{"id":28239,"date":"2024-02-06T10:17:25","date_gmt":"2024-02-06T02:17:25","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28239"},"modified":"2024-02-06T10:17:26","modified_gmt":"2024-02-06T02:17:26","slug":"%e5%8d%95%e7%bb%86%e8%83%9e%e8%a1%a8%e5%9e%8b%e9%a2%84%e6%b5%8b%e9%bb%91%e7%a7%91%e6%8a%80%ef%bc%813%e5%88%86%e9%92%9f%e5%b8%a6%e4%bd%a0%e8%a7%a3%e9%94%81scpp%e5%ae%8c%e5%85%a8%e4%bd%93%ef%bc%81","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=28239","title":{"rendered":"\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\u9ed1\u79d1\u6280\uff013\u5206\u949f\u5e26\u4f60\u89e3\u9501ScPP\u5b8c\u5168\u4f53\uff01\uff01"},"content":{"rendered":"

\u5355\u7ec6\u80de\u5206\u6790\u4e00\u76f4\u662f\u751f\u4fe1\u4ee5\u53ca\u533b\u5b66\u671f\u520a\u6bd4\u8f83\u6d41\u884c\u7684\u65b9\u5411\uff0c\u5c0f\u679c\u5728\u6d4f\u89c8\u671f\u520a\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u8fd9\u6837\u4e00\u4e2aR\u5305\u2014ScPP\uff08Single Cells\u2019Phenotype Prediction\uff09\uff0c\u8fd9\u4e2aR\u5305\u662f\u4e00\u79cd\u57fa\u4e8e\u8868\u578b\u76f8\u5173\u6807\u8bb0\u57fa\u56e0\u5728\u6574\u4f53\u548c\u5355\u7ec6\u80de\u4e2d\u7684\u8868\u8fbe\u8c31\uff0c\u6765\u8bc6\u522b\u5177\u6709\u7279\u5b9a\u8868\u578b\u7684\u7ec6\u80de\u4e9a\u7fa4\u7684\u5de5\u5177\uff0c\u662f\u4e00\u4e2a\u4e8e2023\u5e74\u65b0\u53d1\u5e03\u7684\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\u5de5\u5177\uff0c\u53ef\u4ee5\u975e\u5e38\u5feb\u901f\u7684\u8fdb\u884c\u5355\u7ec6\u80de\u7684\u76f8\u5173\u5206\u6790\u3002\u8bdd\u4e0d\u591a\u8bf4\uff0c\u5c0f\u679c\u6765\u5e26\u5927\u5bb6\u89e3\u9501ScPP\u8fd9\u4e2a\u5305\u7684\u7528\u6cd5\uff01\uff08\u672c\u6b21\u6240\u7528R\u5305\u5bf9\u670d\u52a1\u5668\u6027\u80fd\u8981\u6c42\u8f83\u9ad8\uff0c\u6b22\u8fce\u5c0f\u4f19\u4f34\u4eec\u6765\u79df\u8d41\u6211\u4eec\u7684\u670d\u52a1\u5668\u6765\u8fd0\u884c\u4ee3\u7801\u5466~\uff09<\/p>\n

\u5728\u8fdb\u884c\u6559\u7a0b\u4e4b\u524d\uff0c\u6211\u4eec\u9996\u5148\u9700\u8981\u4e86\u89e3\u4ec0\u4e48\u662f\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff0c\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff08Single Cells\u2019Phenotype Prediction\uff09\u662f\u4e00\u79cd\u5229\u7528\u5355\u7ec6\u80de\u6570\u636e\u6765\u63a8\u65ad\u7ec6\u80de\u7684\u529f\u80fd\u6216\u72b6\u6001\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5e2e\u52a9\u7814\u7a76\u4eba\u5458\u53d1\u73b0\u7ec6\u80de\u7684\u5f02\u8d28\u6027\u3001\u8f6c\u5316\u5173\u7cfb\uff0c\u5e76\u4e14\u6709\u52a9\u4e8e\u63ed\u793a\u7ec6\u80de\u8868\u578b\u4e0e\u57fa\u56e0\u578b\u3001\u86cb\u767d\u8d28\u4ee5\u53ca\u73af\u5883\u56e0\u7d20\u4e4b\u95f4\u7684\u5173\u8054\uff0c\u8fd8\u53ef\u4ee5\u5e94\u7528\u4e8e\u836f\u7269\u54cd\u5e94\u3001\u75be\u75c5\u8bca\u65ad\u3001\u7ec4\u7ec7\u53d1\u80b2\u548c\u5e72\u7ec6\u80de\u5206\u5316\u7b49\u3002\u53ef\u4ee5\u8bf4\u662f\u5355\u7ec6\u80de\u5206\u6790\u4e2d\u975e\u5e38\u5177\u6709\u5f00\u53d1\u6f5c\u80fd\u7684\u4e00\u4e2a\u65b9\u5411\u5462\u3002<\/p>\n

