusters Named list of cluster IDs of reference cells 7 | #' @param markers Data frame of cluster specific genes that will be used instead of the reference data 8 | #' @param logFC LogFC threshold for extracting markers from reference clusters 9 | #' @param use_reference_for_weights Logical to use either reference or target data for sorting the signature genes 10 | #' @param only_pos Logical to include negative markers in the cluster specific gene sets 11 | #' @param normalize_reference Logical to select if reference data need to be normalized (when raw counts have been provided) 12 | #' 13 | #' @return list of cluster IDs for target cells 14 | #' @return scID scores for target cells 15 | #' @return markers used 16 | #' @return estimated gene weights 17 | #' 18 | #' @export 19 | scid_multiclass <- function(target_gem = NULL, reference_gem = NULL, 20 | reference_clusters = NULL, markers = NULL, 21 | logFC = 0.5, normalize_reference=TRUE, 22 | estimate_weights_from_target = FALSE, 23 | weights = NULL, only_pos=FALSE) { 24 | 25 | # ---------------------------------------------------------------------------------------------------- 26 | # Data pre-processing 27 | # ---------------------------------------------------------------------------------------------------- 28 | if (is.null(reference_gem) && is.null(reference_clusters) && is.null(markers)) { 29 | stop("Please provide either clustered reference data or list of markers for each reference cluster") 30 | } 31 | if (!is.null(reference_gem) && !is.null(reference_clusters)) { 32 | # Check all reference cells have a cluster ID 33 | common_cells <- intersect(names(reference_clusters), colnames(reference_gem)) 34 | if (length(common_cells) == 0) { 35 | stop("None of the reference cells has a cluster ID. Please check the reference_clusters list provided.") 36 | } else { 37 | reference_gem <- reference_gem[, common_cells] 38 | rownames(reference_gem) <- make.names(toupper(rownames(reference_gem)), unique=TRUE) 39 | 40 | # Remove genes that are zero across all cells 41 | reference_gem <- reference_gem[which(rowSums(reference_gem) != 0), ] 42 | reference_clusters <- reference_clusters[common_cells] 43 | } 44 | } 45 | 46 | if (!is.null(markers)) { 47 | # Check markers have gene and cluster columns 48 | if (length(intersect(c("gene", "cluster"), colnames(markers))) !=2 ) { 49 | stop("Please provide a data frame of markers with gene and cluster in columns") 50 | } 51 | markers$gene <- toupper(markers$gene) 52 | } 53 | 54 | 55 | # Target 56 | rownames(target_gem) <- make.names(toupper(rownames(target_gem)), unique=TRUE) 57 | # Remove genes that are zero across all cells 58 | target_gem <- target_gem[which(rowSums(target_gem) != 0), ] 59 | 60 | # ---------------------------------------------------------------------------------------------------- 61 | # Stage 1: Find signature genes from reference data 62 | # ---------------------------------------------------------------------------------------------------- 63 | if (is.null(markers)) { 64 | message("Stage 1: extract signatures genes from reference clusters") 65 | markers <- find_markers(reference_gem, reference_clusters, logFC, only.pos=only_pos, normalize_reference=normalize_reference) 66 | # Filter out signature genes that are not present in the target data 67 | markers <- markers[which(markers$gene %in% rownames(target_gem)), ] 68 | celltypes <- unique(markers$cluster) 69 | if (estimate_weights_from_target) { 70 | rm(reference_gem, reference_clusters) 71 | } 72 | } else { 73 | markers <- markers[which(markers$gene %in% rownames(target_gem)), ] 74 | celltypes <- unique(markers$cluster) 75 | } 76 | 77 | # Min-max normalization of target gem 78 | target_gem_norm <- t(apply(target_gem[unique(markers$gene), ], 1, function(x) normalize_gene(x))) 79 | target_gem_norm <- target_gem_norm[complete.cases(target_gem_norm), ] 80 | 81 | # ---------------------------------------------------------------------------------------------------- 82 | # Stage 2: Weight signature genes 