├── LICENSE ├── README.md └── scripts ├── external_data ├── Randolph2021 │ ├── mock_pbmc_processing.R │ └── monocytes_trajectory.R └── Smillie2019 │ └── singlecell_analysis.R ├── finemap ├── 01_calc_LD.sh ├── 01_normalize_blueprint.R ├── 02_merge_pseudobulk_blueprint_snps.sh ├── 02_prep_finemap_files.R ├── 03_merge_pseudobulk_blueprint_resid.R └── 03_run_finemap.sh ├── post_analysis ├── calc_singlecell_betas.R ├── cluster_eGenes.R ├── enrich_atac.R ├── enrich_impact.R ├── enrich_tss.R ├── run_coloc.R └── run_homer.sh ├── pseudobulk ├── 01_make_pseudobulk_exprs.R ├── 02_normalize.R ├── 03_make_BED.sh ├── 04_fastqtl_nominal.sh └── 05_fastqtl_permute.sh └── singlecell ├── linear_nostate.R ├── linear_nostate_permute.R ├── linear_univariate.R ├── linear_univariate_permute.R ├── linear_univariate_simulDE.R ├── nb_univariate.R ├── poisson_multivariate.R ├── poisson_nostate.R ├── poisson_nostate_permute.R ├── poisson_univariate.R ├── poisson_univariate_condition.R ├── poisson_univariate_permute.R ├── poisson_univariate_simulDE.R └── poisson_univariate_simulnull.R /LICENSE: -------------------------------------------------------------------------------- 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 | 635 | Copyright (C) 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 | Copyright (C) 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 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Single-cell eQTL analysis of memory T cells 2 | 3 | ## Overview 4 | This repo provides code used for single-cell eQTL analysis of single-cell CITE-seq data from 500,089 memory T cells. If you are interested in code for any additional analyses from our study, please contact us at aparnanathan@g.harvard.edu 5 | 6 | This study is available as a preprint: 7 | 8 | > Nathan A, et al, 2021. Modeling memory T cell states at single-cell resolution identifies in vivo state-dependence of eQTLs influencing disease. [bioRxiv](https://doi.org/10.1101/2021.07.29.454316). 9 | 10 | ## Data availability 11 | 12 | Single-cell data are availale on GEO (Accession: GSE158769) and can be interactively browsed on [our website](https://immunogenomics.io/tbru/). 13 | Genotyping data are available on dbGaP (Accession: phs002025). 14 | 15 | Please send any questions regarding the study, data, or analyses to Aparna Nathan (aparnanathan@g.harvard.edu). 16 | -------------------------------------------------------------------------------- /scripts/external_data/Randolph2021/mock_pbmc_processing.R: -------------------------------------------------------------------------------- 1 | # Scripts to process mock-treated PBMC single-cell data from Randolph, et al. (2021) Science 2 | 3 | # Load libraries 4 | 5 | library(Seurat) 6 | library(Matrix) 7 | library(singlecellmethods) 8 | library(dplyr) 9 | library(harmony) 10 | library(uwot) 11 | 12 | # Load data 13 | ge_counts <- readRDS('/path/randolph_raw.rds') # 19248 236993 (genes x cells) 14 | meta.data <- readRDS('/path/randolph_meta.rds') # 236993 17 15 | 16 | # Remove IAV genes from Gene count matrix 17 | iav_genes <- c('PB2','PB1','PA','HA','NP','NA','M2','M1','NEP','NS1') 18 | ge_counts <- ge_counts[!rownames(ge_counts) %in% iav_genes,] 19 | 20 | # Select MOCK 21 | meta.data <- meta.data[meta.data$SOC_infection_status=='NI',] # 124976 cells 22 | ge_counts <- ge_counts[,colnames(ge_counts) %in% rownames(meta.data)] 23 | 24 | # Select cells with > 500 genes 25 | meta.data <- meta.data[meta.data$nFeature_RNA>500,] # 108,209 18 26 | ge_counts <- ge_counts[ ,colnames(ge_counts) %in% rownames(meta.data)] 27 | 28 | # Normalize and scale variable genes 29 | mrnaNorm <- singlecellmethods::normalizeData(ge_counts, method = "log") 30 | var_genes <- singlecellmethods::vargenes_vst(object = ge_counts, topn = 3000) 31 | mrnaCosScaled <- mrnaNorm[rownames(mrnaNorm) %in% var_genes, ] %>% scaleData() %>% cosine_normalize(2) 32 | 33 | # PCA 34 | pca_mrnaScaled <- irlba::prcomp_irlba(t(mrnaCosScaled ), 20) 35 | 36 | # Harmony batch correction 37 | harmony <- HarmonyMatrix(pca_mrnaScaled$x, meta.data, c("SOC_indiv_ID","batchID"), theta = c(1,1), lambda = c(1,1), 38 | plot_convergence = TRUE, nclust = 100, max.iter.harmony = 20, 39 | max.iter.cluster = 20, do_pca = F, verbose = T) 40 | 41 | # UMAP 42 | umap_mrnaScaled <- uwot::umap(harmony, n_neighbors = 30, metric = "euclidean", min_dist = .1) 43 | -------------------------------------------------------------------------------- /scripts/external_data/Randolph2021/monocytes_trajectory.R: -------------------------------------------------------------------------------- 1 | # Scripts to conduct trajectory analysis on monocytes from Randolph, et al. (2021) Science 2 | 3 | # Load libraries 4 | 5 | library(Seurat) 6 | library(Matrix) 7 | library(singlecellmethods) 8 | library(dplyr) 9 | library(harmony) 10 | library(uwot) 11 | library(DDRTree) 12 | library(princurve) 13 | 14 | # Load post-QC data 15 | 16 | exprs_raw <- readRDS("/path/randolph_raw.rds") 17 | exprs_raw <- exprs_raw[!row.names(exprs_raw) %in% c("PB2", "PB1", "PA", "HA", "NP", "NA", "M2", "M1", "NEP", "NS1"),] 18 | meta.data <- readRDS("/path/randolph_meta.rds") 19 | 20 | # Normalize and scale 21 | 22 | exprs_raw <- exprs_raw[,row.names(meta.data)][,grepl("monocyte", meta.data$celltype)] 23 | mono <- CreateSeuratObject(counts = exprs_raw) 24 | mono <- PercentageFeatureSet(mono, pattern = "^MT-", col.name = "percent.mt") 25 | mono <- SCTransform(mono, vars.to.regress = "percent.mt", verbose = FALSE) 26 | 27 | # PCA 28 | 29 | pca_res_filter <- irlba::prcomp_irlba(t(mono@assays$SCT@scale.data), 20) 30 | 31 | # Batch correction with Harmony 32 | 33 | harmony_res <- HarmonyMatrix(pca_res_filter$x, meta.data[grepl("monocyte", meta.data$celltype),], c("SOC_indiv_ID", "batchID", "SOC_infection_status"), theta = c(1,1,1), 34 | plot_convergence = TRUE, max.iter.harmony = 10, epsilon.cluster = -Inf, epsilon.harmony = -Inf, 35 | max.iter.cluster = 10, do_pca = F, verbose = T, return_object = FALSE) 36 | 37 | # UMAP for visualization 38 | 39 | umap_res_harmony <- umap(harmony_res[,1:10], n_neighbors = 30L, metric = "euclidean", min_dist = .1) 40 | 41 | # Plot cells by treatment condition 42 | 43 | fig.size(4,5) 44 | ggplot(as.data.frame(umap_res_harmony), aes(V1, V2, color = meta.data[grepl("monocyte", meta.data$celltype), "SOC_infection_status"])) + 45 | geom_point(shape = ".") + theme_classic() + theme(legend.title = element_blank(), axis.text = element_blank(), axis.title = element_text(size = 12)) + 46 | xlab("UMAP1") + ylab("UMAP2") 47 | 48 | # Trajectory analysis with DDRTree 49 | 50 | ncells <- nrow(harmony_res) 51 | ncenter <- round(2 * 100 * log(ncells) / (log(ncells) + log(100))) 52 | 53 | ddr_args <- c(list( 54 | X = t(harmony_res), 55 | dimensions = 2, ## LOW DIMENSIONALITY 56 | ncenter = ncenter, ## number of nodes allowed in the regularization graph 57 | param.gamma = 10, ## param.gamma regularization parameter for k-means 58 | maxIter = 20, 59 | tol = 1e-3, 60 | sigma = 0.0001, 61 | verbose = FALSE)) 62 | 63 | ddrtree_res <<- do.call(DDRTree, ddr_args) 64 | pseud_integr <- t(ddrtree_res$Z) 65 | pseud_integr <- as.data.frame(pseud_integr) 66 | 67 | pc_res <- principal_curve(t(ddrtree_res$Z)) 68 | pseud_integr$pseudo <- pc_res$lambda 69 | 70 | # Plot trajectory across cells 71 | 72 | fig.size(4,5) 73 | ggplot(data = as.data.frame(umap_res_harmony), aes(x = V1, y = V2, color = pseud_integr$pseudo)) + 74 | geom_point(shape = ".") + 75 | theme_classic() + 76 | theme(legend.text=element_text(size=12)) + scale_color_viridis(option = "plasma") + 77 | theme(legend.title=element_blank(), axis.text = element_blank()) + 78 | xlab("UMAP1") + ylab("UMAP2") 79 | 80 | # Plot deciles by treatment condition composition 81 | data.frame(V3 = meta.data.mono$SOC_infection_status) %>% mutate(bin = cut(pseud_integr$pseudo, breaks = quantile(pseud_integr$pseudo, probs = seq(0, 1, .1), labels = 1:10, include.lowest = TRUE))) %>% 82 | # group_by(bin, V3) %>% summarise(count = n()) %>% filter(!is.na(bin)) %>% 83 | filter(!is.na(bin)) %>% ggplot(aes(bin, fill = V3)) + geom_bar(stat = "count", position = "fill") + theme_classic() + 84 | theme(axis.text = element_text(size = 12), axis.title = element_text(size = 14)) 85 | -------------------------------------------------------------------------------- /scripts/external_data/Smillie2019/singlecell_analysis.R: -------------------------------------------------------------------------------- 1 | # Script to process single-cell data from Smillie, et al. (2019) Cell 2 | 3 | # Load libraries 4 | 5 | library(Seurat) 6 | library(Matrix) 7 | library(singlecellmethods) 8 | library(dplyr) 9 | library(harmony) 10 | library(uwot) 