\u6211\u4eec\u5728\u8fdb\u884c\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\u65f6\u901a\u5e38\u9700\u8981\u4ee5\u4e0b\u51e0\u4e2a\u6b65\u9aa4\uff1a<\/p>\n

1.\u4ece\u5355\u7ec6\u80de\u6570\u636e\u4e2d\u63d0\u53d6\u7279\u5f81\uff0c\u6bd4\u5982\u8bf4\u57fa\u56e0\u8868\u8fbe\u3001\u8868\u89c2\u9057\u4f20\u4fee\u9970\u3001\u86cb\u767d\u8d28\u6c34\u5e73\u6216\u7ec6\u80de\u5f62\u6001\u7b49\u7279\u5f81\u3002<\/p>\n

2.\u9009\u62e9\u4e0e\u8868\u578b\u76f8\u5173\u7684\u7279\u5f81\uff0c\u53ef\u4ee5\u901a\u8fc7\u5dee\u5f02\u5206\u6790\u3001\u7279\u5f81\u9009\u62e9\u6216\u6df1\u5ea6\u5b66\u4e60\u7b49\u65b9\u6cd5\u8fdb\u884c\u7b5b\u9009\u3002<\/p>\n

3.\u5efa\u7acb\u9884\u6d4b\u6a21\u578b\uff0c\u8fd9\u4e00\u6b65\u53ef\u4ee5\u901a\u8fc7\u76d1\u7763\u5b66\u4e60\u3001\u65e0\u76d1\u7763\u5b66\u4e60\u6216\u534a\u76d1\u7763\u5b66\u4e60\u7b49\u65b9\u6cd5\u5b8c\u6210\u3002<\/p>\n

4.\u9a8c\u8bc1\u548c\u8bc4\u4f30\u9884\u6d4b\u7ed3\u679c\uff0c\u901a\u8fc7\u4ea4\u53c9\u9a8c\u8bc1\u3001ROC\u66f2\u7ebf\u3001\u6df7\u6dc6\u77e9\u9635\u6216\u751f\u7269\u5b66\u5b9e\u9a8c\u7b49\u65b9\u6cd5\u8fdb\u884c\u9a8c\u8bc1\u3002<\/p>\n

\u4e86\u89e3\u4e86\u4ec0\u4e48\u662f\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff0c\u6211\u4eec\u6765\u770b\u4e00\u4e0b\u4eca\u5929\u7684\u4e3b\u89d2\u2014\u2014ScPP\u3002\u4e0b\u9762\u7684\u56fe\u7247\u662f\u901a\u8fc7ScPP\u8fdb\u884c\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\u7684\u6d41\u7a0b\u56fe\uff1a<\/p>\n

<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u53ef\u4ee5\u901a\u8fc7Bulk RNA-seq\u83b7\u53d6\u4e00\u7ec4\u7ec6\u80de\u7684\u8f6c\u5f55\u7ec4\u6d4b\u5e8f\u6570\u636e\uff0c\u7136\u540e\u8fd0\u7528Binary\u8fdb\u884c\u5dee\u5f02\u8868\u8fbe\u5206\u6790\uff0c\u5f53\u7136\u4e5f\u53ef\u4ee5\u4f7f\u7528\u751f\u5b58\u5206\u6790\u6216\u8005\u8fde\u7eed\u53d8\u91cf\u76f8\u5173\u6027\u5206\u6790\uff0c\u63d0\u53d6\u51faPhenotype+ \u6807\u8bb0\u57fa\u56e0\u4ee5\u53caPhenotype-\u6807\u8bb0\u57fa\u56e0\uff0c\u8fdb\u800c\u5c06\u5176\u7528\u4e8e\u9274\u5b9a\u548c\u5206\u7c7b\u7ec6\u80de\u3002<\/p>\n