83 | # ---------------------------------------------------------------------------------------------------- 84 | if (is.null(weights)) { 85 | if (estimate_weights_from_target) { 86 | message("Stage 2: Estimate weights of signature genes from target") 87 | weights <- list() 88 | for (i in 1:length(celltypes)) { 89 | celltype_markers <- markers[which(markers$cluster == celltypes[i]), ] 90 | positive_markers <- celltype_markers$gene[which(celltype_markers$avg_log2FC > 0)] 91 | negative_markers <- celltype_markers$gene[which(celltype_markers$avg_log2FC < 0)] 92 | training_groups <- choose_training_set(target_gem, positive_markers, negative_markers) 93 | signature_genes <- c(positive_markers, negative_markers) 94 | gene.weights <- scID_weight(target_gem_norm[signature_genes, , drop=FALSE], training_groups$in_pop, training_groups$out_pop) 95 | # If only positive markers are selected, truncate all negative weights to 0 96 | if (only_pos) { 97 | gene.weights[which(gene.weights < 0)] <- 0 98 | } 99 | # Make Inf weights 0 100 | gene.weights[is.infinite(gene.weights)] <- 0 101 | weights[[as.character(celltypes[i])]] <- gene.weights 102 | svMisc::progress(i*100/length(celltypes)) 103 | Sys.sleep(0.01) 104 | if (i==length(celltypes)) cat("Done!") 105 | } 106 | names(weights) <- celltypes 107 | } else { 108 | if (!is.null(reference_gem) && !is.null(reference_clusters)) { 109 | message("Stage 2: Estimate weights of signature genes from reference") 110 | weights <- list() 111 | # Normalize reference gem 112 | ref_gem_norm <- t(apply(reference_gem[unique(markers$gene), ], 1, function(x) normalize_gene(x))) 113 | ref_gem_norm <- ref_gem_norm[complete.cases(ref_gem_norm), ] 114 | for (i in 1:length(celltypes)) { 115 | signature_genes <- markers$gene[which(markers$cluster == celltypes[i])] 116 | true_cells <- names(reference_clusters)[which(reference_clusters == as.character(celltypes[i]))] 117 | false_cells <- setdiff(names(reference_clusters), true_cells) 118 | gene.weights <- scID_weight(gem = ref_gem_norm[signature_genes, ,drop=FALSE], true_cells, false_cells) 119 | 120 | weights[[as.character(celltypes[i])]] <- gene.weights 121 | # If only positive markers are selected, truncate all negative weights to 0 122 | if (only_pos) { 123 | gene.weights[which(gene.weights < 0)] <- 0 124 | } 125 | svMisc::progress(i*100/length(celltypes)) 126 | Sys.sleep(0.01) 127 | if (i==length(celltypes)) cat("Done!") 128 | } 129 | # Won't need reference data any more, remove for efficiency 130 | rm(reference_gem, reference_clusters, ref_gem_norm) 131 | } else { 132 | stop("Please provide reference data in order to calculate weights, choose to estimate weights from target data, or provide precompted gene weights.") 133 | } 134 | } 135 | } 136 | 137 | #---------------------------------------------------------------------------------------------------- 138 | # Stage 3: Find scores and putative matches 139 | # ---------------------------------------------------------------------------------------------------- 140 | message("Stage 3.1-2: Calculate scores and find matching cells") 141 | 142 | scores <- data.frame(matrix(NA, length(celltypes), ncol(target_gem)), row.names = celltypes) 143 | colnames(scores) <- colnames(target_gem) 144 | 145 | full_scores <- data.frame(matrix(NA, length(celltypes), ncol(target_gem)), row.names = celltypes) 146 | colnames(full_scores) <- colnames(target_gem) 147 | 148 | for (i in 1:length(celltypes)) { 149 | celltype <- as.character(celltypes[i]) 150 | signature <- intersect(names(weights[[celltype]]), rownames(target_gem_norm)) 151 | weighted_gem <- weights[[celltype]][signature] * target_gem_norm[signature, ,drop=FALSE] 152 | # Check if whole weighted gem is 0 (when all gene weighst are zero) 153 | if (all(weighted_gem == 0)) { 154 | full_scores[as.character(celltype), ] <- rep(0, ncol(full_scores)) 155 | } else { 156 | score <- colSums(weighted_gem)/sqrt(sum(weights[[celltype]]^2)) 157 | matches <- final_populations(score) 158 | scores[as.character(celltype), matches] <- scale(score[matches]) 159 | full_scores[as.character(celltype), ] <- score 160 | } 161 | if (i==length(celltypes)) cat("Done!") 162 | } 163 | 164 | # Resolve multiclass assignments 165 | message ("Stage 3.3: Resolve multiclass assignments") 166 | labels <- apply(scores, 2, function(x) {ifelse(all(is.na(x)), "unassigned", rownames(scores)[which(x == max(x, na.rm = T))])}) 167 | 168 | # return result 169 | list(markers=markers, estimated_weights=weights, labels=labels, scores=full_scores) 170 | 171 | } 172 | 173 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # scID 2 | 3 | The power of single cell RNA sequencing (scRNA-seq) stems from its ability to uncover cell type-dependent phenotypes, which rests on the accuracy of cell type identification. However, resolving cell types within and, thus, comparison of scRNA-seq data across conditions is challenging due to technical factors such as sparsity, low number of cells and batch effect. To address these challenges we developed scID (Single Cell IDentification), which uses the framework of Fisher's Linear Discriminant Analysis to identify transcriptionally related cell types between scRNA-seq datasets. Detailed information on the method and performance evaluation is demostrated in the publication [Boufea et al., iScience 2020](https://www.sciencedirect.com/science/article/pii/S2589004220300985?via%3Dihub). By increasing power to identify transcriptionally similar cell types across datasets, scID enhances investigator's ability to extract biological insights from scRNA-seq data. 4 | 5 | scID classifies cells of a given target dataset based on their transcriptional similarity to given reference clusters in 4 steps. As a first step, scID extracts cluster-specific gene sets from the reference data and calculates weights (based on Fisher's Linear Discriminant Analysis) that represent their discriminative power to identify the cluster of interest. Next, scID scores all target cells based on the expression of the cluster-specific gene sets and, finally, identifies equivalent target cells by fitting a mixture of Gaussian distributions. 6 | 7 | ![](./assets/images/scID_pipeline.png) 8 | 9 | 10 | ## Installation 11 | On May 17, 2019, we released the new version of scID (v2.0.0) that uses negative markers together with positive for identifying equivalent cells. We have seen that this improves classification in presense of very simlar cell types in the dataset. 12 | 13 | scID can be installed using the devtools R package: 14 | ``` 15 | install.packages('devtools') 16 | devtools::install_github("BatadaLab/scID") 17 | ``` 18 | 19 | ## Usage 20 | Given two single-cell RNA-seq gene expression datasets with one of them having known groups of cells (clusters), scID can be used to identify transcriptionally similar cells in the second dataset. 21 | 22 | ``` 23 | scID_output <- scID::scid_multiclass(target_gem, reference_gem, reference_clusters, ...) 24 | ``` 25 | #### Input 26 | 1. ```target_gem``` An nxm data frame of n genes (rows) in m cells (columns) of the dataset with unknown grouping, where each entry is library-depth or column normalized gene expression. Cell names are expected to be unique 27 | 28 | 2. ```reference_gem``` An NxM data frame of N genes (rows) in M cells (columns) of the dataset with known grouping, where each entry is library-depth or column normalized gene expression 29 | 30 | 3. ```reference_clusters``` A list of cluster labels for the reference cells 31 | 32 | #### Output 33 | 34 | scID_output is a list of four objects 35 | 36 | 1. ```scID_output$labels``` A named list of cluster labels for the target cells 37 | 38 | 2. ```scID_output$markers``` A data frame of signature genes extracted from the reference clusters 39 | 40 | 3. ```scID_output$weights``` A list of the estimated weights for all cluster-specific genes 41 | 42 | 4. ```scID_output$scores``` A data frame of scores of target cells (columns) for each reference cluster-specific geneset (rows) 43 | 44 | ## Vignettes 45 | 46 | [Identification of equivalent cells across single-cell RNA-seq datasets](./vignettes/Mapping_example.md) 47 | 48 | ## Need help? 49 | To report bugs or ask any questions please use the [GitHub issues tracker](https://github.com/BatadaLab/scID/issues). 