.. _covid19_data_loading_label: 2. Covid19 data loading =========================== .. code:: r library(Seurat) .. parsed-literal:: Attaching SeuratObject read data ========= .. code:: r o<-function(w,h) options(repr.plot.width=w, repr.plot.height=h) .. code:: r C141 <- Read10X_h5("covid/GSM4339769_C141_filtered_feature_bc_matrix.h5") C142 <- Read10X_h5("covid/GSM4339770_C142_filtered_feature_bc_matrix.h5") C143 <- Read10X_h5("covid/GSM4339771_C143_filtered_feature_bc_matrix.h5") C144 <- Read10X_h5("covid/GSM4339772_C144_filtered_feature_bc_matrix.h5") C145 <- Read10X_h5("covid/GSM4339773_C145_filtered_feature_bc_matrix.h5") C146 <- Read10X_h5("covid/GSM4339774_C146_filtered_feature_bc_matrix.h5") C148 <- Read10X_h5("covid/GSM4475051_C148_filtered_feature_bc_matrix.h5") C149 <- Read10X_h5("covid/GSM4475052_C149_filtered_feature_bc_matrix.h5") C152 <- Read10X_h5("covid/GSM4475053_C152_filtered_feature_bc_matrix.h5") C51 <- Read10X_h5("covid/GSM4475048_C51_filtered_feature_bc_matrix.h5" ) C52 <- Read10X_h5("covid/GSM4475049_C52_filtered_feature_bc_matrix.h5" ) C100 <- Read10X_h5("covid/GSM4475050_C100_filtered_feature_bc_matrix.h5") .. parsed-literal:: Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” Warning message in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : “'giveCsparse' has been deprecated; setting 'repr = "T"' for you” .. code:: r GSM3660650<- Read10X(data.dir = 'covid/GSM3660650_SC249NORbal_fresh') .. code:: r C141<-CreateSeuratObject(counts = C141, project = "C141",min.cells = 3, min.features = 200) C142<-CreateSeuratObject(counts = C142, project = "C142",min.cells = 3, min.features = 200) C143<-CreateSeuratObject(counts = C143, project = "C143",min.cells = 3, min.features = 200) C144<-CreateSeuratObject(counts = C144, project = "C144",min.cells = 3, min.features = 200) C145<-CreateSeuratObject(counts = C145, project = "C145",min.cells = 3, min.features = 200) C146<-CreateSeuratObject(counts = C146, project = "C146",min.cells = 3, min.features = 200) C148<-CreateSeuratObject(counts = C148, project = "C148",min.cells = 3, min.features = 200) C149<-CreateSeuratObject(counts = C149, project = "C149",min.cells = 3, min.features = 200) C152<-CreateSeuratObject(counts = C152, project = "C152",min.cells = 3, min.features = 200) C51<-CreateSeuratObject(counts = C51, project = "C51",min.cells = 3, min.features = 200) C52<-CreateSeuratObject(counts = C52, project = "C52",min.cells = 3, min.features = 200) C100<-CreateSeuratObject(counts = C100, project = "C100",min.cells = 3, min.features = 200) GSM3660650<-CreateSeuratObject(counts = GSM3660650, project = "GSM3660650",min.cells = 3, min.features = 200) .. code:: r C141$group<-"mild" C142$group<-"mild" C143$group<-"severe" C144$group<-"mild" C145$group<-"severe" C146$group<-"severe" C148$group<-"severe" C149$group<-"severe" C152$group<-"severe" C51$group <- "healthy" C52$group <- "healthy" C100$group <- "healthy" GSM3660650$group <- "healthy" .. code:: r # We first calculate mt-gene fractions and visualize them C141[["percent.mt"]]<-PercentageFeatureSet(C141,pattern = "^MT") p141<-VlnPlot(C141, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C142[["percent.mt"]]<-PercentageFeatureSet(C142,pattern = "^MT") p142<-VlnPlot(C142, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C143[["percent.mt"]]<-PercentageFeatureSet(C143,pattern = "^MT") p143<-VlnPlot(C143, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C144[["percent.mt"]]<-PercentageFeatureSet(C144,pattern = "^MT") p144<-VlnPlot(C144, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C145[["percent.mt"]]<-PercentageFeatureSet(C145,pattern = "^MT") p145<-VlnPlot(C145, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C146[["percent.mt"]]<-PercentageFeatureSet(C146,pattern = "^MT") p146<-VlnPlot(C146, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C148[["percent.mt"]]<-PercentageFeatureSet(C148,pattern = "^MT") p148<-VlnPlot(C148, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C149[["percent.mt"]]<-PercentageFeatureSet(C149,pattern = "^MT") p149<-VlnPlot(C149, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C152[["percent.mt"]]<-PercentageFeatureSet(C152,pattern = "^MT") p152<-VlnPlot(C152, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C51[["percent.mt"]]<-PercentageFeatureSet(C51,pattern = "^MT") p51<-VlnPlot(C51, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C52[["percent.mt"]]<-PercentageFeatureSet(C52,pattern = "^MT") p52<-VlnPlot(C52, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) C100[["percent.mt"]]<-PercentageFeatureSet(C100,pattern = "^MT") p100<-VlnPlot(C100, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) GSM3660650[["percent.mt"]]<-PercentageFeatureSet(GSM3660650,pattern = "^MT") pGSM3660650<-VlnPlot(GSM3660650, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3) .. code:: r C141 <- subset(C141, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C142 <- subset(C142, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C143 <- subset(C143, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C144 <- subset(C144, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C145 <- subset(C145, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C146 <- subset(C146, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C148 <- subset(C148, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C149 <- subset(C149, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C152 <- subset(C152, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C51 <- subset(C51, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C52 <- subset(C52, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) C100 <- subset(C100, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) GSM3660650 <- subset(GSM3660650, subset = nFeature_RNA > 200 & nFeature_RNA < 6000 & percent.mt < 10 & nCount_RNA >1000) .. code:: r C141 <- NormalizeData(C141,verbose = F) C142 <- NormalizeData(C142,verbose = F) C143 <- NormalizeData(C143,verbose = F) C144 <- NormalizeData(C144,verbose = F) C145 <- NormalizeData(C145,verbose = F) C146 <- NormalizeData(C146,verbose = F) C148 <- NormalizeData(C148,verbose = F) C149 <- NormalizeData(C149,verbose = F) C152 <- NormalizeData(C152,verbose = F) C51 <- NormalizeData(C51,verbose = F) C52 <- NormalizeData(C52,verbose = F) C100 <- NormalizeData(C100,verbose = F) GSM3660650<- NormalizeData(GSM3660650,verbose = F) .. code:: r C141 <- FindVariableFeatures(C141, selection.method = "vst", nfeatures = 2000) C142 <- FindVariableFeatures(C142, selection.method = "vst", nfeatures = 2000) C143 <- FindVariableFeatures(C143, selection.method = "vst", nfeatures = 2000) C144 <- FindVariableFeatures(C144, selection.method = "vst", nfeatures = 2000) C145 <- FindVariableFeatures(C145, selection.method = "vst", nfeatures = 2000) C146 <- FindVariableFeatures(C146, selection.method = "vst", nfeatures = 2000) C148 <- FindVariableFeatures(C148, selection.method = "vst", nfeatures = 2000) C149 <- FindVariableFeatures(C149, selection.method = "vst", nfeatures = 2000) C152 <- FindVariableFeatures(C152, selection.method = "vst", nfeatures = 2000) C51 <- FindVariableFeatures(C51, selection.method = "vst", nfeatures = 2000) C52 <- FindVariableFeatures(C52, selection.method = "vst", nfeatures = 2000) C100 <- FindVariableFeatures(C100, selection.method = "vst", nfeatures = 2000) GSM3660650 <- FindVariableFeatures(GSM3660650, selection.method = "vst", nfeatures = 2000) integrate data ============== .. code:: r nCoV.list <- list(C141= C141,C142= C142,C143= C143,C144= C144,C145= C145, C146= C146,C148= C148,C149= C149,C152= C152,C51= C51 , C52= C52 ,C100= C100,GSM3660650= GSM3660650) .. code:: r # select features that are repeatedly variable across datasets for integration features <- SelectIntegrationFeatures(object.list = nCoV.list) .. code:: r library(future) options(future.globals.maxSize = 1000 * 1024^2*5) plan("multiprocess", workers = 15) nCoV.anchors <- FindIntegrationAnchors(object.list = nCoV.list, anchor.features = features) nCoV.integrated <- IntegrateData(anchorset = nCoV.anchors) plan("sequential") .. parsed-literal:: Warning message in CheckDuplicateCellNames(object.list = object.list): “Some cell names are duplicated across objects provided. Renaming to enforce unique cell names.” Scaling features for provided objects Finding all pairwise anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8807 anchors Filtering anchors Retained 6635 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 12279 anchors Filtering anchors Retained 2232 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 11752 anchors Filtering anchors Retained 2246 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1584 anchors Filtering anchors Retained 1546 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1595 anchors Filtering anchors Retained 1561 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1658 anchors Filtering anchors Retained 1535 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 11271 anchors Filtering anchors Retained 2583 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 10846 anchors Filtering anchors Retained 2206 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 33359 anchors Filtering anchors Retained 9840 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1585 anchors Filtering anchors Retained 629 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4210 anchors Filtering anchors Retained 2074 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4115 anchors Filtering anchors Retained 2069 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4951 anchors Filtering anchors Retained 3518 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1367 anchors Filtering anchors Retained 890 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4870 anchors Filtering anchors Retained 2607 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5118 anchors Filtering anchors Retained 2922 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4912 anchors Filtering anchors Retained 2575 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6555 anchors Filtering anchors Retained 5164 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1472 anchors Filtering anchors Retained 1112 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6364 anchors Filtering anchors Retained 4490 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 3720 anchors Filtering anchors Retained 2889 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6409 anchors Filtering anchors Retained 3343 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6419 anchors Filtering anchors Retained 3564 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7636 anchors Filtering anchors Retained 5139 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1609 anchors Filtering anchors Retained 1001 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6964 anchors Filtering anchors Retained 4282 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 3599 anchors Filtering anchors Retained 2338 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4555 anchors Filtering anchors Retained 3072 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6442 anchors Filtering anchors Retained 2840 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6395 anchors Filtering anchors Retained 2749 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8831 anchors Filtering anchors Retained 4652 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1633 anchors Filtering anchors Retained 916 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8416 anchors Filtering anchors Retained 3887 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 3974 anchors Filtering anchors Retained 2263 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4680 anchors Filtering anchors Retained 2964 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5594 anchors Filtering anchors Retained 3043 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 10428 anchors Filtering anchors Retained 2724 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 