\u9664\u4e86Bulk RNA-seq\uff0c\u4e5f\u53ef\u4ee5\u76f4\u63a5\u8fdb\u884cSingle-cell RNA-seq\uff0c\u627e\u5230Phenotype\u6807\u8bb0\u57fa\u56e0\uff0c\u7136\u540e\u8fdb\u884cAUCell\u5206\u6790\uff0c\u8bc6\u522b\u5177\u6709\u6807\u8bb0\u57fa\u56e0\u7684\u7ec6\u80de\u7fa4\uff0c\u5e76\u4e14\u5c06Phenotype-\u4ee5\u53caPhenotype+\u533a\u5206\u7684\u7ed3\u679c\u8fdb\u884c\u5408\u5e76\uff0c\u5c31\u53ef\u4ee5\u5f97\u5230\u5728\u6574\u4e2a\u7ec6\u80de\u4e2d\uff0c\u8868\u578b\u57fa\u56e0\u8868\u8fbe\u9ad8\u548c\u8868\u8fbe\u4f4e\u7684\u5206\u5e03\u56fe\uff0c\u4e5f\u5c31\u5b8c\u6210\u4e86\u8868\u578b\u9884\u6d4b\u3002<\/p>\n

\u63a5\u4e0b\u6765\u662f\u4ee3\u7801\u5b9e\u64cd\uff1a<\/p>\n

Step 1 \u8fdb\u884cScPP\u5305\u7684\u521d\u59cb\u5316<\/p>\n

#\u5bfc\u5165ScPP\u4e2d\u7684\u529f\u80fd\u51fd\u6570<\/p>\n

##\u4e3a\u5355\u7ec6\u80de\u8868\u8fbe\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u8f93\u5165\u6570\u636e\u662f\u4e00\u4e2a\u8ba1\u6570\u77e9\u9635\uff0c\u5176\u4e2d\u7ec6\u80de\u662f\u5217\u540d\uff0c\u57fa\u56e0\u662f\u884c\u540d<\/p>\n

sc_Preprocess <- function(counts, project = “sc_preprocess”,<\/p>\n

normalization.method = “LogNormalize”, scale.factor = 10000,<\/p>\n

selection.method = “vst”, resolution = 0.1,<\/p>\n

dims_Neighbors = 1:20, dims_TSNE = 1:20, dims_UMAP = 1:20){<\/p>\n

library(Seurat)<\/p>\n

sc_dat <- CreateSeuratObject(counts = counts, project = project)<\/p>\n

sc_dat <- NormalizeData(object = sc_dat, normalization.method = normalization.method, scale.factor = scale.factor)<\/p>\n

sc_dat <- FindVariableFeatures(object = sc_dat, selection.method = selection.method)<\/p>\n

sc_dat <- ScaleData(object = sc_dat, features = rownames(sc_dat))<\/p>\n

sc_dat <- RunPCA(object = sc_dat, features = VariableFeatures(sc_dat))<\/p>\n

sc_dat <- FindNeighbors(object = sc_dat, dims = dims_Neighbors)<\/p>\n

sc_dat <- FindClusters(object = sc_dat, resolution = resolution)<\/p>\n

sc_dat <- RunTSNE(object = sc_dat, dims = dims_TSNE)<\/p>\n

sc_dat <- RunUMAP(object = sc_dat, dims = dims_UMAP)<\/p>\n

return(sc_dat)<\/p>\n

}<\/p>\n

#\u8fd0\u7528Binary\u8fdb\u884c\u5dee\u5f02\u8868\u8fbe\u5206\u6790<\/p>\n

##\u751f\u6210\u4e8c\u5143\u53d8\u91cf\u4e0e\u8868\u578b+\u6216\u8868\u578b-\u7ec4\u76f8\u5173\u7684\u6807\u8bb0\u57fa\u56e0\u4ee5\u53ca\u7279\u5f81\uff0c\u4e00\u5171\u5305\u542b4\u4e2a\u53c2\u6570:bulk_data\u3001features\u3001ref_group\u548cLog2FC_cutoff<\/p>\n

marker_Binary <- function(bulk_data, features, ref_group, Log2FC_cutoff = 0.585){<\/p>\n

library(dplyr)<\/p>\n

if (missing(ref_group))<\/p>\n

stop(“‘ref_group’ is missing or incorrect.”)<\/p>\n

if (missing(bulk_data) || !class(bulk_data) %in% c(“matrix”, “data.frame”))<\/p>\n