50 | 51 | 52 | 53 | 54 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-dinky 2 | title: scID 3 | description: R library that enables identification of equivalent cell populations across single-cell RNA-seq datasets 4 | show_downloads: "true" 5 | -------------------------------------------------------------------------------- /assets/images/scID_pipeline.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BatadaLab/scID/dba2e4e8cfb20acc0bffaf244b61fb1906de5a6c/assets/images/scID_pipeline.png -------------------------------------------------------------------------------- /man/choose_training_set.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/rank_signature_genes.R 3 | \name{choose_training_set} 4 | \alias{choose_training_set} 5 | \title{Function to choose training IN and OUT populations using precision-recall} 6 | \usage{ 7 | choose_training_set(gem, positive_markers, negative_markers) 8 | } 9 | \arguments{ 10 | \item{gem}{Data frame of gene expression of genes (rows) in cells (columns)} 11 | 12 | \item{positive_markers}{List of gene names expected to be upregulated in IN population} 13 | 14 | \item{negative_markers}{List of gene names expected to be downregulated in IN population} 15 | } 16 | \value{ 17 | Lists of training IN and OUT cells 18 | } 19 | \description{ 20 | Function to choose training IN and OUT populations using precision-recall 21 | } 22 | -------------------------------------------------------------------------------- /man/counts_to_cpm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/normalization_functions.R 3 | \name{counts_to_cpm} 4 | \alias{counts_to_cpm} 5 | \title{Function to convert gene counts CPM-normalized} 6 | \usage{ 7 | counts_to_cpm(counts_gem) 8 | } 9 | \arguments{ 10 | \item{gem}{Data frame of} 11 | } 12 | \value{ 13 | CPM-normalized gene expression matrix 14 | } 15 | \description{ 16 | Function to convert gene counts CPM-normalized 17 | } 18 | -------------------------------------------------------------------------------- /man/final_populations.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/fit_GMM.R 3 | \name{final_populations} 4 | \alias{final_populations} 5 | \title{Function to identify matching population from scID score by fitting 6 | a Gaussian finite mixture model from Mclust} 7 | \usage{ 8 | final_populations(score) 9 | } 10 | \arguments{ 11 | \item{score}{List of scID scores for target cells} 12 | } 13 | \value{ 14 | List of matching cells 15 | } 16 | \description{ 17 | Function to identify matching population from scID score by fitting 18 | a Gaussian finite mixture model from Mclust 19 | } 20 | -------------------------------------------------------------------------------- /man/find_markers.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/find_markers.R 3 | \name{find_markers} 4 | \alias{find_markers} 5 | \title{Function to extract cluster specific genes from reference clusters. 6 | This function uses MAST test as implemented in the Seurat package.} 7 | \usage{ 8 | find_markers( 9 | reference_gem, 10 | reference_clusters, 11 | logFC, 12 | only.pos, 13 | normalize_reference 14 | ) 15 | } 16 | \arguments{ 17 | \item{reference_gem}{Data frame of gene expression (rows) per cell (columns) in reference data} 18 | 19 | \item{reference_clusters}{Named list of cluster IDs of reference cells} 20 | 21 | \item{logFC}{Log2FC threshold for extracting markers from reference clusters} 22 | 23 | \item{only.pos}{Logical to include negative markers in the cluster specific gene sets} 24 | 25 | \item{normalize_reference}{Logical to select if reference data need to be normalized (when raw counts have been provided)} 26 | } 27 | \value{ 28 | Data frame of cluster specific genes extracted 29 | } 30 | \description{ 31 | Function to extract cluster specific genes from reference clusters. 