11 | 12 | # Load data 13 | 14 | data <- readMM("/path/gene_sorted-Imm.matrix.mtx") 15 | genes <- read.table("/path/Imm.genes.tsv") 16 | row.names(data) <- genes$V1 17 | 18 | cells <- read.table("/path/Imm.barcodes2.tsv") 19 | colnames(data) <- cells$V1 20 | 21 | meta_data <- read.table("/path/all.meta2.txt", sep = "\t", header = T) 22 | meta_data <- meta_data[-1,] 23 | row.names(meta_data) <- meta_data$NAME 24 | meta_data$Subject <- factor(meta_data$Subject, levels = levels(meta_data$Subject)[-1]) 25 | 26 | # Select T cells 27 | 28 | meta_data$Tcell = meta_data$Cluster %in% c("CD4+ Activated Fos-hi", "CD4+ Activated Fos-lo", "CD4+ Memory", "CD4+ PD1+", "CD8+ IELs", 29 | "CD8+ IL17+", "CD8+ LP", "Cycling T", "Tregs") 30 | meta_data <- meta_data[meta_data$Tcell,] 31 | data <- data[,row.names(meta_data)] 32 | 33 | # QC cells (> 300 genes, < 20% MT UMIs) 34 | 35 | meta_data$percent_mito <- colSums(data[grepl("^MT-", row.names(data)),])/colSums(data) 36 | meta_data$nGene <- as.numeric(as.character(meta_data$nGene)) 37 | meta_data <- meta_data[meta_data$nGene > 300 & meta_data$percent_mito < .2,] 38 | data <- data[,row.names(meta_data)] 39 | 40 | # Normalize and scale 41 | 42 | data <- as(data, "dgCMatrix") 43 | var_genes <- vargenes_vst(data, meta_data$Subject, 200) 44 | exprs_norm <- normalizeData(data, method = "log") 45 | cc_genes <- c(Seurat::cc.genes$s.genes, Seurat::cc.genes$g2m.genes) 46 | exprs_cosine <- exprs_norm[var_genes[!var_genes %in% cc_genes],] %>% scaleData %>% cosine_normalize(2) 47 | 48 | # PCA 49 | 50 | pca_res_filter <- irlba::prcomp_irlba(t(exprs_cosine), 20) 51 | 52 | # Batch correction with Harmony 53 | 54 | harmony_res <- HarmonyMatrix(pca_res_filter$x, meta_data, c("Subject"), theta = c(1), 55 | plot_convergence = TRUE, max.iter.harmony = 10, epsilon.cluster = -Inf, epsilon.harmony = -Inf, 56 | max.iter.cluster = 10, do_pca = F, verbose = T, return_object = FALSE) 57 | 58 | # UMAP for visualization 59 | 60 | umap_res_harmony <- umap(harmony_res, n_neighbors = 30L, metric = "euclidean", min_dist = .1) 61 | -------------------------------------------------------------------------------- /scripts/finemap/01_calc_LD.sh: -------------------------------------------------------------------------------- 1 | # Calculate in-sample LD between variants 2 | 3 | chr=$1 4 | gene=$2 5 | 6 | 7 | /path/plinkv1.90b6.22 --vcf merged_${chr}.QC.vcf.gz \ 8 | --extract merged_${chr}_${gene}.TMP.snpIDs \ 9 | --keep-allele-order \ 10 | --r square \ 11 | --out merged_${chr}_${gene}.TMP 12 | 13 | /path/plinkv1.90b6.22 --vcf merged_${chr}.QC.vcf.gz \ 14 | --extract merged_${chr}_${gene}.TMP.snpIDs \ 15 | --keep-allele-order \ 16 | --freq \ 17 | --out merged_${chr}_${gene}.TMP 18 | 19 | sed -i 's/\t/ /g' merged_${chr}_${gene}.TMP.ld 20 | sed -i 's/nan/0/g' merged_${chr}_${gene}.TMP.ld 21 | -------------------------------------------------------------------------------- /scripts/finemap/01_normalize_blueprint.R: -------------------------------------------------------------------------------- 1 | # Script to process Blueprint data similarly to pseudobulk memory T cell data by PEER normalizing and regressing out covariates 2 | 3 | library(data.table) 4 | library(readr) 5 | library(stringr) 6 | library(peer) 7 | 8 | K = 30 9 | 10 | ### phenotype data downloaded from here: http://dcc.blueprint-epigenome.eu/#/datasets/EGAD00001002671 11 | pheno <- read.delim("/path/EGAD00001002663.pheno.txt", sep = "\t") 12 | pheno <- pheno[c("DONOR_ID","DONOR_AGE","DONOR_SEX")] 13 | pheno <- pheno[!duplicated(pheno$DONOR_ID),] 14 | row.names(pheno) <- pheno$DONOR_ID 15 | pheno <- pheno[c("DONOR_AGE","DONOR_SEX")] 16 | pheno <- as.data.frame(pheno) 17 | 18 | ### Expression data (batch corrected and normalized) 19 | norm_exp <- fread(paste0("/path/tcel_gene_nor_combat_20151109.txt.gz")) 20 | norm_exp <- as.data.frame(norm_exp) 21 | rownames(norm_exp) <- str_split_fixed(norm_exp$ens.id, "[.]", 2)[,1] 22 | norm_exp <- norm_exp[,-1] 23 | dim(norm_exp) 24 | 25 | ### select the genes that are also present in single-cell data 26 | tbru_exp <- read_tsv("/path/tbru_IDs.txt.gz") 27 | tbru_exp <- as.data.frame(tbru_exp) 28 | norm_exp <- subset(norm_exp, rownames(norm_exp) %in% tbru_exp$ids) 29 | 30 | ### inverse normal transformation 31 | rn<-apply(norm_exp,1,function(x){ 32 | qnorm( (rank(x, na.last="keep") - 0.5) / sum(!is.na(x)) ) 33 | }) 34 | 35 | ### Genotyping PCs 36 | pcs <- read.delim("/path/EGAZ00001235598_blueprint06092016_qc_pruned_noMHC.pc.eigenvec", sep = " ", header = F) 37 | row.names(pcs) <- pcs$V2 38 | pcs <- pcs[,3:ncol(pcs)] 39 | colnames(pcs) <- c(sprintf("gPC%d", seq(1,20))) 40 | pcs <- pcs[c("gPC1", "gPC2", "gPC3")] 41 | 42 | ### age group and sex as covariates 43 | covs <- merge(pheno, pcs, by='row.names') 44 | rownames(covs) <- covs[,1] 45 | covs <- covs[,-1] 46 | covs <- covs[rownames(covs) %in% rownames(rn),] 47 | covs <- covs[match(rownames(rn), rownames(covs)),] 48 | levels(covs$DONOR_AGE) <- 1:10 49 | covs$DONOR_SEX <- covs$DONOR_SEX == "Female" 50 | covs$DONOR_AGE <- as.numeric(covs$DONOR_AGE) 51 | head(covs) 52 | 53 | ### correct for covariates and 30 peer factors 54 | model = PEER() 55 | PEER_setPhenoMean(model, as.matrix(rn)) 56 | dim(PEER_getPhenoMean(model)) 57 | PEER_setAdd_mean(model, TRUE) 58 | PEER_setNk(model,K) 59 | PEER_getNk(model) 60 | PEER_setCovariates(model, as.matrix(covs)) 61 | PEER_setNmax_iterations(model,10000) 62 | 63 | #perform the inference 64 | PEER_update(model) 65 | 66 | ### Output the peer factors 67 | factors = PEER_getX(model) 68 | dump <- data.frame(id=colnames(norm_exp),factors) 69 | gz1 <- gzfile(paste0("/path/Blueprint_INT_NoePCs_3gPCs_age_sex_peer_factors_K30.txt.gz"),"w") 70 | write.table(dump, gz1, sep = "\t", quote = F, row.names = FALSE) 71 | close(gz1) 72 | 73 | ### output the residulas 74 | residuals = PEER_getResiduals(model) 75 | colnames(residuals) <- row.names(norm_exp) 76 | row.names(residuals) <- colnames(norm_exp) 77 | dump <- data.frame( ids = row.names(norm_exp),t(residuals)) 78 | gz1 <- gzfile(paste0("/path/Blueprint_INT_NoePCs_3gPCs_age_sex_peer_factors_K30_residuals.txt.gz"),"w") 79 | write.table(dump, gz1, sep = "\t", quote = F, row.names = FALSE) 80 | close(gz1) 81 | -------------------------------------------------------------------------------- /scripts/finemap/02_merge_pseudobulk_blueprint_snps.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script to merge genotypes from pseudobulk memory T cells and Blueprint 4 | 5 | module load vcftools/0.1.15 6 | module load tabix/0.2.6 7 | module load bcftools/1.10.2 8 | 9 | # Format pseudobulk genotype VCF 10 | bcftools annotate --set-id '%CHROM\_%POS\_%REF\_%FIRST_ALT' /path/geno_imputed_dosage_maf05_hg38_${chr}.vcf.gz | bgzip -c > /path/geno_imputed_dosage_maf05_hg38_${chr}_QC.vcf.gz 11 | tabix -f -p /path/geno_imputed_dosage_maf05_hg38_${chr}_QC.vcf.gz 12 | 13 | # Format Blueprint genotype VCF 14 | bcftools annotate --set-id '%CHROM\_%POS\_%REF\_%FIRST_ALT' /path/EGAZ00001235598_blueprint06092016_qc_hg38_PICARD_chr${chr}.vcf.gz | bgzip -c > /path/EGAZ00001235598_blueprint06092016_qc_hg38_PICARD_chr${chr}.QC.vcf.gz 15 | tabix -f -p vcf BLUEPRINT/PlinkFiles/EGAZ00001235598_blueprint06092016_qc_hg38_PICARD_chr${chr}.QC.vcf.gz 16 | 17 | # Merge VCFs 18 | /path/vcftools/0.1.15/bin/vcf-merge /path/EGAZ00001235598_blueprint06092016_qc_hg38_PICARD_chr${chr}.QC.vcf.gz /path/geno_imputed_dosage_maf05_hg38_${chr}_QC.vcf.gz | bgzip -c > /path/merged_chr${chr}.vcf.gz 19 | tabix -f -p vcf /path/merged_chr${chr}.vcf.gz 20 | 21 | # Get pseudobulk variants 22 | zcat /path/geno_imputed_dosage_maf05_hg38_${chr}_QC.vcf.gz | grep -v "#" | cut -f3,3 | sort | uniq -c | awk '{if (\$1 == 1) print \$2}' > /path/pseudobulk_chr${chr}.VarIDs.txt 23 | 24 | # Blueprint variants 25 | zcat /path/EGAZ00001235598_blueprint06092016_qc_hg38_PICARD_chr${chr}.QC.vcf.gz | grep -v "#" | cut -f3,3 > /path/Blueprint_chr${chr}.VarIDs.txt 26 | 27 | # Get shared variant IDs 28 | cat /path/pseudobulk_chr${chr}.VarIDs.txt /path/Blueprint_chr${chr}.VarIDs.txt | sort | uniq -c | awk '{if (\$1 == 2) print \$2}' > /merged_chr${chr}.VarIDs.txt 29 | 30 | # Merged VCF of shared variants 31 | /path/vcftools/0.1.15/bin/vcftools --gzvcf /path/merged_chr${chr}.vcf.gz --snps /path/merged_chr${chr}.VarIDs.txt --recode --out /path/merged_chr${chr}.QC --keep /path/merged_QC.vcftools.keep 32 | bgzip -f /path/merged_chr${chr}.QC.vcf 33 | tabix -f -p vcf /path/merged_chr${chr}.QC.vcf.gz 34 | -------------------------------------------------------------------------------- /scripts/finemap/02_prep_finemap_files.R: -------------------------------------------------------------------------------- 