stop(“‘bulk_data’ is missing or incorrect.”)<\/p>\n

if (missing(features) || !class(features) %in% c(“matrix”, “data.frame”))<\/p>\n

stop(“‘features’ is missing or incorrect.”)<\/p>\n

ref = features$Sample[features$Feature == ref_group]<\/p>\n

tes = features$Sample[features$Feature != ref_group]<\/p>\n

ref_pos = which(colnames(bulk_data) %in% ref)<\/p>\n

tes_pos = which(colnames(bulk_data) %in% tes)<\/p>\n

pvalues <- apply(bulk_data, 1, function(x) {<\/p>\n

t.test(as.numeric(x)[tes_pos], as.numeric(x)[ref_pos])$p.value<\/p>\n

})<\/p>\n

log2FCs <- rowMeans(bulk_data[, tes_pos]) – rowMeans(bulk_data[, ref_pos])<\/p>\n

res <- data.frame(pvalue = pvalues, log2FC = log2FCs)<\/p>\n

res <- res[order(res$pvalue), ]<\/p>\n

res$fdr <- p.adjust(res$pvalue, method = “fdr”)<\/p>\n

geneList <- list(<\/p>\n

gene_pos = res %>% filter(pvalue < 0.05, log2FC > Log2FC_cutoff ) %>% rownames(.),<\/p>\n

gene_neg = res %>% filter(pvalue < 0.05, log2FC < -Log2FC_cutoff ) %>% rownames(.)<\/p>\n

)<\/p>\n

if(length(geneList[[1]]) > 0 & length(geneList[[2]] > 0)){return(geneList)}<\/p>\n

else if (length(geneList[[1]]) == 0){warning(“There is no genes positively correlated with the given feature in this bulk dataset.”);geneList = list(gene_pos = geneList[[2]]);return(geneList)}<\/p>\n

else if (length(geneList[[2]]) == 0){warning(“There is no genes negatively correlated with the given feature in this bulk dataset.”);geneList = list(gene_neg = geneList[[1]]);return(geneList)}<\/p>\n

}<\/p>\n

#\u8fdb\u884c\u8fde\u7eed\u53d8\u91cf\u76f8\u5173\u6027\u5206\u6790<\/p>\n

##\u751f\u6210\u8fde\u7eed\u53d8\u91cf\u4ee5\u53ca\u548c\u8868\u578b\u76f8\u5173\u7684\u6807\u8bb0\u57fa\u56e0\u6216\u7279\u5f81\uff0c\u5305\u542b4\u4e2a\u53c2\u6570:bulk_data\u3001features\u3001method\u548cestimate_cutoff<\/p>\n

marker_Continuous <- function(bulk_data, features, method = “spearman”, estimate_cutoff = 0.2){<\/p>\n

library(dplyr)<\/p>\n

if (missing(bulk_data) || !class(bulk_data) %in% c(“matrix”, “data.frame”))<\/p>\n

stop(“‘bulk_data’ is missing or incorrect.”)<\/p>\n

if (missing(features) || !class(features) %in% c(“integer”, “numeric”))<\/p>\n

stop(“‘features’ is missing or incorrect.”)<\/p>\n

CorrelationTest = apply(bulk_data,1,function(x){<\/p>\n

pvalue = cor.test(as.numeric(x),log2(as.numeric(features)+1), method = method)$p.value<\/p>\n

estimate = cor.test(as.numeric(x),log2(as.numeric(features)+1), method = method)$estimate<\/p>\n

res = cbind(pvalue,estimate)<\/p>\n

return(res)<\/p>\n

})<\/p>\n

resc = t(CorrelationTest)<\/p>\n

colnames(resc) = c(“pvalue”,”estimate”)<\/p>\n

resc = as.data.frame(resc[order(as.numeric(resc[,1]),decreasing = FALSE),])<\/p>\n

resc$fdr <- p.adjust(resc$pvalue,method = “fdr”)<\/p>\n

geneList <- list(<\/p>\n

gene_pos = resc %>% filter(fdr < 0.05, estimate > estimate_cutoff) %>% rownames(.),<\/p>\n

gene_neg = resc %>% filter(fdr < 0.05, estimate < -estimate_cutoff) %>% rownames(.)<\/p>\n

)<\/p>\n

if(length(geneList[[1]]) > 0 & length(geneList[[2]] > 0)){return(geneList)}<\/p>\n

else if (length(geneList[[1]]) == 0){warning(“There is no genes positively correlated with the given feature in this bulk dataset.”);geneList = list(gene_pos = geneList[[2]]);return(geneList)}<\/p>\n

else if (length(geneList[[2]]) == 0){warning(“There is no genes negatively correlated with the given feature in this bulk dataset.”);geneList = list(gene_neg = geneList[[1]]);return(geneList)}<\/p>\n