32 | This function uses MAST test as implemented in the Seurat package. 33 | } 34 | -------------------------------------------------------------------------------- /man/gmt_to_markers.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/read_data.R 3 | \name{gmt_to_markers} 4 | \alias{gmt_to_markers} 5 | \title{Function to read markers from .gmt file format (Genomic Cytometry)} 6 | \usage{ 7 | gmt_to_markers(gmt_file) 8 | } 9 | \arguments{ 10 | \item{filename}{.gmt file with markers} 11 | } 12 | \value{ 13 | Data frame of cluster specific genes per celltype 14 | } 15 | \description{ 16 | Function to read markers from .gmt file format (Genomic Cytometry) 17 | } 18 | -------------------------------------------------------------------------------- /man/loadfast.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/read_data.R 3 | \name{loadfast} 4 | \alias{loadfast} 5 | \title{Function to read Gene Expression Matrix from file} 6 | \usage{ 7 | loadfast(filename, header = T) 8 | } 9 | \arguments{ 10 | \item{filename}{directory and name of input file} 11 | 12 | \item{header}{specify if first line of file is the field names} 13 | } 14 | \value{ 15 | Data frame of GEM with genes in rows and cells in columns 16 | } 17 | \description{ 18 | Function to read Gene Expression Matrix from file 19 | } 20 | -------------------------------------------------------------------------------- /man/make_heatmap.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/plots_functions.R 3 | \name{make_heatmap} 4 | \alias{make_heatmap} 5 | \title{Function to plot heatmap of average cluster-specific geneset 6 | expression in clusters of cells} 7 | \usage{ 8 | make_heatmap(gem, labels, markers) 9 | } 10 | \arguments{ 11 | \item{gem}{Data frame of gene expression (rows) per cell (columns) in target data} 12 | 13 | \item{labels}{List of cluster IDs for cells of gem} 14 | 15 | \item{markers}{Data frame of cluster specific genes with at least a "gene" and a "cluster" column} 16 | } 17 | \description{ 18 | Function to plot heatmap of average cluster-specific geneset 19 | expression in clusters of cells 20 | } 21 | -------------------------------------------------------------------------------- /man/normalize_gene.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/normalization_functions.R 3 | \name{normalize_gene} 4 | \alias{normalize_gene} 5 | \title{Function to row-normalize a numerical vector by the 99th percentile} 6 | \usage{ 7 | normalize_gene(x) 8 | } 9 | \arguments{ 10 | \item{x}{Numerical vector} 11 | } 12 | \value{ 13 | Vector with normalized values 14 | } 15 | \description{ 16 | Function to row-normalize a numerical vector by the 99th percentile 17 | } 18 | -------------------------------------------------------------------------------- /man/plot_score_2D.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/plots_functions.R 3 | \name{plot_score_2D} 4 | \alias{plot_score_2D} 5 | \title{Function to plot heatmap of average cluster-specific geneset 6 | expression in clusters of cells} 7 | \usage{ 8 | plot_score_2D(gem, labels, markers, clusterID, weights) 9 | } 10 | \arguments{ 11 | \item{gem}{Data frame of gene expression (rows) per cell (columns) in target data} 12 | 13 | \item{labels}{List of cluster IDs for cells of gem} 14 | 15 | \item{markers}{Data frame of cluster specific genes with at least "gene", "cluster" and "avg_logFC" columns} 16 | 17 | \item{clusterID}{cluster ID of cluster of interest} 18 | 19 | \item{weights}{list of weighst of cluster specific genes as returned by scid_multiclass} 20 | } 21 | \description{ 22 | Function to plot heatmap of average cluster-specific geneset 23 | expression in clusters of cells 24 | } 25 | -------------------------------------------------------------------------------- /man/scID_weight.