10222 anchors Filtering anchors Retained 2722 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 26281 anchors Filtering anchors Retained 3159 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1682 anchors Filtering anchors Retained 455 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 25872 anchors Filtering anchors Retained 2827 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4951 anchors Filtering anchors Retained 805 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6346 anchors Filtering anchors Retained 1259 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7227 anchors Filtering anchors Retained 1167 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8765 anchors Filtering anchors Retained 1417 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 11269 anchors Filtering anchors Retained 2164 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 11197 anchors Filtering anchors Retained 2083 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 27092 anchors Filtering anchors Retained 3126 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1605 anchors Filtering anchors Retained 319 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 26615 anchors Filtering anchors Retained 2740 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4855 anchors Filtering anchors Retained 947 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6030 anchors Filtering anchors Retained 1143 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7753 anchors Filtering anchors Retained 1006 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8959 anchors Filtering anchors Retained 1304 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 22573 anchors Filtering anchors Retained 4484 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7042 anchors Filtering anchors Retained 3989 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7010 anchors Filtering anchors Retained 3650 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 10122 anchors Filtering anchors Retained 4106 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1565 anchors Filtering anchors Retained 846 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 9744 anchors Filtering anchors Retained 3135 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4356 anchors Filtering anchors Retained 1678 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5041 anchors Filtering anchors Retained 2105 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5484 anchors Filtering anchors Retained 2553 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6079 anchors Filtering anchors Retained 1856 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8863 anchors Filtering anchors Retained 3774 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 9113 anchors Filtering anchors Retained 3259 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5410 anchors Filtering anchors Retained 3152 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5438 anchors Filtering anchors Retained 3219 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8872 anchors Filtering anchors Retained 3356 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 1645 anchors Filtering anchors Retained 1035 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 7739 anchors Filtering anchors Retained 2961 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4115 anchors Filtering anchors Retained 1664 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4290 anchors Filtering anchors Retained 2500 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 4821 anchors Filtering anchors Retained 2740 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 5640 anchors Filtering anchors Retained 2751 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 8822 anchors Filtering anchors Retained 1952 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 9351 anchors Filtering anchors Retained 1664 anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 6050 anchors Filtering anchors Retained 2442 anchors :: Error in slot(object = anchorset, name = "reference.objects"): object 'nCoV' not found Traceback: 1. IntegrateData(anchorset = nCoV) 2. slot(object = anchorset, name = "reference.objects") .. code:: r plan("sequential") options(future.globals.maxSize = 1000 * 1024^2*5) plan("multiprocess", workers = 15) nCoV.integrated <- IntegrateData(anchorset = nCoV.anchors) plan("sequential") .. parsed-literal:: Merging dataset 4 into 2 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 7 into 3 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 6 into 3 7 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 8 into 5 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 1 into 2 4 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 9 into 3 7 6 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 12 into 10 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 13 into 2 4 1 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 5 8 into 3 7 6 9 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 2 4 1 13 into 10 12 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 11 into 10 12 2 4 1 13 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data Merging dataset 10 12 2 4 1 13 11 into 3 7 6 9 5 8 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights Integrating data .. code:: r samples = read.delim2("./covid_meta.txt",header = TRUE, stringsAsFactors = FALSE,check.names = FALSE, sep = "\t") # nCoV.integrated=sample.combined sample_info = as.data.frame(colnames(nCoV.integrated)) colnames(sample_info) = c('ID') rownames(sample_info) = sample_info$ID sample_info$sample = nCoV.integrated@meta.data$orig.ident sample_info = dplyr::left_join(sample_info,samples) rownames(sample_info) = sample_info$ID nCoV.integrated = AddMetaData(object = nCoV.integrated, metadata = sample_info) .. parsed-literal:: Joining, by = "sample" .. code:: r samples .. raw:: html
A data.frame: 13 × 10
samplesample_newsample_new_oldgroupdiseasenCoV_meannFeature_RNA_lownFeature_RNA_highnCount_RNA_thresholdpercent.mito
<chr><chr><chr><chr><chr><chr><int><int><int><int>
C51 HC1HC1HCN0 2006000100010
C52 HC2HC2HCN0 2006000100010
C100 HC3HC3HCN0 2006000100010
GSM3660650HC4HC4HCN0 2006000100010
C141 M1 O1 M Y0 2006000100010
C142 M2 O2 M Y0 2006000100010
C144 M3 O3 M Y0 2006000100010
C145 S1 S1 S Y0.0859011312006000100010
C143 S2 C1 S Y0.0070000482006000100010
C146 S3 C2 S Y0.7499391882006000100010
C148 S4 C3 S Y0.00255102 2006000100010
C149 S5 C4 S Y0.4088225082006000100010
C152 S6 C5 S Y0.0964821522006000100010
.. code:: r DefaultAssay(nCoV.integrated) <- "integrated" # Run the standard workflow for visualization and clustering nCoV.integrated <- ScaleData(nCoV.integrated, verbose = FALSE) nCoV.integrated <- RunPCA(nCoV.integrated, npcs = 30, verbose = FALSE) nCoV.integrated <- RunUMAP(nCoV.integrated, reduction = "pca", dims = 1:30) nCoV.integrated <- FindNeighbors(nCoV.integrated, reduction = "pca", dims = 1:30) nCoV.integrated <- FindClusters(nCoV.integrated, resolution = 0.5) .. parsed-literal:: Warning message: “The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session” 11:55:19 UMAP embedding parameters a = 0.9922 b = 1.112 11:55:19 Read 65706 rows and found 30 numeric columns 11:55:19 Using Annoy for neighbor search, n_neighbors = 30 11:55:19 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * | 11:55:27 Writing NN index file to temp file /tmp/RtmpFpm1Oa/file18efb2a79ef27 11:55:27 Searching Annoy index using 1 thread, search_k = 3000 11:55:58 Annoy recall = 100% 11:55:58 Commencing smooth kNN distance calibration using 1 thread 11:56:03 Initializing from normalized Laplacian + noise 11:56:08 Commencing optimization for 200 epochs, with 3048454 positive edges 11:57:50 Optimization finished Computing nearest neighbor graph Computing SNN .. parsed-literal:: Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 65706 Number of edges: 2333410 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.8994 Number of communities: 20 Elapsed time: 26 seconds .. code:: r nCoV.integrated <- FindClusters(nCoV.integrated, resolution = 1.2) .. parsed-literal:: Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 65706 Number of edges: 2333410 Running Louvain algorithm... Maximum modularity in 10 random starts: 0.8600 Number of communities: 37 Elapsed time: 19 seconds .. code:: r options(repr.plot.width=15, repr.plot.height=15) DimPlot(object = nCoV.integrated, reduction = 'umap',label = TRUE, group.by = 'integrated_snn_res.1.2', split.by = 'sample_new', ncol = 4) .. image:: output_20_0.png :width: 900px :height: 900px .. code:: r markers = c('PTPRC','CD3D','CD3E','ITGAM','ITGAX','TPPP3','KRT18','CD68','FCGR3B','CD1C','CLEC9A', 'LILRA4','TPSB2','KLRD1','MS4A1','IGHG4') .. code:: r library(ggpubr) DefaultAssay(nCoV.integrated)<-'RNA' pp = DotPlot(nCoV.integrated, features = rev(markers), cols = c('white','#F8766D'),dot.scale =5) + RotatedAxis() pp = pp + theme(axis.text.x = element_text(size = 12), axis.text.y = element_text(size = 12)) + labs(x='',y='') + guides(color = guide_colorbar(title = 'Scale expression'), size = guide_legend(title = 'Percent expressed')) + theme(axis.line = element_line(size = 0.6)) o(8,12) pp .. image:: output_22_0.png :width: 480px :height: 720px .. code:: r o(12,12) FeaturePlot(nCoV.integrated, features = 'PTPRC') .. image:: output_23_0.png :width: 720px :height: 720px .. code:: r save(nCoV.integrated, file = 'nCoV.integrated.rda', compress = T, compression_level = 9) .. code:: r nCoV.list = SplitObject(nCoV.integrated, split.by = 'sample_new') .. code:: r dissociation.genes.hs<-c("ACTG1","ANKRD1","ARID5A","ATF3","ATF4","BAG3","BHLHE40","CCNL1","CCRN4L", "CEBPB","CEBPD","CEBPG","CSRNP1","CXCL1","CYR61","DCN","DDX3XX","DDX5","DES","DNAJA1","DNAJB1", "DNAJB4","DUSP1","DUSP8","EGR1","EGR2","EIF1","EIF5","ERF","ERRFI1","FAM132B","FOS","FOSB","FOSL2", "GADD45A","GADD45G","BRD2","BTG1","BTG2","GCC1","GEM","H3F3B","HIPK3","HSP90AA1","HSP90AB1", "HSPA1A","HSPA1B","HSPA5","HSPA8","HSPB1","HSPE1","HSPH1","ID3","IDI1","IER2","IER3","IER5", "IFRD1","IL6","IRF1","IRF8","ITPKC","JUN","JUNB","JUND","KCNE4","KLF2","KLF4","KLF6","KLF9", "LITAF","LMNA","MAFF","MAFK","MCL1","MIDN","MIR22HG","MT1","MT2","MYADM","MYC","MYD88","NCKAP5L", "NCOA7","NFKBIA","NFKBIZ","NOP58","NPPC","NR4A1","ODC1","OSGIN1","OXNAD1","PCF11","PDE4B","PER1", "PHLDA1","PNP","PNRC1","PPP1CC","PPP1R15A","PXDC1","RAP1B","RASSF1","RHOB","RHOH","RIPK1","SAT1X", "SBNO2","SDC4","SERPINE1","SKIL","SLC10A6","SLC38A2","SLC41A1","SOCS3","SQSTM1","SRF","SRSF5", "SRSF7","STAT3","TAGLN2","TIPARP","TNFAIP3","TNFAIP6","TPM3","TPPP3","TRA2A","TRA2B","TRIB1", "TUBB4B","TUBB6","UBC","USP2","WAC","ZC3H12A","ZFAND5","ZFP36","ZFP36L1","ZFP36L2","ZYX") # normalize and identify variable features for each dataset independently nCoV.list <- lapply(X = nCoV.list, FUN = function(x) { DefaultAssay(x)<-'RNA' x <- NormalizeData(x, verbose = F) x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000, verbose = F) x <- ScaleData(x, features = rownames(x), verbose = F) x <- RunPCA(object = x, features = VariableFeatures(x), npcs = 50, verbose = F) x <- FindNeighbors(x, reduction = "pca", dims = 1:30, verbose = F) x <- RunUMAP(object=x,reduction = "pca", dims = 1:30, verbose = F) x <- FindClusters(object=x, resolution = c(0.7,0.9,1.2),verbose = F) (x %>% as.SingleCellExperiment %>% cxds_bcds_hybrid)@colData[,c('cxds_score','bcds_score','hybrid_score') ] %>% as.data.frame -> scds.doublet.profiles meta <- merge(x@meta.data, scds.doublet.profiles, by.x=0, by.y=0) rownames(meta) <- meta$Row.names meta$Row.names <- NULL x@meta.data <- meta gset <- dissociation.genes.hs gset <- gset[gset %in% rownames(x)] x[["percent.disso"]]<-PercentageFeatureSet(x, features = gset) x }) .. parsed-literal:: [12:39:39] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:40:41] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:43:14] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:43:28] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:45:22] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:45:50] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:46:20] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:46:54] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:47:35] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:49:23] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:50:57] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:51:44] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. [12:52:29] WARNING: amalgamation/../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior. .. code:: r library(tictoc) tic() save(nCoV.list, file = 'nCoV.list.rda', compress = T, compression_level = 9) toc() .. parsed-literal:: 2938.669 sec elapsed .. code:: r i=1 nCoV.list[[i]][[]] .. raw:: html
A data.frame: 3520 × 25
orig.identnCount_RNAnFeature_RNAgrouppercent.mtIDsamplesample_newsample_new_olddisease⋯integrated_snn_res.0.5seurat_clustersintegrated_snn_res.1.2RNA_snn_res.0.7RNA_snn_res.0.9RNA_snn_res.1.2cxds_scorebcds_scorehybrid_scorepercent.disso
<chr><dbl><int><chr><dbl><chr><chr><chr><chr><chr>⋯<fct><fct><fct><fct><fct><fct><dbl><dbl><dbl><dbl>
AAACCTGAGATGTCGG-1_1C141 37311594M1.7421603AAACCTGAGATGTCGG-1_1C141M1O1Y⋯7 2 100 3 2 7.385357e+030.00601114980.0970494042.197802
AAACCTGAGGCTCATT-1_1C141333395273M1.6377216AAACCTGAGGCTCATT-1_1C141M1O1Y⋯2 1 5 2 1 1 6.435988e+040.17289558050.9681248471.580731
AAACCTGCAATCCGAT-1_1C141 57271789M0.2793784AAACCTGCAATCCGAT-1_1C141M1O1Y⋯171 212 1 1 2.040284e-010.00123497730.0010124501.606426
AAACCTGCATGGTCAT-1_1C141 43962002M0.9326661AAACCTGCATGGTCAT-1_1C141M1O1Y⋯7 5 154 5 5 1.060499e+040.01061193650.1414388672.775250
AAACCTGGTTTAGCTG-1_1C141 32191451M2.9201615AAACCTGGTTTAGCTG-1_1C141M1O1Y⋯5 2 160 3 2 5.606312e+030.00128508140.0703352902.329916
AAACCTGTCAATCACG-1_1C141 40021691M2.3488256AAACCTGTCAATCACG-1_1C141M1O1Y⋯7 5 154 5 5 1.259333e+040.04467350240.2001058822.948526
AAACCTGTCCGAGCCA-1_1C141269885497M2.1379873AAACCTGTCCGAGCCA-1_1C141M1O1Y⋯6 6 8 2 1 6 7.821672e+030.00981744380.1062511232.141693
AAACCTGTCCTCCTAG-1_1C141 1118 559M1.2522361AAACCTGTCCTCCTAG-1_1C141M1O1Y⋯1710216 7 107.471562e+020.00106831490.0100754331.073345
AAACGGGAGAACTCGG-1_1C141 42231768M1.8233483AAACGGGAGAACTCGG-1_1C141M1O1Y⋯5 2 160 3 2 8.899078e+030.03288862850.1426599201.799668
AAACGGGAGTCAAGCG-1_1C141190453571M1.8955106AAACGGGAGTCAAGCG-1_1C141M1O1Y⋯2 0 3 1 0 0 2.099826e+030.00764944590.0333780111.575217
AAACGGGCACTCTGTC-1_1C141286044594M2.8527479AAACGGGCACTCTGTC-1_1C141M1O1Y⋯2 0 1 1 0 0 6.093326e+040.03380524740.7865454201.408894
AAACGGGCAGCGTAAG-1_1C141258324283M2.4698049AAACGGGCAGCGTAAG-1_1C141M1O1Y⋯1 0 5 1 0 0 1.060602e+040.00863873680.1394763151.238774
AAACGGGGTGGAAAGA-1_1C141 29691488M1.9198383AAACGGGGTGGAAAGA-1_1C141M1O1Y⋯5 3 160 4 3 6.573174e+030.00709189380.0880954292.222971
AAACGGGTCGTTACAG-1_1C141326954949M1.7831473AAACGGGTCGTTACAG-1_1C141M1O1Y⋯0 8 1 3 2 8 6.494869e+030.29112043980.3714578521.578223
AAACGGGTCTTCATGT-1_1C141117663014M1.6403196AAACGGGTCTTCATGT-1_1C141M1O1Y⋯0 8 1 3 2 8 1.831672e+030.00496574030.0273779751.946286
AAACGGGTCTTGCAAG-1_1C141143413900M0.8018967AAACGGGTCTTGCAAG-1_1C141M1O1Y⋯2 6 3 2 1 6 1.167642e+040.01895079200.1630258861.506171
AAAGATGAGTAAGTAC-1_1C141255154162M4.9970606AAAGATGAGTAAGTAC-1_1C141M1O1Y⋯1 0 5 1 0 0 5.938664e+040.00656859160.7401687881.262003
AAAGATGAGTGGTCCC-1_1C141 1050 706M3.9047619AAAGATGAGTGGTCCC-1_1C141M1O1Y⋯7 2 150 3 2 1.802625e+020.00082811820.0028300773.142857
AAAGATGCAATGGAGC-1_1C141306054902M1.1370691AAAGATGCAATGGAGC-1_1C141M1O1Y⋯1 0 5 1 0 0 1.266751e+040.03805180270.1943938021.421336
AAAGATGCACTCTGTC-1_1C141165943972M0.3977341AAAGATGCACTCTGTC-1_1C141M1O1Y⋯2 6 3 2 1 6 5.887888e+030.01874651760.0912945872.115222
AAAGATGGTCTAGTCA-1_1C141315095159M1.2631312AAAGATGGTCTAGTCA-1_1C141M1O1Y⋯6 6 8 2 1 6 7.689386e+030.02464782070.1194626301.837570
AAAGATGTCACTCTTA-1_1C141267484709M3.4058621AAAGATGTCACTCTTA-1_1C141M1O1Y⋯3 0 1 1 0 0 6.605723e+040.00299353900.8190160781.936593