}<\/p>\n

##\u751f\u5b58\u6570\u636e\u5206\u6790\uff0c\u751f\u6210\u4e0e\u60a3\u8005\u9884\u540e\u76f8\u5173\u7684\u6807\u8bb0\u57fa\u56e0\u6216\u7279\u5f81\uff0c\u5305\u542b2\u4e2a\u53c2\u6570:bulk_data\u548csurvival_data<\/p>\n

marker_Survival <- function(bulk_data,survival_data){<\/p>\n

library(survival)<\/p>\n

library(dplyr)<\/p>\n

if (missing(bulk_data) || !class(bulk_data) %in% c(“matrix”, “data.frame”))<\/p>\n

stop(“‘bulk_data’ is missing or incorrect.”)<\/p>\n

if (missing(survival_data) || !class(survival_data) %in% c(“matrix”, “data.frame”))<\/p>\n

stop(“‘survival_data’ is missing or incorrect.”)<\/p>\n

SurvivalData <- data.frame(cbind(survival_data,t(bulk_data)))<\/p>\n

colnames(SurvivalData) = make.names(colnames(SurvivalData))<\/p>\n

var <- make.names(rownames(bulk_data))<\/p>\n

Model_Formula <- sapply(var, function(x) as.formula(paste(“Surv(time, status) ~”, x)))<\/p>\n

Model_all <- lapply(Model_Formula, function(x) coxph(x, data = SurvivalData))<\/p>\n

res <- lapply(seq_along(Model_all), function(i) {<\/p>\n

coef_summary <- summary(Model_all[[i]])$coefficients<\/p>\n

data.frame(<\/p>\n

variable = var[i],<\/p>\n

pvalue = coef_summary[,5],<\/p>\n

coef = coef_summary[,2]<\/p>\n

)<\/p>\n

}) %>% bind_rows()<\/p>\n

res <- res[order(res$pvalue), ]<\/p>\n

res$fdr <- p.adjust(res$pvalue, method = “fdr”)<\/p>\n

geneList <- list(<\/p>\n

gene_pos = res %>% filter(fdr < 0.05, coef > 1) %>% pull(variable), #correalted with worse survival<\/p>\n

gene_neg = res %>% filter(fdr < 0.05, coef < 1) %>% pull(variable) #correlated with better survival<\/p>\n

)<\/p>\n

if(length(geneList[[1]]) > 0 & length(geneList[[2]] > 0)){return(geneList)}<\/p>\n

else if (length(geneList[[1]]) == 0){warning(“There is no genes negatively correlated with patients’ prognosis in this bulk dataset.”);geneList = list(gene_pos = geneList[[2]]);return(geneList)}<\/p>\n

else if (length(geneList[[2]]) == 0){warning(“There is no genes positively correlated with patients’ prognosis in this bulk dataset.”);geneList = list(gene_neg = geneList[[1]]);return(geneList)}<\/p>\n

}<\/p>\n

##\u8fdb\u884c\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff0c\u5305\u542b3\u4e2a\u53c2\u6570:sc_dataset, geneList\u548cprobs\u3002<\/p>\n

ScPP = function(sc_dataset, geneList, probs = 0.2){<\/p>\n

if(length(geneList) != 2){<\/p>\n

stop(“This gene list do not have enough information correlated with interested feature.”)<\/p>\n

}<\/p>\n

if (missing(sc_dataset) || class(sc_dataset) != “Seurat”)<\/p>\n

stop(“‘sc_dataset’ is missing or not a seurat object.”)<\/p>\n

library(AUCell)<\/p>\n

cellrankings = AUCell_buildRankings(sc_dataset@assays$RNA@data,plotStats = FALSE)<\/p>\n

cellAUC = AUCell_calcAUC(geneList,cellrankings)<\/p>\n

metadata = as.data.frame(sc_dataset@meta.data)<\/p>\n

metadata$AUCup <- as.numeric(getAUC(cellAUC)[“gene_pos”, ])<\/p>\n

metadata$AUCdown <- as.numeric(getAUC(cellAUC)[“gene_neg”, ])<\/p>\n

downcells1 = rownames(metadata)[which(metadata$AUCup <= quantile(metadata$AUCup,probs = probs))]<\/p>\n

upcells1 = rownames(metadata)[which(metadata$AUCup >= quantile(metadata$AUCup,probs = (1-probs)))]<\/p>\n

downcells2 = rownames(metadata)[which(metadata$AUCdown >= quantile(metadata$AUCdown,probs = (1-probs)))]<\/p>\n

upcells2 = rownames(metadata)[which(metadata$AUCdown <= quantile(metadata$AUCdown,probs = probs))]<\/p>\n