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/rank_signature_genes.R 3 | \name{scID_weight} 4 | \alias{scID_weight} 5 | \title{Main function for estimation of gene ranks} 6 | \usage{ 7 | scID_weight(gem, true_cells, false_cells) 8 | } 9 | \arguments{ 10 | \item{gem}{Data frame of signature genes in cells} 11 | 12 | \item{true_cells}{List of training IN cells} 13 | 14 | \item{false_cells}{List of training OUT cells} 15 | } 16 | \value{ 17 | List of weights for signature genes 18 | } 19 | \description{ 20 | Main function for estimation of gene ranks 21 | } 22 | -------------------------------------------------------------------------------- /man/scid_multiclass.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/scID.R 3 | \name{scid_multiclass} 4 | \alias{scid_multiclass} 5 | \title{Main function to get Gene expression matrix and signature genes 6 | and return matches and scores} 7 | \usage{ 8 | scid_multiclass( 9 | target_gem = NULL, 10 | reference_gem = NULL, 11 | reference_clusters = NULL, 12 | markers = NULL, 13 | logFC = 0.5, 14 | normalize_reference = TRUE, 15 | estimate_weights_from_target = FALSE, 16 | weights = NULL, 17 | only_pos = FALSE 18 | ) 19 | } 20 | \arguments{ 21 | \item{target_gem}{Data frame of gene expression (rows) per cell (columns) in target data} 22 | 23 | \item{reference_gem}{Data frame of gene expression (rows) per cell (columns) in reference data} 24 | 25 | \item{reference_clusters}{Named list of cluster IDs of reference cells} 26 | 27 | \item{markers}{Data frame of cluster specific genes that will be used instead of the reference data} 28 | 29 | \item{logFC}{LogFC threshold for extracting markers from reference clusters} 30 | 31 | \item{normalize_reference}{Logical to select if reference data need to be normalized (when raw counts have been provided)} 32 | 33 | \item{only_pos}{Logical to include negative markers in the cluster specific gene sets} 34 | 35 | \item{use_reference_for_weights}{Logical to use either reference or target data for sorting the signature genes} 36 | } 37 | \value{ 38 | list of cluster IDs for target cells 39 | 40 | scID scores for target cells 41 | 42 | markers used 43 | 44 | estimated gene weights 45 | } 46 | \description{ 47 | Main function to get Gene expression matrix and signature genes 48 | and return matches and scores 49 | } 50 | -------------------------------------------------------------------------------- /vignettes/Mapping_example.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Identification of equivalent cells across single-cell RNA-seq datasets" 3 | date: `r Sys.Date()` 4 | output: rmarkdown::html_vignette 5 | vignette: > 6 | %\VignetteIndexEntry{Vignette Title} 7 | %\VignetteEngine{knitr::rmarkdown} 8 | %\VignetteEncoding{UTF-8} 9 | --- 10 | 11 | # Tutorial: Identification of equivalent cells across single-cell RNA-seq datasets 12 | 13 | This tutorial is an example of using scID for mapping across two 10X datasets of E18 mouse brain [single-cells](https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k) and [single-nuclei](https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/nuclei_900) from cortex, hippocampus and subverticular zone. To speed-up preprocessing you can download the [TPM-normalized data](https://github.com/BatadaLab/scID/tree/master/ExampleData) we have pre-computed. 14 | 15 | The reference cells can be grouped into 15 clusters as shown in the next plot. 16 | ![](../ExampleData/figures/Reference_tSNE.png) 17 | 18 | ### Mapping across datasets 19 | First, load scID library and read the files. 20 | ``` 21 | library(scID) 22 | 23 | target_gem <- readRDS(file="~/scID/ExampleData/target_gem.rds") 24 | 25 | reference_gem <- readRDS(file="~/scID/ExampleData/reference_gem.rds") 26 | reference_clusters <- readRDS(file="~/scID/ExampleData/reference_clusters.rds") 27 | ``` 28 | 29 | Next, run scID with the above inputs and the following settings: 30 | 1. ```normalize_reference``` is set to ```FALSE``` as the reference data is already normalized. Any library-depth normalization (e.g. TPM, CPM) is compatibe with scID, but not log-transformed data. 31 | 2. ```logFC``` is defining minimum logFold-change for a gene to be seleced as cluster-specific. Low ```logFC``` lead to identification of longer lists of cluster-specific genes that can help resolve classes in presense of very similar reference clusters but will require longer computational time. 32 | 3. ```estimate_weights_from_target``` is set to ```TRUE``` in order to estimated gene weights from the target by selecting training target cells as described in the [manuscript](https://www.biorxiv.org/content/10.1101/470203v1). Alternatively, weights can be estimated from the reference data (using the known cell labels), which is recommended when library depth of the two datasets is similar or when the reference clusters are transcriptionally similar. 33 | 4. ```only_pos``` is set to ```FALSE``` to include cluster-specific downregulated genes that can help distinguish clusters from their nearest neighbours. 34 | 35 | ``` 36 | scID_output <- scid_multiclass(target_gem = target_gem, reference_gem = reference_gem, reference_clusters = reference_clusters, 37 | logFC = 0.6, only_pos = FALSE, estimate_weights_from_target = FALSE) 38 | ``` 39 | 40 | Alternatively, scID can take a precomputed or curated list of cluster-specific genes. The data frame should include the following columns: 41 | 1. ```gene```: containing the gene name/symbol/ID in the same format as in the target gem 42 | 2. ```cluster```: containing the ID for which the respective gene is a marker 43 | 44 | ``` 45 | markers <- readRDS(file="~/scID/ExampleData/markers.rds") 46 | 47 | scID_output <- scid_multiclass(target_gem = target_gem, markers = markers, estimate_weights_from_target = FALSE, 48 | reference_gem = reference_gem, reference_clusters = reference_clusters) 49 | ``` 50 | 51 | ### Visualising results 52 | scID provides functions to visualise the results. 53 | The next function creates a heatmap of the average expression of each cluster-specific geneset in each of the reference or target clusters clusters. Each row represents a luster-specific geneset and each column a cluster of cells. 54 | 55 | So, the following plot shows the expression of cluster-specific genes in the reference dataset 56 | 57 | ``` 58 | make_heatmap(gem = reference_gem, labels = reference_clusters, markers = scID_output$markers) 59 | ``` 60 | ![](../ExampleData/figures/Reference_heatmap.png) 61 | 62 | and the respective heatmap of target nuclei data grouped by scID is shown below 63 | ``` 64 | make_heatmap(gem = target_gem, labels = scID_output$labels, markers = scID_output$markers) 65 | ``` 66 | ![](../ExampleData/figures/Target_heatmap.png) 67 | 68 | Here we can see that the gene expression pattern in the target data is very similar to the one in the reference data. The gray column shows that no target cell was assigned to the reference clusters 5 and 13. 69 | 70 | 71 | Additionally, for a given reference cluster, selected target cells can be projected onto two dimensions: i) the score for positive markers of the reference cluster and ii) the score for negative markers of the reference clysrer. Matching target cells are expected to be on the top right corner of the plot. Cells are coloured based on the result of ```scid_multiclass```. 72 | For example, the plot for reference cluster 1 from the above result can be obtained as follows: 73 | ``` 74 | plot_score_2D(gem = target_gem, labels = scID_output$labels, markers = scID_output$markers, 75 | clusterID = "4", weights = scID_output$estimated_weights) 76 | ``` 77 | ![](../ExampleData/figures/2D_score_plot.png) 78 | 79 | 80 | [back](../README.md) 81 | -------------------------------------------------------------------------------- /vignettes/introduction.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Vignette Title" 3 | author: "Vignette Author" 4 | date: "`r Sys.Date()`" 5 | output: rmarkdown::html_vignette 6 | vignette: > 7 | %\VignetteIndexEntry{Vignette Title} 8 | %\VignetteEngine{knitr::rmarkdown} 9 | %\VignetteEncoding{UTF-8} 10 | --- 11 | 12 | ```{r setup, include = FALSE} 13 | knitr::opts_chunk$set( 14 | collapse = TRUE, 15 | comment = "#>" 16 | ) 17 | ``` 18 | 19 | ## Usage 20 | 21 | There are three ways to use scID. 22 | 23 | ### Usage 1: Canonical usage (for finding equivalent groups of cells across data) 24 | Given two datasets of single-cell RNA-seq gene expression for which cell grouping for one the datasets (reference) is known, scID seeks to find transcriptionally equivalent groups of cells for the second dataset (target). 