1 | # Script to prepare files for fine-mapping from FastQTL nominal output 2 | 3 | gene = "CTLA4" 4 | 5 | # Read fastqtl nominal output 6 | data <- read_delim(path1, delim=" ", col_names=F) 7 | colnames(data) <- c("EnsemblID", "var", "dist", "P", "beta") 8 | data <- data %>% filter(EnsemblID == gene) 9 | 10 | # Read minor allele frequencies per variant 11 | maf <- read.table(path2, header=T) 12 | 13 | data <- merge(data, maf, by.x="var", by.y="SNP") 14 | 15 | data$Z <- qnorm(data$P/2, lower.tail = FALSE) 16 | data$Z <- ifelse(data$beta < 0, -1*data$Z, data$Z) 17 | data$SE <- data$beta/data$Z 18 | 19 | data <- data[,c("var", "Z")] 20 | data <- unique(data) 21 | 22 | write.table(data, paste(path3,"_", gene, ".TMP.z", sep=""), quote=F, col.names=F, row.names=F) 23 | -------------------------------------------------------------------------------- /scripts/finemap/03_merge_pseudobulk_blueprint_resid.R: -------------------------------------------------------------------------------- 1 | # Script to merge PEER residuals from pseudobulk memory T cells and Blueprint bulk T cells 2 | 3 | library(data.table) 4 | library(stringr) 5 | library(readr) 6 | 7 | ## get GTF files of TSS for each gene in Cell Ranger reference (refdata-cellranger-GRCh38-3.0.0) 8 | files <- Sys.glob(file.path("/path/chr*_tss_gene_basic.bed")) 9 | 10 | tss <- rbindlist(lapply(files, function(filename) { 11 | read.table(filename, header=T) 12 | }), fill=TRUE) 13 | 14 | tss$CHR <- str_remove(tss$CHR, "chr") 15 | tss$ENS <- str_split_fixed(tss$ID, "[.]", 2)[,1] 16 | tss$start <- tss$POS-1 17 | 18 | tss <- tss[,c(1,6,4,5)] 19 | colnames(tss) <- c("CHR", "Start", "End", "ID") 20 | 21 | ## combine residuals from the two datasets -> 13978 genes 22 | tbru_exp <- read_tsv("/path/peer_residuals.txt.gz") 23 | tbru_exp <- as.data.frame(tbru_exp) 24 | rownames(tbru_exp) <- tbru_exp$ids 25 | tbru_exp <- tbru_exp[,-1] 26 | blue_exp <- read_tsv("/path/Blueprint_INT_NoePCs_3gPCs_age_sex_peer_factors_K30_residuals.txt.gz") 27 | blue_exp <- as.data.frame(blue_exp) 28 | rownames(blue_exp) <- blue_exp$ids 29 | blue_exp <- blue_exp[,-1] 30 | 31 | merged_exp <- merge(tbru_exp, blue_exp, by='row.names') 32 | merged_exp <- as.data.frame(merged_exp) 33 | dim(merged_exp) 34 | merged_exp[1:4,1:5] 35 | 36 | ### Merge with TSS data from Cell Ranger GTF -> 13963 genes 37 | data <- merge(tss, merged_exp, by.x="ID", by.y="Row.names") 38 | data <- data[with(data, order(as.numeric(CHR), as.numeric(End))),] 39 | data <- as.data.frame(data) 40 | data <- data[,c(2,3,4,1,5:ncol(data))] 41 | 42 | ### fix names to match vcf 43 | colnames(data) <- str_replace_all(colnames(data), "[.]", "-") 44 | 45 | ### create a cohort covariate 46 | #fam <- read.table("/path/SampleIDs.txt", header=F) 47 | #fam <- fam %>% dplyr::mutate(cov=ifelse(grepl('-',V1), 1, 0)) 48 | 49 | ## order expression like fam 50 | samp_names <- c(colnames(data[,1:4]), as.character(fam$V1)) 51 | data <- data[,match(samp_names, colnames(data))] 52 | data[,1] <- paste("chr", data[,1], sep="") 53 | colnames(data)[1] <- "#CHR" 54 | 55 | reg_exp <- sapply(1:nrow(data), function(x){resid(lm(unlist(data[x,-c(1:4)])~ fam$cov))}) 56 | res <- data.frame(data[,1:4],t(reg_exp)) 57 | colnames(res)[1] <- "#CHR" 58 | colnames(res) <- str_replace_all(colnames(res), "[.]", "-") 59 | write.table(res,"/path/merged.bed", sep = "\t", quote = F, row.names = FALSE) 60 | 61 | ##bgzip and index 62 | -------------------------------------------------------------------------------- /scripts/finemap/03_run_finemap.sh: -------------------------------------------------------------------------------- 1 | # Run fine-mapping with CAVIAR 2 | 3 | CAVIAR -o merged_${chr}_${gene}_SingleCausal.caviar -l merged_${chr}_${gene}.TMP.ld -z merged_${chr}_${gene}.TMP.z -c 1 4 | -------------------------------------------------------------------------------- /scripts/post_analysis/calc_singlecell_betas.R: -------------------------------------------------------------------------------- 1 | # Script to calculate single-cell betas from single-cell eQTL model interaction effects and CV scores 2 | 3 | # Load libraries 4 | library(ggplot2) 5 | library(dplyr) 6 | library(RColorBrewer) 7 | 8 | # Load eQTL results, CV scores, and UMAP coordinates 9 | 10 | sceqtl_results <- readRDS("/path/sceqtl_results.rds") 11 | cv_scores <- readRDS("/path/cca_donor_batch.rds")[,1:7] 12 | umap_res <- readRDS("/path/umap_donor_batch.rds") 13 | 14 | # Calculate single-cell betas 15 | 16 | betas <- sceqtl_results %>% filter(grepl("^G", term) & gene == "GZMA") %>% select(Estimate) %>% unlist 17 | sc_betas <- t(data.frame(X1 = rowSums(sweep(cbind(1, cv_scores[,1:7]), MARGIN=2, betas, `*`)))) 18 | 19 | # Plot eQTL betas per cell 20 | 21 | plot_eqtl <- function (ab, umap, exprs, pct = 0.95, geno) 22 | { 23 | max.cutoff = quantile(exprs[ab, ], pct) 24 | min.cutoff = quantile(exprs[ab, ], 1 - pct) 25 | tmp <- sapply(X = exprs[ab, ], FUN = function(x) { 26 | return(ifelse(test = x > max.cutoff, yes = max.cutoff, 27 | no = x)) 28 | }) 29 | tmp <- sapply(X = tmp, FUN = function(x) { 30 | return(ifelse(test = x < min.cutoff, yes = min.cutoff, 31 | no = x)) 32 | }) 33 | umap_res_plot <- cbind(umap, tmp) 34 | return(ggplot(data = as.data.frame(umap_res_plot)[sample(nrow(umap_res_plot)), 35 | ], aes(x = V1, y = V2)) + geom_point_rast(mapping = aes(color = tmp), 36 | shape = ".") + scale_color_distiller(palette = "RdYlBu", 37 | limits = c(geno - max(abs(geno - tmp)), geno + max(abs(geno - 38 | tmp)))) + theme_classic() + theme(axis.text = element_blank(), 39 | axis.title = element_blank())) 40 | } 41 | 42 | plot_eqtl("X1", umap_res, sc_betas, 1, betas[1]) 43 | -------------------------------------------------------------------------------- /scripts/post_analysis/cluster_eGenes.R: -------------------------------------------------------------------------------- 1 | # Script to cluster eGenes and test gene set enrichments 2 | 3 | # Load libraries 4 | library(dplyr) 5 | library(tidyr) 6 | library(Seurat) 7 | library(msigdbr) 8 | library(RColorBrewer) 9 | library(pheatmap) 10 | library(parallel) 11 | library(patchwork) 12 | 13 | # Load eQTL output (data frame from concatenating output of single-cell eQTL models; 195,330 rows = 6,511 eGenes x 30 fixed effects estimated for each) 14 | sceqtl_results <- readRDS("/path/sceqtl_results.rds") 15 | 16 | # Extract genotype betas, and interaction betas and p values for 7 eQTL interaction terms per eGene 17 | 18 | g_mat <- sceqtl_results %>% filter(term == "G") %>% select(gene, Estimate) 19 | row.names(g_mat) <- g_mat$gene 20 | 21 | beta_mat <- sceqtl_results %>% filter(grepl("G:CV", term)) %>% select(gene, term, Estimate) %>% spread(term, Estimate) 22 | row.names(beta_mat) <- beta_mat$gene 23 | beta_mat <- beta_mat[,-1] 24 | 25 | p_mat <- sceqtl_results %>% filter(grepl("G:CV", term)) %>% select(gene, term, pval) %>% spread(term, pval) 26 | row.names(p_mat) <- p_mat$gene 27 | p_mat <- p_mat[,-1] 28 | 29 | # Identify significant interactions 30 | 31 | sig_mat <- beta_mat 32 | sig_mat[p_mat >= .05/7/nrow(p_mat)] = 0 33 | 34 | # Adjust beta signs 35 | 36 | beta_mat <- beta_mat*sign(g_mat[row.names(beta_mat),"Estimate"]) 37 | 38 | # Louvain clustering of re-scaled betas 39 | 40 | snn_ref <- BuildSNNSeurat(apply(beta_mat[rowSums(sig_mat != 0) > 1,], 1, function(x){ 41 | lower = ifelse(any(x < 0), min(x), 0) 42 | upper = ifelse(any(x > 0), max(x), 0) 43 | extreme = ifelse(abs(lower) > upper, abs(lower), upper) 44 | 45 | return(x/extreme) 46 | }) %>% t, nn.eps = 0) 47 | resolution_list <- c(2,3,4,5) 48 | ids_ref <- Reduce(cbind, mclapply(resolution_list, function(res_use) { 49 | Seurat:::RunModularityClustering(SNN = snn_ref, modularity = 1, 50 | resolution = res_use, algorithm = 1, n.start = 20, 51 | n.iter = 20, random.seed = 100, print.output = FALSE, 52 | temp.file.location = NULL, edge.file.name = NULL) 53 | }, mc.preschedule = FALSE, mc.cores = min(20, length(resolution_list)))) 54 | ids_ref <- data.frame(ids_ref) 55 | rm(snn_ref) 56 | gc() 57 | 58 | # Plot heatmap of mean eGene interaction effects in clusters 59 | 60 | pheatmap(sapply(0:7, function(x){colMeans((apply(beta_mat[rowSums(sin_mat != 0) > 1,], 1, function(x){ 61 | lower = ifelse(any(x < 0), min(x), 0) 62 | upper = ifelse(any(x > 0), max(x), 0) 63 | extreme = ifelse(abs(lower) > upper, abs(lower), upper) 64 | 65 | return(x/extreme) 66 | }) %>% t)[ids_ref$V2 == x,])}), 67 | color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(200), 68 | breaks = seq(-1, 1, .01), 69 | cluster_cols = F, cluster_rows = F, scale = "none", filename = "plot.pdf", height = 6, width = 12) 70 | 71 | # Plot eGene clusters 72 | 73 | fig.size(6,12) 74 | Reduce(`+`, lapply(0:7, function(x){ 75 | ggplot(apply(beta_mat[rowSums(sig_mat != 0) > 1,], 1, function(x){ 76 | lower = ifelse(any(x < 0), min(x), 0) 77 | upper = ifelse(any(x > 0), max(x), 0) 78 | extreme = ifelse(abs(lower) > upper, abs(lower), upper) 79 | 80 | return(x/extreme) 81 | }) %>% t %>% data.frame %>% filter(ids_ref$V2 == x) %>% rownames_to_column("gene") %>% gather(CV, direction, -gene), aes(x = CV, y = direction, group = gene)) + geom_line(color = "lightgrey") + geom_line(data = data.frame(V1 = paste0("GxCV", 1:7), V2 = colMeans((apply(beta_mat[rowSums(sig_mat != 0) > 1,], 1, function(x){ 82 | lower = ifelse(any(x < 0), min(x), 0) 83 | upper = ifelse(any(x > 0), max(x), 0) 84 | extreme = ifelse(abs(lower) > upper, abs(lower), upper) 85 | 86 | return(x/extreme) 87 | }) %>% t)[ids_ref$V2 == x,])), aes(V1, V2, group = NA), color = "red") + theme_classic() + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust =1 88 | )) 89 | })) + plot_layout(ncol = 4) 90 | 91 | # Test enrichment of GO term gene sets from MSigDB 92 | 93 | gene_sets = msigdbr(species = "Homo sapiens", category = "C5") 94 | msigdbr_list = split(x = gene_sets$gene_symbol, f = gene_sets$gs_name) 95 | enrich_p = lapply(msigdbr_list, function(gene_set){ 96 | x = length(intersect(gene_set, row.names(beta_mat[rowSums(sig_mat != 0) > 1,])[ids_ref$V2 == 0])) 97 | y = length(intersect(gene_set, row.names(beta_mat[rowSums(sig_mat != 0) > 1,])[ids_ref$V2 != 0])) 98 | 99 | return(fisher.test(matrix(c(x, y, sum(ids_ref$V2 == 0)-x, sum(ids_ref$V2 != 0)-y), nrow = 2))$p.value) 100 | }) 101 | enrich_OR = lapply(msigdbr_list, function(gene_set){ 102 | x = length(intersect(gene_set, row.names(beta_mat[rowSums(sig_mat != 0) > 1,])[ids_ref$V2 == 0])) 103 | y = length(intersect(gene_set, row.names(beta_mat[rowSums(sig_mat != 0) > 1,])[ids_ref$V2 != 0])) 104 | 105 | return(fisher.test(matrix(c(x, y, sum(ids_ref$V2 == 0)-x, sum(ids_ref$V2 != 0)-y), nrow = 2))$estimate) 106 | }) 107 | -------------------------------------------------------------------------------- /scripts/post_analysis/enrich_atac.R: -------------------------------------------------------------------------------- 1 | # Script to calculate enrichment of eQTL signal in ATAC-seq peaks from sorted T cell types in Calderon, et al. (2019)Nat Genetics 2 | 3 | # Load libraries 4 | library(data.table) 5 | library(dplyr) 6 | library(tidyr) 7 | library(edgeR) 8 | 9 | args = commandArgs(trailingOnly=TRUE) 10 | celltype = args[1] 11 | n = args[2] 12 | 13 | # Load table of eGenes and lead SNPs 14 | 15 | genes <- read.table("/path/pseudobulk_sig_eqtls.txt", stringsAsFactors = F) 16 | 17 | # Load keys to convert gene names to Ensembl IDs (from Cell Ranger refdata-cellranger-GRCh38-3.0.0) and GRCh38 variants to hg19 (based on liftOver) 18 | ensg_ids <- fread("/path/features.tsv.gz") 19 | conversion_key <- fread("/path/convert_grch38_hg19.txt.gz") 20 | 21 | # Define open regions based on ATAC 22 | atac <- fread("/data/srlab2/anathan/external_data/Calderon2019/GSE118189_ATAC_counts.txt.gz") 23 | atac <- data.frame(atac, check.names = F) 24 | atac <- atac[,grepl(paste(c("V1",unique(substring(colnames(atac), 6, nchar(colnames(atac))-2))[6:19]), collapse = "|"), colnames(atac))] 25 | 26 | atac[,-1] <- cpm(atac[,-1]) 27 | atac_bed <- data.frame(V1 = atac$V1) %>% separate(V1, into = c("V1", "V2", "V3"), sep = "_") 28 | atac_bed$V2 <- as.numeric(atac_bed$V2) 29 | atac_bed$V3 <- as.numeric(atac_bed$V3) 30 | atac_bed$V4 = rowMeans(atac[,grepl(paste0("-", celltype, "-U"), colnames(atac))]) 31 | atac_bed$size = as.numeric(atac_bed$V3) - as.numeric(atac_bed$V2) 32 | 33 | genes <- genes %>% left_join(ensg_ids, by = c("V3"="V2")) %>% separate(V1, into = c("chr", "pos", "ref", "alt"), sep = "_") 34 | 35 | results <- sapply(1:nrow(genes), function(x) { 36 | if(file.exists(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V1.y[x], "_SingleCausal.caviar_post"))) { 37 | cav <- read.table(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_SingleCausal.caviar_post"), header = T) 38 | if(file.exists(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_ConditionSecondSNP_SingleCausal.caviar_post"))) { 39 | cav <- read.table(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_ConditionSecondSNP_SingleCausal.caviar_post"), header = T)} 40 | if(max(cav$Prob_in_pCausalSet) >=.5) { 41 | cav <- cav %>% left_join(conversion_key, by = c("SNP_ID"="V1")) %>% separate(V2, into = c("chr", "pos", "ref", "alt"), sep = "_") 42 | cav$overlap = Infi 43 | tmp <- atac_bed %>% filter(V1 == genes$chr[y]) 44 | cav$overlap = sapply(as.numeric(cav$pos), function(x){sum(tmp$V2 <= x & tmp$V3 >= x & tmp$V4 > n)}) 45 | e_overlap = sum(cav$overlap) 46 | e_pip = sum(cav$Prob_in_pCausalSet) 47 | e_num = sum(cav$Prob_in_pCausalSet*cav$overlap) 48 | return(c(x, e_overlap, e_pip, e_num, nrow(cav), sum(cumsum(sort(cav$Prob_in_pCausalSet, decreasing = T)) <= .95)))} 49 | else{ 50 | return(c(NA,NA,NA,NA,NA,NA)) 51 | }} 52 | 53 | else { 54 | return(c(NA,NA,NA,NA,NA,NA)) 55 | }}) 56 | 57 | # Load eQTL output (data frame from concatenating output of single-cell eQTL models; 195,330 rows = 6,511 eGenes x 30 fixed effects estimated for each) 58 | sceqtl_results <- readRDS("/path/sceqtl_results.rds") 59 | 60 | genes_sig <- sceqtl_results %>% filter(term == "G") %>% mutate(qval = qvalue::qvalue(lrt_pval)$qvalue) %>% filter(qval < .05) %>% select(gene) %>% unlist 61 | 62 | results <- results %>% filter(!is.na(X1)) 63 | 64 | # Find intersection with eGenes significant in Peruvian/Blueprint joint meta-analysis 65 | genes_bp <- read.table("/path/joint_bp_sig_eqtls.txt") 66 | genes_int <- genes[genes$V3 %in% genes_bp$V3,] 67 | 68 | # Calculate mean enrichment stats 69 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% select(es) %>% unlist %>% mean 70 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 71 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(!gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 72 | -------------------------------------------------------------------------------- /scripts/post_analysis/enrich_impact.R: -------------------------------------------------------------------------------- 1 | # Script to calculate enrichment of eQTL signal in T cell-specific regulatory regions defined by IMPACT (Amariuta, et al. [2019] AJHG) 2 | 3 | # Load libraries 4 | library(data.table) 5 | library(dplyr) 6 | library(tidyr) 7 | 8 | # Load table of eGenes and lead SNPs 9 | 10 | genes <- read.table("/path/pseudobulk_sig_eqtls.txt", stringsAsFactors = F) 11 | 12 | # Load keys to convert gene names to Ensembl IDs (from Cell Ranger refdata-cellranger-GRCh38-3.0.0) and GRCh38 variants to hg19 (based on liftOver) 13 | ensg_ids <- fread("/path/features.tsv.gz") 14 | conversion_key <- fread("/path/convert_grch38_hg19.txt.gz") 15 | 16 | # Load +/- 2kb windows around all 6,511 eGenes 17 | tss_track <- read.table("/path/tss_track.txt", header = F) 18 | 19 | genes <- genes %>% left_join(ensg_ids, by = c("V3"="V2")) %>% separate(V1, into = c("chr", "pos", "ref", "alt"), sep = "_") 20 | results <- c() 21 | for(x in 1:22) { 22 | impact <- fread(paste0("/path/IMPACT_predictions_92_chr", x, "_bedgraph.txt.gz"), header = T) 23 | 24 | genes_chr <- genes %>% filter(chr == paste0("chr",x)) 25 | 26 | results_chr <- sapply(1:nrow(genes_chr), function(y) { 27 | if(file.exists(paste0("/path/chr", x, "/chr", x, "_", genes_chr$V2.y[y], "_SingleCausal.caviar_post"))) { 28 | cav <- read.table(paste0("/path/chr", x, "/chr", x, "_", genes_chr$V2.y[y], "_SingleCausal.caviar_post"), header = T) 29 | if(file.exists(paste0("/path/chr", x, "/chr", x, "_", genes$V2.y[y], "_ConditionSecondSNP_SingleCausal.caviar_post"))) { 30 | cav <- read.table(paste0("/path/chr", x, "/chr", x, "_", genes$V2.y[y], "_ConditionSecondSNP_SingleCausal.caviar_post"), header = T)} 31 | if(max(cav$Prob_in_pCausalSet) >=.5) { 32 | cav <- cav %>% left_join(conversion_key, by = c("SNP_ID"="V1")) %>% separate(V2, into = c("chr", "pos", "ref", "alt"), sep = "_") 33 | tmp <- impact[tail(which(impact$V2 < min(as.numeric(cav$pos))), n = 1):head(which(impact$V3 > max(as.numeric(cav$pos))), n = 1),] 34 | cav$overlap = Inf 35 | cav$overlap = sapply(as.numeric(cav$pos), function(z){ifelse(any(tss_track$V1 == paste0("chr", x) & tss_track$V2 <= z & tss_track$V3 >= z), 0, tmp$V4[tmp$V2 <= z & tmp$V3 >= z])}) 36 | e_overlap = sum(cav$overlap) 37 | e_pip = sum(cav$Prob_in_pCausalSet) 38 | e_num = sum(cav$Prob_in_pCausalSet*cav$overlap) 39 | return(c(which(genes$V2.y == genes_chr$V2.y[y]), e_overlap, e_pip, e_num, nrow(cav), sum(cumsum(sort(cav$Prob_in_pCausalSet, decreasing = T)) <= .95)))} 40 | else{ 41 | return(c(NA,NA,NA,NA,NA,NA)) 