ScPP_neg = intersect(downcells1,downcells2)<\/p>\n

ScPP_pos = intersect(upcells1,upcells2)<\/p>\n

metadata$ScPP <- ifelse(rownames(metadata) %in% ScPP_pos, “Phenotype+”, “Background”)<\/p>\n

metadata$ScPP <- ifelse(rownames(metadata) %in% ScPP_neg, “Phenotype-“, metadata$ScPP)<\/p>\n

return(metadata)<\/p>\n

}<\/p>\n

#\u5bfc\u5165\u7ed8\u56fe\u6240\u9700\u7684\u5305<\/p>\n

library(ggplot2)<\/p>\n

#\u5b89\u88c5Scpp\u5305\u4ee5\u53ca\u76f8\u5173\u7684R\u5305<\/p>\n

if (!require(“BiocManager”, quietly = TRUE))<\/p>\n

install.packages(“BiocManager”)<\/p>\n

BiocManager::install(“AUCell”)<\/p>\n

if (!requireNamespace(“devtools”, quietly = TRUE))<\/p>\n

install.packages(“devtools”)<\/p>\n

devtools::install_github(“WangX-Lab\/ScPP”)<\/p>\n

Step 2 \u5e94\u7528\u4e8c\u5143\u53d8\u91cf\u8fdb\u884cScPP<\/p>\n

\u672c\u6b21\u5206\u6790\u6240\u9700\u7684\u6570\u636e\u5c0f\u679c\u5df2\u7ecf\u5e2e\u5927\u5bb6\u6574\u7406\u597d\u4e86\uff0c\u5927\u5bb6\u70b9\u51fb\u94fe\u63a5\u5373\u53ef\u4e0b\u8f7d\uff1a<\/p>\n

\u94fe\u63a5\uff1ahttps:\/\/pan.baidu.com\/s\/1Kuox0OuispGobwKDeQJ0Bw<\/p>\n

\u63d0\u53d6\u7801\uff1av4fz<\/p>\n

# \u51c6\u5907\u6570\u636e<\/p>\n

load(“\/data\/binary.RData”)<\/p>\n

# \u6570\u636e\u603b\u89c8<\/p>\n

bulk[1:6,1:6] # bulk data<\/p>\n

head(binary) # phenotype data of bulk<\/p>\n

sc_count[1:6,1:6] # scRNA-seq data<\/p>\n

# \u901a\u8fc7 ScPP \u9009\u62e9\u4fe1\u606f\u7ec6\u80de<\/p>\n

sc = sc_Preprocess(sc_count)<\/p>\n

geneList = marker_Binary(bulk, binary, ref_group = “Normal”)<\/p>\n

metadata = ScPP(sc, geneList)<\/p>\n

write.csv(metadata, “\/results\/binary_meta.csv”)<\/p>\n

head(metadata)<\/p>\n

sc$ScPP = metadata$ScPP<\/p>\n

Idents(sc) = “ScPP”<\/p>\n

#\u5bf9ScPP\u8bc6\u522b\u7684\u7ec6\u80de\u8fdb\u884c\u53ef\u89c6\u5316<\/p>\n

DimPlot(sc, group = “ScPP”, cols = c(“grey”,”blue”,”red”))<\/p>\n

ggsave(“Fig_binary.pdf”,path = “\/results\/”,width = 10, height = 10, units = “cm”)<\/p>\n

<\/p>\n

<\/p>\n

<\/a><\/a> Binary\u6570\u636e<\/p>\n

<\/a><\/a> sc_count\u6570\u636e<\/p>\n

<\/a><\/a> Bulk\u6570\u636e<\/p>\n

\u5bfc\u5165\u7684\u6570\u636e\u5982\u56fe\uff0c\u662f\u6211\u4eec\u8fdb\u884cBinary\u5206\u6790\u6240\u9700\u7684\u4e09\u4e2a\u6570\u636e\uff0c\u5206\u522b\u662f\uff1aBinary\u6570\u636e\uff08\u6837\u672c\u7684\u80bf\u7624\u4e0e\u6b63\u5e38\u7684\u4e8c\u5143\u6570\u636e\uff09\uff0cBulk\u6570\u636e\uff08\u8868\u578b\u6570\u636e\uff09\uff0csc_count\u6570\u636e\uff08scRNA\u7684\u6d4b\u5e8f\u6570\u636e\uff09\u3002<\/p>\n