25 | ``` 26 | scID_output <- scID::scid_multiclass(target_gem, reference_gem, reference_clusters, ...) 27 | ``` 28 | 29 | #### Input 30 | 1. ```target_gem``` An nxm data frame of n genes (rows) in m cells (columns) of the dataset with unknown grouping, where each entry is library-depth or column normalized gene expression. Cell names are expected to be unique. 31 | 2. ```reference_gem``` An NxM data frame of N genes (rows) in M cells (columns) of the dataset with known grouping, where each entry is library-depth or column normalized gene expression. 32 | 3. ```reference_clusters``` A list of cluster labels for the reference cells. 33 | 34 | #### Output 35 | 36 | scID_output is a list of two objects 37 | 38 | 1. ```scID_output$labels``` A named list of cluster labels for the target cells 39 | 40 | 2. ```scID_output$markers``` A data frame of signature genes extracted from the reference clusters. 41 | 42 | ### Usage 2: Canonical usage (for finding equivalent groups of cells across data) with multiple targets (T1, T2) 43 | 44 | * Step 1: Extract markers from reference clusters 45 | ``` 46 | markers_generated_by_scID <- scID::find_markers(reference_gem, reference_clusters, logFC) 47 | ``` 48 | This step can be skipped when the user has own method for extracting markers. 49 | 50 | * Step 2: Find transctiptionally equivalent cells in target datasets 51 | ``` 52 | scID_output_T1 <- scID:scid_multiclass(T1, markers_generated_by_scID, ...) 53 | 54 | scID_output_T2 <- scID::scid_multiclass(T2, markers_generated_by_scID, ...) 55 | ``` 56 | ### Usage 3: User-specified cluster gene signatures 57 | A pre-computed set of markers can be given as input by the user alternatively. The markers object has to be a data frame with genes and cluster ID in columns as in [this](https://github.com/BatadaLab/scID/blob/master/ExampleData/markers.rds) example file. 58 | ``` 59 | scID_output <- scID::scid_multiclass(T, markers_generated_by_user, ...) 60 | ``` 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The `html_vignette` output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The `html_vignette` format: 76 | 77 | - Never uses retina figures 78 | - Has a smaller default figure size 79 | - Uses a custom CSS stylesheet instead of the default Twitter Bootstrap style 80 | 81 | ## Vignette Info 82 | 83 | Note the various macros within the `vignette` section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the `title` field and the `\VignetteIndexEntry` to match the title of your vignette. 84 | 85 | ## Styles 86 | 87 | The `html_vignette` template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows: 88 | 89 | output: 90 | rmarkdown::html_vignette: 91 | css: mystyles.css 92 | 93 | ## Figures 94 | 95 | The figure sizes have been customised so that you can easily put two images side-by-side. 96 | 97 | ```{r, fig.show='hold'} 98 | plot(1:10) 99 | plot(10:1) 100 | ``` 101 | 102 | You can enable figure captions by `fig_caption: yes` in YAML: 103 | 104 | output: 105 | rmarkdown::html_vignette: 106 | fig_caption: yes 107 | 108 | Then you can use the chunk option `fig.cap = "Your figure caption."` in **knitr**. 109 | 110 | ## More Examples 111 | 112 | You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using `knitr::kable()`. 113 | 114 | ```{r, echo=FALSE, results='asis'} 115 | knitr::kable(head(mtcars, 10)) 116 | ``` 117 | 118 | Also a quote using `>`: 119 | 120 | > "He who gives up [code] safety for [code] speed deserves neither." 121 | ([via](https://twitter.com/hadleywickham/status/504368538874703872)) 122 | --------------------------------------------------------------------------------