42 | }} 43 | 44 | else { 45 | return(c(NA,NA,NA,NA,NA,NA)) 46 | }}) 47 | results <- rbind(results, results_chr) 48 | } 49 | 50 | # Load eQTL output (data frame from concatenating output of single-cell eQTL models; 195,330 rows = 6,511 eGenes x 30 fixed effects estimated for each) 51 | sceqtl_results <- readRDS("/path/sceqtl_results.rds") 52 | 53 | genes_sig <- sceqtl_results %>% filter(term == "G") %>% mutate(qval = qvalue::qvalue(lrt_pval)$qvalue) %>% filter(qval < .05) %>% select(gene) %>% unlist 54 | 55 | results <- results %>% filter(!is.na(X1)) 56 | 57 | # Find intersection with eGenes significant in Peruvian/Blueprint joint meta-analysis 58 | genes_bp <- read.table("/path/joint_bp_sig_eqtls.txt") 59 | genes_int <- genes[genes$V3 %in% genes_bp$V3,] 60 | 61 | # Calculate mean enrichment stats 62 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% select(es) %>% unlist %>% mean 63 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 64 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(!gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 65 | -------------------------------------------------------------------------------- /scripts/post_analysis/enrich_tss.R: -------------------------------------------------------------------------------- 1 | # Script to calculate enrichment of eQTL signal in transcription start sites 2 | 3 | # Load libraries 4 | library(data.table) 5 | library(dplyr) 6 | library(tidyr) 7 | 8 | # Load table of eGenes and lead SNPs 9 | 10 | genes <- read.table("/path/pseudobulk_sig_eqtls.txt", stringsAsFactors = F) 11 | 12 | # Load keys to convert gene names to Ensembl IDs (from Cell Ranger refdata-cellranger-GRCh38-3.0.0) and GRCh38 variants to hg19 (based on liftOver) 13 | ensg_ids <- fread("/path/features.tsv.gz") 14 | conversion_key <- fread("/path/convert_grch38_hg19.txt.gz") 15 | 16 | # Load +/- 2kb windows around all 6,511 eGenes 17 | my_tss <- read.table("/path/tss_track.txt", header = F) 18 | 19 | genes <- genes %>% left_join(ensg_ids, by = c("V3"="V2")) %>% separate(V1, into = c("chr", "pos", "ref", "alt"), sep = "_") 20 | 21 | results <- sapply(1:nrow(genes), function(x) { 22 | if(file.exists(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V1.y[x], "_SingleCausal.caviar_post"))) { 23 | cav <- read.table(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_SingleCausal.caviar_post"), header = T) 24 | if(file.exists(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_ConditionSecondSNP_SingleCausal.caviar_post"))) { 25 | cav <- read.table(paste0("/path/", genes$chr[x], "/", genes$chr[x], "_", genes$V2.y[x], "_ConditionSecondSNP_SingleCausal.caviar_post"), header = T)} 26 | if(max(cav$Prob_in_pCausalSet) >=.5) { 27 | cav <- cav %>% left_join(conversion_key, by = c("SNP_ID"="V1")) %>% separate(V2, into = c("chr", "pos", "ref", "alt"), sep = "_") 28 | cav$overlap = Inf 29 | tmp <- my_tss %>% filter(V1 == genes$chr[x]) 30 | cav$overlap = sapply(as.numeric(cav$pos), function(y){sum(tmp$V2 <= y & tmp$V3 >= y)}) 31 | e_overlap = sum(cav$overlap) 32 | e_pip = sum(cav$Prob_in_pCausalSet) 33 | e_num = sum(cav$Prob_in_pCausalSet*cav$overlap) 34 | return(c(x, e_overlap, e_pip, e_num, nrow(cav), sum(cumsum(sort(cav$Prob_in_pCausalSet, decreasing = T)) <= .95)))} 35 | else{ 36 | return(c(NA,NA,NA,NA,NA,NA)) 37 | }} 38 | 39 | else { 40 | return(c(NA,NA,NA,NA,NA,NA)) 41 | }}) 42 | 43 | # Load eQTL output (data frame from concatenating output of single-cell eQTL models; 195,330 rows = 6,511 eGenes x 30 fixed effects estimated for each) 44 | sceqtl_results <- readRDS("/path/sceqtl_results.rds") 45 | 46 | genes_sig <- sceqtl_results %>% filter(term == "G") %>% mutate(qval = qvalue::qvalue(lrt_pval)$qvalue) %>% filter(qval < .05) %>% select(gene) %>% unlist 47 | 48 | results <- results %>% filter(!is.na(X1)) 49 | 50 | # Find intersection with eGenes significant in Peruvian/Blueprint joint meta-analysis 51 | genes_bp <- read.table("/path/joint_bp_sig_eqtls.txt") 52 | genes_int <- genes[genes$V3 %in% genes_bp$V3,] 53 | 54 | # Calculate mean enrichment stats 55 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% select(es) %>% unlist %>% mean 56 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 57 | results %>% mutate(es = X4/X5/(X2/X5*X3/X5)) %>% filter(!is.na(es)) %>% mutate(gene = genes$V3[X1]) %>% filter(gene %in% genes_int$V3) %>% filter(!gene %in% genes_sig) %>% select(es) %>% unlist %>% mean 58 | -------------------------------------------------------------------------------- /scripts/post_analysis/run_coloc.R: -------------------------------------------------------------------------------- 1 | # Script to run coloc on GWAS summary statistics and pseudobulk eQTL results 2 | 3 | # Load libraries 4 | 5 | library(data.table) 6 | library(coloc) 7 | library(dplyr) 8 | library(tidyr) 9 | 10 | # Load table of eGenes and lead SNPs 11 | 12 | genes <- read.table("/path/pseudobulk_sig_eqtls.txt", stringsAsFactors = F) 13 | genes <- genes %>% mutate(snp = V1) %>% separate(V1, into = c("chr", "pos", "ref", "alt"), sep = "_") %>% mutate(chr = gsub("chr", "", chr)) 14 | genes$nsnps = NA 15 | genes$PP.H0.abf = NA 16 | genes$PP.H1.abf = NA 17 | genes$PP.H2.abf = NA 18 | genes$PP.H3.abf = NA 19 | genes$PP.H4.abf = NA 20 | 21 | # Load GWAS summary statistics 22 | 23 | gwas_sumstat <- fread("/path/24076602-GCST005531-EFO_0003885.h.tsv.gz") 24 | gwas_sumstat <- gwas_sumstat %>% filter(!is.na(hm_variant_id)) %>% filter(!duplicated(hm_variant_id)) %>% filter(!is.na(hm_beta)) 25 | 26 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 27 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 28 | 29 | gwas_dataset = list(type = "cc", 30 | snp = paste0("chr", gwas_sumstat$hm_variant_id), 31 | beta = gwas_sumstat$hm_beta, 32 | varbeta = gwas_sumstat$standard_error^2, 33 | position = gwas_sumstat$hm_pos) 34 | check_dataset(gwas_dataset) 35 | 36 | for(x in 1:22){ 37 | eqtl <- fread(paste0("/path/fastqtl_nominal/chr", x, "/chr", x, "_output.nominal.tbru.txt.gz")) 38 | 39 | resid <- data.frame(fread(paste0("/path/chr", x, "_input.bed.gz"))) 40 | 41 | for(i in which(genes$chr == x)){ 42 | G <- t(subset(geno, ID == sub(":", "_", genes$snp[i]))[,unique(as.character(meta$donor))])[,1] 43 | select_eqtl <- eqtl %>% filter(V1 == genes$V2[i]) %>% mutate(z = qnorm(V4/2, lower.tail = F)*sign(V5), se = V5/z) 44 | 45 | eQTL_dataset = list(beta = select_eqtl$V5, 46 | varbeta = select_eqtl$se^2, 47 | sdY = sd(unlist(resid[resid$ID == genes$V2[i],-c(1:4)])), 48 | type = "quant", 49 | snp = select_eqtl$V2) 50 | tryCatch({ 51 | my.res <- coloc.abf(dataset1=eQTL_dataset, 52 | dataset2=gwas_dataset) 53 | 54 | genes[i,8:13] = my.res$summary 55 | }, error=function(cond){return(NA)}) 56 | } 57 | } 58 | -------------------------------------------------------------------------------- /scripts/post_analysis/run_homer.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script to run HOMER on promoters of eGenes interacting with given CV 4 | x=$1 5 | 6 | perl /path/homer/bin/findMotifs.pl /path/homer_out/CV${x}_genes.txt human /path/homer_out/CV${x}/ -start -2000 -end 2000 -len 8,10 -p 4 7 | 8 | # Script to run HOMER on BED file of +/- 20bp windows around eQTL variants interacting with given CV 9 | x=$1 10 | 11 | perl /path/homer/bin/findMotifsGenome.pl /path/homer_out/CV${x}_snps.bed hg19 /path/homer_out/CV${x}_snps_given/ -size given 12 | -------------------------------------------------------------------------------- /scripts/pseudobulk/01_make_pseudobulk_exprs.R: -------------------------------------------------------------------------------- 1 | # Script to make pseudobulk expression profiles 2 | 3 | # Load libraries 4 | library(data.table) 5 | library(presto) 6 | library(DESeq2) 7 | library(edgeR) 8 | library(dplyr) 9 | 10 | # Load metadata (from GEO: GSE158769) 11 | meta_data <- fread("/path/GSE158769_meta_data.txt.gz") 12 | meta_data <- meta_data %>% mutate(id = paste(donor, batch, sep = "_")) 13 | 14 | # Load RAW UMI COUNTS matrix (from GEO) 15 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 16 | 17 | # Remove replicates 18 | reps <- meta_data %>% filter(!duplicated(id)) %>% arrange(batch) %>% filter(duplicated(donor)) %>% select(id) %>% unlist 19 | meta_data <- meta_data %>% filter(!id %in% reps) 20 | exprs_raw <- exprs_raw[,meta_data$cell_id] 21 | 22 | # Make pseudobulk profiles; specify variable to make pseudobulk samples for 23 | all_collapse <- collapse_counts(exprs_raw, meta_data, c("donor")) 24 | colnames(all_collapse$counts_mat) <- all_collapse$meta_data$donor 25 | 26 | # Save genes (rows) x samples (columns) matrix 27 | 28 | out <- data.frame(id=row.names(all_collapse$counts_mat), all_collapse$counts_mat) 29 | 30 | gz1 <- gzfile("/path/pseudobulk_exprs_mat.txt.gz","w") 31 | write.table(out, gz1, sep = "\t", quote = F, row.names = FALSE) 32 | close(gz1) 33 | -------------------------------------------------------------------------------- /scripts/pseudobulk/02_normalize.R: -------------------------------------------------------------------------------- 1 | # Script to normalize pseudobulk expression 2 | 3 | # Load libraries 4 | library(ggplot2) 5 | library(Seurat) 6 | library(data.table) 7 | library(ggrepel) 8 | library(peer) 9 | library(edgeR) 10 | 11 | # Load donor-level covariates (from dbGaP: phs002467) 12 | pheno <- read.delim("/path/donor_covariates.txt", sep = " ") 13 | pheno <- pheno[!duplicated(pheno$donor),] 14 | row.names(pheno) <- pheno$donor 15 | 16 | # Load genotype PCs 17 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 18 | row.names(pcs) <- pcs$V2 19 | pcs <- pcs[,3:ncol(pcs)] 20 | 21 | # PEER normalization: regress out latent variables (Stegle 2010, PLoS Comp Bio) 22 | 23 | # Based on GTEx 2017 Nature: 24 | K = 45 25 | 26 | # Replace with reading in your pseudobulk expression matrix 27 | sum_exp <- fread("/path/pseudobulk_exprs_mat.txt.gz") 28 | sum_exp <- as.data.frame(sum_exp) 29 | row.names(sum_exp) <- sum_exp[,1] 30 | sum_exp <- sum_exp[,-1] 31 | 32 | # Remove genes that with non-zero expression in <= half of the samples 33 | sum_exp <- sum_exp[rowSums(sum_exp > 0) > .5*ncol(sum_exp),] 34 | 35 | # Log2 CPM normalization 36 | norm_exp <- log2(cpm(sum_exp)+1) 37 | 38 | # Inverse normal transformation 39 | rn<-apply(norm_exp,1,function(x){ 40 | qnorm( (rank(x, na.last="keep") - 0.5) / sum(!is.na(x)) ) 41 | }) 42 | rn<-t(rn) 43 | 44 | # Select known covariates to regress out (age, sex, and 5 genotype PCs) 45 | covs <- cbind(pheno[colnames(norm_exp),"age"], 46 | pheno[colnames(norm_exp),"Sex"] == "F", 47 | pcs[colnames(norm_exp),1:5]) 48 | colnames(covs) <- c("age", "female", "PC1", "PC2", "PC3", "PC4", "PC5") 49 | 50 | model = PEER() 51 | PEER_setPhenoMean(model, as.matrix(t(rn))) 52 | PEER_setAdd_mean(model, TRUE) 53 | PEER_setNk(model,K) 54 | PEER_getNk(model) 55 | PEER_setCovariates(model, as.matrix(covs)) 56 | PEER_setNmax_iterations(model,10000) 57 | PEER_update(model) 58 | 59 | # Save residuals 60 | residuals = PEER_getResiduals(model) 61 | colnames(residuals) <- row.names(norm_exp) 62 | row.names(residuals) <- colnames(norm_exp) 63 | dump <- data.frame( ids = row.names(norm_exp),t(residuals)) 64 | gz1 <- gzfile("/path/peer_residuals.txt.gz","w") 65 | write.table(dump, gz1, sep = "\t", quote = F, row.names = FALSE) 66 | close(gz1) 67 | -------------------------------------------------------------------------------- /scripts/pseudobulk/03_make_BED.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Script to make BED file from PEER residuals 4 | 5 | # BED header 6 | echo -e "#Chr\tStart\tEnd\tID" > tmp 7 | zcat /path/peer_residuals.txt.gz | head -n1 | cut -f2- | sed 's/\./-/g' | paste tmp - > header 8 | 9 | zcat /path/peer_residuals.txt.gz | sed -e "1d" | 10 | sort -k 1b,1 > tmp01 11 | 12 | # Needs to be by chromosome for FastQTL 13 | for chr in $(seq 1 22);do 14 | echo chr$chr ... 15 | 16 | # Get gene TSS using GTFs from Cell Ranger (refdata-cellranger-GRCh38-3.0.0) 17 | sed -e "1d" /path/chr${chr}_tss_gene_basic.bed | 18 | awk -v chr=$chr '{if($1==chr){print $2,chr,$4 -1,$4}}' | 19 | awk '{ $1 = substr($1, 1, 15)} 1' | 20 | sort -k 1b,1 > tmp02 21 | 22 | outfile="/path/chr${chr}_input.bed" 23 | # Link ENSG to gene name using features.tsv.gz file from cellranger-3.1.0, GRCh38 24 | zcat /path/features.tsv.gz | 25 | grep ^ENSG | 26 | sort -k 1b,1 | 27 | join - tmp02 | 28 | awk '{print $2,$5,$6,$7,$2}' | 29 | sort -k 1b,1 | 30 | uniq -f 4 | 31 | join - tmp01 | 32 | cut -d " " -f2- | 33 | sort -k 2,2n | 34 | perl -pe "s/ /\t/g" | 35 | awk '{print $0}' | 36 | cat header - > "$outfile" 37 | 38 | bgzip -f "$outfile" 39 | 40 | tabix -f -p bed "$outfile.gz" 41 | done 42 | -------------------------------------------------------------------------------- /scripts/pseudobulk/04_fastqtl_nominal.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | # Script to run FastQTL (Ongen 2015, Bioinformatics) 4 | 5 | # VCF: post QC and imputation genotype dosage VCF (genotype data from dbGaP: phs002025) 6 | # BED: output from previous script 7 | # Region: select chromosome 8 | # out: output file 9 | 10 | chr=$1 11 | 12 | mkdir -p /path/fastqtl_nominal/chr$chr 13 | 14 | /path/fastQTL \ 15 | --vcf /path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz \ 16 | --bed /path/chr${chr}_input.bed.gz \ 17 | --region chr$chr \ 18 | --out /path/fastqtl_nominal/chr$chr/chr${chr}_output.nominal.tbru.txt 19 | 20 | gzip -f /path/fastqtl_nominal/chr$chr/chr${chr}_output.nominal.tbru.txt 21 | -------------------------------------------------------------------------------- /scripts/pseudobulk/05_fastqtl_permute.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | # Script to run FastQTL (Ongen 2015, Bioinformatics) 4 | 5 | # VCF: post QC and imputation genotype dosage VCF (genotype data from dbGaP: phs002025) 6 | # BED: output from previous script 7 | # Region: select chromosome 8 | # out: output file 9 | 10 | chr=$1 11 | 12 | mkdir -p /path/fastqtl_permute/chr$chr 13 | 14 | /path/fastQTL \ 15 | --vcf /path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz \ 16 | --bed /path/chr${chr}_input.bed.gz \ 17 | --region chr$chr \ 18 | --permute 1000 \ 19 | --out /path/fastqtl_nominal/chr$chr/chr${chr}_output.permute.tbru.txt 20 | 21 | gzip -f /path/fastqtl_nominal/chr$chr/chr${chr}_output.permute.tbru.txt 22 | -------------------------------------------------------------------------------- /scripts/singlecell/linear_nostate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell linear mixed effects eQTL model without cell state interaction 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load normalized UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_norm <- fread("/path/GSE158769_exprs_norm.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_norm[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 37 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 38 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5]) 39 | data$G <- as.numeric(as.character(data$G)) 40 | 41 | full_model <- lme4::lmer(formula = E ~ G + (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 +PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 42 | data= data, REML = F) 43 | null_model <- lme4::lmer(formula = E ~ (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 44 | data= data, REML = F) 45 | model_lrt <- anova(null_model, full_model) 46 | 47 | out <- summary(test)$coefficients 48 | colnames(out) <- c("Estimate","Std.Error","tvalue") 49 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 50 | -------------------------------------------------------------------------------- /scripts/singlecell/linear_nostate_permute.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell linear mixed effects eQTL model without cell state interaction on permuted data (permuting genotype) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load normalized UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_norm <- fread("/path/GSE158769_exprs_norm.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | set.seed(1010*as.numeric) 26 | E <- as.numeric(exprs_norm[gene,]) 27 | G <- subset(geno, ID == snp) 28 | colnames(G)[10:276] <- sample(colnames(G)[10:276]) 29 | G <- t(G[,as.character(meta$donor)])[,1] 30 | IND <- factor(meta$donor) 31 | B <- meta$batch 32 | AGE <- scale(meta$tbru_age) 33 | SEX <- meta$Sex 34 | nUMI <- scale(log(meta$nUMI)) 35 | MT <- meta$percent_mito 36 | PC <- pcs[as.character(meta$donor),1:5] 37 | expPC <- pca_res[,1:5] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5]) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::lmer(formula = E ~ G + (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 +PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 45 | data= data, REML = F) 46 | null_model <- lme4::lmer(formula = E ~ (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 47 | data= data, REML = F) 48 | model_lrt <- anova(null_model, full_model) 49 | 50 | out <- summary(test)$coefficients 51 | colnames(out) <- c("Estimate","Std.Error","tvalue") 52 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/linear_univariate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell linear mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load normalized UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_norm <- fread("/path/GSE158769_exprs_norm.