<\/p>\n

\u63a5\u7740\u662f\u6211\u4eec\u5206\u6790\u7684\u7ed3\u679c\uff0c\u901a\u8fc7ScPP\u5229\u7528\u4e8c\u5143\u53d8\u91cf\u5206\u6790\u5f97\u5230\u4e86\u7ec6\u80de\u4e2d\u7684\u80cc\u666f\u57fa\u56e0\uff0c\u4ee5\u53ca\u5e26\u6709\u6807\u8bb0\u57fa\u56e0\u7684\u9ad8\u8868\u8fbe\u548c\u4f4e\u8868\u8fbe\u7fa4\uff0c\u56fe\u4e2d\u7684\u7ea2\u8272\u8868\u793a\u9ad8\u8868\u8fbe\u7fa4\uff0c\u8bf4\u660e\u8fd9\u90e8\u5206\u7684\u7ec6\u80de\u53ef\u80fd\u662f\u5177\u6709\u6211\u4eec\u6240\u9700\u8981\u7684\u8868\u578b\u7684\u7ec6\u80de\u3002<\/p>\n

Step 3 \u8fd0\u7528\u8fde\u7eed\u53d8\u91cf\u8fdb\u884cScPP<\/p>\n

# \u5bfc\u5165\u6570\u636e\uff08\u6240\u9700\u6570\u636e\u5c0f\u679c\u653e\u5728\u4e0a\u6587\u7684\u94fe\u63a5\u4e86\u54e6~\uff09<\/p>\n

# library(ScPP)<\/p>\n

load(“\/data\/continuous.RData”)<\/p>\n

# \u6570\u636e\u603b\u89c8<\/p>\n

bulk[1:6,1:6] # bulk data<\/p>\n

head(continuous) # phenotype data of bulk<\/p>\n

sc_count[1:6,1:6] # scRNA-seq data<\/p>\n

# \u901a\u8fc7 ScPP \u9009\u62e9\u4fe1\u606f\u7ec6\u80de<\/p>\n

sc = sc_Preprocess(sc_count)<\/p>\n

geneList = marker_Continuous(bulk, continuous$TMB_non_silent)<\/p>\n

metadata = ScPP(sc, geneList)<\/p>\n

write.csv(metadata, “\/results\/continuous_meta.csv”)<\/p>\n

sc$ScPP = metadata$ScPP<\/p>\n

Idents(sc) = “ScPP”<\/p>\n

#\u5bf9ScPP\u8bc6\u522b\u7684\u7ec6\u80de\u8fdb\u884c\u53ef\u89c6\u5316<\/p>\n

DimPlot(sc, group = “ScPP”, cols = c(“grey”,”blue”,”red”))<\/p>\n

ggsave(“Fig_continuous.pdf”,path = “\/results\/”,width = 10, height = 10, units = “cm”)<\/p>\n

<\/p>\n

\u7ed3\u679c\u7684\u53ef\u89c6\u5316\u5982\u56fe\uff0c\u53ef\u4ee5\u53d1\u73b0\u548c\u8fd0\u7528\u4e8c\u5143\u53d8\u91cf\u8fdb\u884c\u7684ScPP\u7684\u7ed3\u679c\u662f\u6bd4\u8f83\u76f8\u4f3c\u7684\uff0c\u8fd9\u4e5f\u53ef\u4ee5\u8bc1\u660eScPP\u8fd9\u4e2a\u5305\u7684\u5206\u6790\u6bd4\u8f83\u7a33\u5b9a\u3002ScPP\u7684\u5206\u6790\u6587\u4ef6\u50a8\u5b58\u5728csv\u6587\u4ef6\u4e2d\uff0c\u5c0f\u4f19\u4f34\u4eec\u4e5f\u53ef\u4ee5\u7528csv\u6587\u4ef6\u8fdb\u884c\u5176\u4ed6\u7684\u5206\u6790~<\/p>\n

<\/p>\n

Step 4 \u8fd0\u7528\u751f\u5b58\u6570\u636e\u8fdb\u884cScPP<\/p>\n

#\u5bfc\u5165\u6570\u636e<\/p>\n

load(“\/data\/survival.RData”)<\/p>\n

#\u901a\u8fc7 ScPP \u9009\u62e9\u4fe1\u606f\u7ec6\u80de<\/p>\n

sc = sc_Preprocess(sc_count)<\/p>\n

geneList = marker_Survival(bulk, survival)<\/p>\n

str(geneList)<\/p>\n

metadata = ScPP(sc, geneList)<\/p>\n

write.csv(metadata, “\/results\/survival_meta.csv”)<\/p>\n

sc$ScPP = metadata$ScPP<\/p>\n

Idents(sc) = “ScPP”<\/p>\n

#\u53ef\u89c6\u5316<\/p>\n

DimPlot(sc, group = “ScPP”, cols = c(“grey”,”blue”,”red”))<\/p>\n

ggsave(“Fig_survival.pdf”,path = “\/results\/”,width = 10, height = 10, units = “cm”)<\/p>\n

<\/p>\n

\u7ed3\u679c\u5982\u56fe\uff0c\u53ef\u4ee5\u53d1\u73b0\u6211\u4eec\u8fdb\u884cScPP\u7684\u7ed3\u679c\u4e0e\u524d\u4e24\u6b21\u7684\u5206\u6790\u7565\u6709\u4e0d\u540c\uff0c\u4f46\u662f\u6574\u4f53\u7684\u7ec6\u80de\u8868\u578b\u5206\u5e03\u8fd8\u662f\u5927\u81f4\u76f8\u540c\u7684\u3002<\/p>\n

\u4ee5\u4e0a\u5c31\u662f\u5c0f\u679c\u4eca\u5929\u5206\u4eab\u7684\u5168\u90e8\u5185\u5bb9\u5566\uff01\u4eca\u5929\u6211\u4eec\u4e3b\u8981\u4e86\u89e3\u4e86\u4ec0\u4e48\u662f\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff0c\u4ee5\u53ca\u8fd0\u7528\u4e09\u79cd\u6570\u636e\uff08\u4e8c\u8fdb\u5236\u6570\u636e\uff0c\u751f\u5b58\u6570\u636e\uff0c\u8fde\u7eed\u6027\u6570\u636e\uff09\u5728ScPP\u8fd9\u4e2aR\u5305\u7684\u5e2e\u52a9\u4e0b\u8fdb\u884c\u5355\u7ec6\u80de\u8868\u578b\u9884\u6d4b\uff0c\u5e76\u4e14\u5bf9\u7ed3\u679c\u8fdb\u884c\u4e86\u89e3\u8bfb\u3002\u5982\u679c\u5927\u5bb6\u5bf9\u4eca\u5929\u7684\u5206\u4eab\u6709\u4efb\u4f55\u7591\u95ee\uff0c\u6b22\u8fce\u5173\u6ce8\u516c\u4f17\u53f7\u5411\u5c0f\u679c\u63d0\u95ee\uff0c\u6211\u4eec\u6709\u4e13\u95e8\u7684\u751f\u4fe1\u5206\u6790\u670d\u52a1\u54e6\u3002\u540c\u65f6\uff0c\u5982\u679c\u5927\u5bb6\u6ca1\u6709\u505a\u51fa\u6765\u56fe\u4e5f\u4e0d\u8981\u7070\u5fc3\uff0c\u53ef\u4ee5\u8bd5\u4e00\u8bd5\u6211\u4eec\u7684\u4e91\u751f\u4fe1\u5c0f\u5de5\u5177\u54e6\uff0c\u53ea\u8981\u8f93\u5165\u5408\u9002\u7684\u6570\u636e\u4ee5\u53ca\u6307\u4ee4\u5c31\u53ef\u4ee5\u76f4\u63a5\u7ed8\u5236\u60f3\u8981\u7684\u56fe\u5462\uff0c\u94fe\u63a5\uff1ahttp:\/\/www.biocloudservice.com\/home.html\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u5355\u7ec6\u80de\u5206\u6790\u4e00\u76f4\u662f\u751f\u4fe1\u4ee5\u53ca\u533b\u5b66\u671f\u520a\u6bd4\u8f83\u6d41\u884c\u7684\u65b9\u5411\uff0c\u5c0f\u679c\u5728\u6d4f\u89c8\u671f\u520a\u7684\u65f6\u5019\u53d1\u73b0\u4e86\u8fd9\u6837\u4e00\u4e2aR\u5305\u2014ScPP\uff08Single […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":""},"categories":[1],"tags":[],"jetpack_featured_media_url":"","_links":{"self":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28239"}],"collection":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=28239"}],"version-history":[{"count":1,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28239\/revisions"}],"predecessor-version":[{"id":28250,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/28239\/revisions\/28250"}],"wp:attachment":[{"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=28239"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=28239"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.biocloudservice.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=28239"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}