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_norm[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariate (e.g., CD4+ binary annotation from gating on CITE-seq surface protein) 37 | cov <- readRDS("/path/citeseq_gates.rds")[,"cd4"] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 45 | data= data, REML = F) 46 | null_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 47 | data= data, REML = F) 48 | model_lrt <- anova(null_model, full_model) 49 | 50 | out <- summary(test)$coefficients 51 | colnames(out) <- c("Estimate","Std.Error","tvalue") 52 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/linear_univariate_permute.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell linear mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) on permuted data (permuting cell state) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load normalized UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_norm <- fread("/path/GSE158769_exprs_norm.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Load interaction covariate (e.g., CD4+ binary annotation from gating on CITE-seq surface protein) 25 | set.seed(1010) 26 | cov <- unlist(readRDS("/path/citeseq_gates.rds")[,"cd4"]) 27 | for(x in unique(meta$donor)){ 28 | cov[meta$donor == x] <- sample(cov[meta$donor == x]) 29 | } 30 | 31 | # Make data frame of variables for model 32 | E <- as.numeric(exprs_norm[gene,]) 33 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 34 | IND <- factor(meta$donor) 35 | B <- meta$batch 36 | AGE <- scale(meta$tbru_age) 37 | SEX <- meta$Sex 38 | nUMI <- scale(log(meta$nUMI)) 39 | MT <- meta$percent_mito 40 | PC <- pcs[as.character(meta$donor),1:5] 41 | expPC <- pca_res[,1:5] 42 | 43 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 44 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 45 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 46 | data$G <- as.numeric(as.character(data$G)) 47 | 48 | full_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 49 | data= data, REML = F) 50 | null_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 51 | data= data, REML = F) 52 | model_lrt <- anova(null_model, full_model) 53 | 54 | out <- summary(test)$coefficients 55 | colnames(out) <- c("Estimate","Std.Error","tvalue") 56 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 57 | -------------------------------------------------------------------------------- /scripts/singlecell/linear_univariate_simulDE.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell linear mixed effects eQTL model with one cell state interaction (CD4+), simulating differential expression 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | frac <- as.numeric(args[3]) # fraction to which to decrease expression in CD4+ cells 12 | 13 | # Load normalized UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 14 | exprs_norm <- fread("/path/GSE158769_exprs_norm.tsv.gz") 15 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 16 | pca_res <- readRDS("/path/pca_irlba.rds") 17 | pca_res <- pca_res$x 18 | 19 | # Load genotype dosages (from dbGaP: phs002025) and PCs 20 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 21 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 22 | row.names(pcs) <- pcs$V2 23 | pcs <- pcs[,3:ncol(pcs)] 24 | 25 | # Make data frame of variables for model 26 | E <- as.numeric(exprs_norm[gene,]) 27 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 28 | IND <- factor(meta$donor) 29 | B <- meta$batch 30 | AGE <- scale(meta$tbru_age) 31 | SEX <- meta$Sex 32 | nUMI <- scale(log(meta$nUMI)) 33 | MT <- meta$percent_mito 34 | PC <- pcs[as.character(meta$donor),1:5] 35 | expPC <- pca_res[,1:5] 36 | 37 | # Load interaction covariate (e.g., CD4+ binary annotation from gating on CITE-seq surface protein) 38 | cov <- readRDS("/path/citeseq_gates.rds")[,"cd4"] 39 | 40 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 41 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 42 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 43 | data$G <- as.numeric(as.character(data$G)) 44 | data$E[data$cov == TRUE] <- data$E[data$cov == TRUE]/frac 45 | 46 | full_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 47 | data= data, REML = F) 48 | null_model <- lme4::lmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 49 | data= data, REML = F) 50 | model_lrt <- anova(null_model, full_model) 51 | 52 | out <- summary(test)$coefficients 53 | colnames(out) <- c("Estimate","Std.Error","tvalue") 54 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 55 | -------------------------------------------------------------------------------- /scripts/singlecell/nb_univariate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell negative binomial mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariate (e.g., batch-corrected CV from CCA) 37 | cov <- readRDS("/path/cca_donor_batch.rds")[,1] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::glmer.nb(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G:cov, 45 | data= data, nAGQ = 0, control = glmerControl(optimizer = "nloptwrap")) 46 | null_model <- lme4::glmer.nb(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 47 | data= data, nAGQ = 0, control = glmerControl(optimizer = "nloptwrap")) 48 | model_lrt <- anova(null_model, full_model) 49 | 50 | out <- summary(test)$coefficients 51 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 52 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_multivariate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model with multiple cell state interactions (e.g., CVs, PCs) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariates (e.g., batch-corrected CVs from CCA) 37 | cov <- readRDS("/path/cca_donor_batch.rds")[,1:7] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::glmer(formula = paste0("E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + ", 45 | paste0("CV", 1:7, collapse = " + ")," + ", paste0("G:CV", 1:7, collapse = " + ")), 46 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 47 | null_model <- lme4::glmer(formula = paste0("E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + ", 48 | paste0("CV", 1:7, collapse = " + ")), 49 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 50 | model_lrt <- anova(null_model, full_model) 51 | 52 | out <- summary(test)$coefficients 53 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 54 | out <- data.frame(gene=gene,snp=snp,term=row.names(out),out, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 55 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_nostate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model without cell state interaction 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 37 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 38 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5]) 39 | data$G <- as.numeric(as.character(data$G)) 40 | 41 | full_model <- lme4::glmer(formula = E ~ G + (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 +PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 42 | family = "poisson", nAGQ=0, data= data, control = glmerControl(optimizer = "nloptwrap")) 43 | null_model <- lme4::glmer(formula = E ~ (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 44 | family = "poisson", nAGQ=0, data= data, control = glmerControl(optimizer = "nloptwrap")) 45 | model_lrt <- anova(null_model, full_model) 46 | 47 | out <- summary(test)$coefficients 48 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 49 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 50 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_nostate_permute.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model without cell state interaction on permuted data (permuting genotype) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | set.seed(1010) 26 | E <- as.numeric(exprs_raw[gene,]) 27 | G <- subset(geno, ID == snp) 28 | colnames(G)[10:276] <- sample(colnames(G)[10:276]) 29 | G <- t(G[,as.character(meta$donor)])[,1] 30 | IND <- factor(meta$donor) 31 | B <- meta$batch 32 | AGE <- scale(meta$tbru_age) 33 | SEX <- meta$Sex 34 | nUMI <- scale(log(meta$nUMI)) 35 | MT <- meta$percent_mito 36 | PC <- pcs[as.character(meta$donor),1:5] 37 | expPC <- pca_res[,1:5] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5]) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::glmer(formula = E ~ G + (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 +PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 45 | family = "poisson", nAGQ=0, data= data, control = glmerControl(optimizer = "nloptwrap")) 46 | null_model <- lme4::glmer(formula = E ~ (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5, 47 | family = "poisson", nAGQ=0, data= data, control = glmerControl(optimizer = "nloptwrap")) 48 | model_lrt <- anova(null_model, full_model) 49 | 50 | out <- summary(test)$coefficients 51 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 52 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_univariate.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariate (e.g., batch-corrected CV from CCA) 37 | cov <- readRDS("/path/cca_donor_batch.rds")[,1] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | full_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 45 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 46 | null_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 47 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 48 | model_lrt <- anova(null_model, full_model) 49 | 50 | out <- summary(test)$coefficients 51 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 52 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_univariate_condition.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL conditional model with CV1 and CD4+ cell states 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariates (batch-corrected CV from CCA and CD4+ binary annotation from gating on CITE-seq surface protein) 37 | cov <- readRDS("/path/cca_donor_batch.rds")[,1] 38 | cd4 <- readRDS("/path/citeseq_gates.rds")[,"cd4"] 39 | 40 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 41 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 42 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov, cd4) 43 | data$G <- as.numeric(as.character(data$G)) 44 | 45 | full_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov + cd4 + G*cd4, 46 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 47 | null_model_condCD4 <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + cd4 + G*cd4, 48 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 49 | model_lrt_condCD4 <- anova(null_model_condCD4, full_model) 50 | null_model_condCV1 <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov + cd4, 51 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 52 | model_lrt_condCV1 <- anova(null_model_condCV1, full_model) 53 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_univariate_permute.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) on permuted data (permuting cell state) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Load interaction covariate (e.g., batch-corrected CV from CCA) 25 | set.seed(1010) 26 | cov <- unlist(readRDS("/path/cca_donor_batch.rds")[,1]) 27 | for(x in unique(meta$donor)){ 28 | cov[meta$donor == x] <- sample(cov[meta$donor == x]) 29 | } 30 | 31 | # Make data frame of variables for model 32 | E <- as.numeric(exprs_raw[gene,]) 33 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 34 | IND <- factor(meta$donor) 35 | B <- meta$batch 36 | AGE <- scale(meta$tbru_age) 37 | SEX <- meta$Sex 38 | nUMI <- scale(log(meta$nUMI)) 39 | MT <- meta$percent_mito 40 | PC <- pcs[as.character(meta$donor),1:5] 41 | expPC <- pca_res[,1:5] 42 | 43 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 44 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 45 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 46 | data$G <- as.numeric(as.character(data$G)) 47 | 48 | full_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 49 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 50 | null_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 51 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 52 | model_lrt <- anova(null_model, full_model) 53 | 54 | out <- summary(test)$coefficients 55 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 56 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 57 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_univariate_simulDE.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model with one cell state interaction (CD4+), simulating differential expression 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | frac <- as.numeric(args[3]) # fraction to which to decrease expression in CD4+ cells 12 | 13 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 14 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 15 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 16 | pca_res <- readRDS("/path/pca_irlba.rds") 17 | pca_res <- pca_res$x 18 | 19 | # Load genotype dosages (from dbGaP: phs002025) and PCs 20 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 21 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 22 | row.names(pcs) <- pcs$V2 23 | pcs <- pcs[,3:ncol(pcs)] 24 | 25 | # Make data frame of variables for model 26 | E <- as.numeric(exprs_raw[gene,]) 27 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 28 | IND <- factor(meta$donor) 29 | B <- meta$batch 30 | AGE <- scale(meta$tbru_age) 31 | SEX <- meta$Sex 32 | nUMI <- scale(log(meta$nUMI)) 33 | MT <- meta$percent_mito 34 | PC <- pcs[as.character(meta$donor),1:5] 35 | expPC <- pca_res[,1:5] 36 | 37 | # Load interaction covariate (e.g., batch-corrected CV from CCA) 38 | cov <- readRDS("/path/citeseq_gates.rds")[,"cd4"] 39 | 40 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 41 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 42 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 43 | data$G <- as.numeric(as.character(data$G)) 44 | data$E[data$cov == TRUE] <- round(2^(log2(data$E[data$cov == TRUE]+1)/frac)-1) 45 | 46 | full_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 47 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 48 | null_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 49 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 50 | model_lrt <- anova(null_model, full_model) 51 | 52 | out <- summary(test)$coefficients 53 | colnames(out) <- c("Estimate","Std.Error","zvalue","pval") 54 | out <- data.frame(gene=gene,snp=snp,term=row.names(x),x, lrt_pval=model_lrt$`Pr(>Chisq)`[2]) 55 | -------------------------------------------------------------------------------- /scripts/singlecell/poisson_univariate_simulnull.R: -------------------------------------------------------------------------------- 1 | # Script to run single-cell Poisson mixed effects eQTL model with one cell state interaction (e.g., CV, PC, CD4+, trajectory, disease status) and simulated null (based on Buzkova, et al. 2011) 2 | 3 | # Load libraries 4 | library(lme4) 5 | library(Matrix) 6 | 7 | args = commandArgs(trailingOnly=TRUE) 8 | 9 | gene <- args[1] 10 | snp <- args[2] 11 | 12 | # Load raw UMI counts matrix and metadata (from GEO: GSE158769) and gene expression PCs 13 | exprs_raw <- fread("/path/GSE158769_exprs_raw.tsv.gz") 14 | meta <- fread("/path/GSE158769_meta_data.txt.gz") 15 | pca_res <- readRDS("/path/pca_irlba.rds") 16 | pca_res <- pca_res$x 17 | 18 | # Load genotype dosages (from dbGaP: phs002025) and PCs 19 | geno <- fread("/path/geno_imputed_dosage_maf05_hg38_QC.vcf.gz") 20 | pcs <- read.delim("/path/geno_PCs.txt", sep = " ", header = F) 21 | row.names(pcs) <- pcs$V2 22 | pcs <- pcs[,3:ncol(pcs)] 23 | 24 | # Make data frame of variables for model 25 | E <- as.numeric(exprs_raw[gene,]) 26 | G <- t(subset(geno, ID == snp)[,as.character(meta$donor)])[,1] 27 | IND <- factor(meta$donor) 28 | B <- meta$batch 29 | AGE <- scale(meta$tbru_age) 30 | SEX <- meta$Sex 31 | nUMI <- scale(log(meta$nUMI)) 32 | MT <- meta$percent_mito 33 | PC <- pcs[as.character(meta$donor),1:5] 34 | expPC <- pca_res[,1:5] 35 | 36 | # Load interaction covariate (e.g., batch-corrected CV from CCA) 37 | cov <- readRDS("/path/cca_donor_batch.rds")[,1] 38 | 39 | data <- data.frame(E,G,IND,B,AGE,SEX,nUMI,MT, 40 | PC1 = PC[,1],PC2 = PC[,2],PC3 = PC[,3],PC4 = PC[,4],PC5 = PC[,5], 41 | expPC1 = expPC[,1], expPC2 = expPC[,2], expPC3 = expPC[,3], expPC4 = expPC[,4], expPC5 = expPC[,5], cov) 42 | data$G <- as.numeric(as.character(data$G)) 43 | 44 | null_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov, 45 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 46 | null_pred_y <- predict(null_model, data,type="response") 47 | 48 | set.seed(1010) 49 | null_betas <- sapply(1:1000, function(x) { 50 | y_vec <- sapply(1:500089, function(x){rpois(1, null_pred_y[x])}) 51 | data$y <- y_vec 52 | 53 | full_model <- lme4::glmer(formula = y ~ G + (1 | IND) + (1 | B) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 54 | family = "poisson", nAGQ=0, data= data, control = glmerControl(optimizer = "nloptwrap")) 55 | 56 | return(fixef(full_model)[18]) 57 | }) 58 | 59 | full_model <- lme4::glmer(formula = E ~ G + (1 | B) + (1 | IND) + AGE + SEX + nUMI + MT + PC1 + PC2 + PC3 + PC4 + PC5 + expPC1 + expPC2 + expPC3 + expPC4 + expPC5 + cov + G*cov, 60 | family = "poisson", nAGQ = 0, data= data, control = glmerControl(optimizer = "nloptwrap")) 61 | 62 | sum(abs(fixef(full_model)[18]) > abs(null_betas)) 63 | --------------------------------------------------------------------------------