AAAGATGTCAGGTTCA-1_1C141 42441775M2.5212064AAAGATGTCAGGTTCA-1_1C141M1O1Y⋯133 200 4 3 8.488594e+030.00477445640.1094436964.853911
AAAGATGTCGGATGGA-1_1C141 38961635M1.4887064AAAGATGTCGGATGGA-1_1C141M1O1Y⋯5 2 100 3 2 7.223007e+030.00809701530.0971313732.977413
AAAGATGTCTCCAACC-1_1C141285734622M1.3299269AAAGATGTCTCCAACC-1_1C141M1O1Y⋯0 0 1 1 2 0 5.844056e+030.02564611290.0976598942.190880
AAAGCAAAGTGAAGAG-1_1C141212825981M6.7333897AAAGCAAAGTGAAGAG-1_1C141M1O1Y⋯1016191114163.519066e+030.12395183740.1673408832.678320
AAAGCAACAGCGTCCA-1_1C141 24251324M0.9484536AAAGCAACAGCGTCCA-1_1C141M1O1Y⋯5 2 100 3 2 7.927301e+030.00701409620.1047500172.268041
AAAGCAACAGTGAGTG-1_1C141 74552468M3.5144199AAAGCAACAGTGAGTG-1_1C141M1O1Y⋯1219336 16191.302528e+040.01591991450.1766591651.703555
AAAGCAAGTCATATCG-1_1C141 86763094M2.9276164AAAGCAAGTCATATCG-1_1C141M1O1Y⋯3 7 0 5 6 7 2.711236e+030.00830961860.0415938542.109267
AAAGCAAGTCCAACTA-1_1C141117683144M1.5550646AAAGCAAGTCCAACTA-1_1C141M1O1Y⋯2 1 8 2 1 1 5.152539e+030.02955603230.0930291311.784500
⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮⋱⋮⋮⋮⋮⋮⋮⋮⋮⋮⋮
TTTATGCCAATTGCTG-1_1C141299464513M4.9255326TTTATGCCAATTGCTG-1_1C141M1O1Y⋯1 0 5 1 0 0 14050.37610.2144362780.388053011.075269
TTTATGCCACAACTGT-1_1C141217594880M1.2362700TTTATGCCACAACTGT-1_1C141M1O1Y⋯6 6 8 2 1 6 11375.00960.0640121400.204410671.971598
TTTATGCCATGCGCAC-1_1C141125073170M0.6236508TTTATGCCATGCGCAC-1_1C141M1O1Y⋯1 4 123 2 4 46454.87870.1774054170.751393751.918925
TTTATGCTCCTAGAAC-1_1C141187154258M3.9593909TTTATGCTCCTAGAAC-1_1C141M1O1Y⋯1613242 1113 715.99920.0090672360.017697851.768635
TTTCCTCAGAGTTGGC-1_1C141 46311927M2.3752969TTTCCTCAGAGTTGGC-1_1C141M1O1Y⋯5 3 160 4 3 11732.54690.0097580630.154516972.742388
TTTCCTCAGCGTTGCC-1_1C141210234499M3.0490415TTTCCTCAGCGTTGCC-1_1C141M1O1Y⋯160 241 0 0 11583.02940.0070072610.149915711.393712
TTTCCTCAGGAATCGC-1_1C141202054199M6.2261816TTTCCTCAGGAATCGC-1_1C141M1O1Y⋯0 7 4 5 6 7 53633.16260.4231037801.086052382.395447
TTTCCTCCACACTGCG-1_1C141324574950M2.4894476TTTCCTCCACACTGCG-1_1C141M1O1Y⋯0 4 1 3 2 4 5575.46270.0548716710.123597562.412423
TTTCCTCCAGCGAACA-1_1C141245404654M3.2192339TTTCCTCCAGCGAACA-1_1C141M1O1Y⋯3 1 0 2 1 1 56334.82080.0358791950.731800251.894866
TTTCCTCGTGAGTATA-1_1C141326635670M0.8725469TTTCCTCGTGAGTATA-1_1C141M1O1Y⋯6 1 182 1 1 2924.48830.1511216610.187192561.898172
TTTCCTCTCCTGTACC-1_1C141215554213M1.2479703TTTCCTCTCCTGTACC-1_1C141M1O1Y⋯1 7 115 6 7 47586.28560.0090955740.596885662.635119
TTTCCTCTCGCGCCAA-1_1C141 43381908M1.2448133TTTCCTCTCGCGCCAA-1_1C141M1O1Y⋯5 5 104 5 5 8836.61820.0074424420.116414932.558783
TTTGCGCAGGGCTTCC-1_1C141 84992905M2.3532180TTTGCGCAGGGCTTCC-1_1C141M1O1Y⋯5 2 160 3 2 17940.09040.1595167670.381139021.753147
TTTGCGCCAATTGCTG-1_1C141120992462M1.8431275TTTGCGCCAATTGCTG-1_1C141M1O1Y⋯3 9 1 7 8 9 31147.60820.0266146680.411296462.694438
TTTGCGCGTGCTCTTC-1_1C141382164877M1.1435001TTTGCGCGTGCTCTTC-1_1C141M1O1Y⋯1 4 123 2 4 8070.53930.1537659320.253427551.452271
TTTGCGCTCACCCTCA-1_1C141206023904M4.7082807TTTGCGCTCACCCTCA-1_1C141M1O1Y⋯8 1 142 1 1 7696.17920.0195992680.114492661.970682
TTTGCGCTCCGTAGGC-1_1C141232673648M1.8781966TTTGCGCTCCGTAGGC-1_1C141M1O1Y⋯129 237 8 9 30037.06250.0066860290.377624063.752095
TTTGCGCTCTACTTAC-1_1C141 51131880M3.2857422TTTGCGCTCTACTTAC-1_1C141M1O1Y⋯1219336 161910664.86920.0057233720.137285081.642871
TTTGCGCTCTTGCAAG-1_1C141234334050M1.2717108TTTGCGCTCTTGCAAG-1_1C141M1O1Y⋯6 0 121 0 0 6955.26170.0527893160.138562681.412538
TTTGCGCTCTTTACGT-1_1C141285844495M3.9707529TTTGCGCTCTTTACGT-1_1C141M1O1Y⋯0 0 1 1 2 0 64083.97040.0356558190.827330121.668766
TTTGGTTAGCACGCCT-1_1C141 37821669M1.5071391TTTGGTTAGCACGCCT-1_1C141M1O1Y⋯5 3 100 4 3 7415.44390.0103007820.101715362.564781
TTTGGTTAGTGGTAAT-1_1C141105603098M1.3731061TTTGGTTAGTGGTAAT-1_1C141M1O1Y⋯12173412151721858.41220.1787945330.448854561.903409
TTTGGTTAGTTGTAGA-1_1C141325164967M2.0943535TTTGGTTAGTTGTAGA-1_1C141M1O1Y⋯0 0 1 1 2 0 7371.83000.0028131930.093680901.556157
TTTGGTTCATACTACG-1_1C141217354314M2.6224983TTTGGTTCATACTACG-1_1C141M1O1Y⋯1 0 5 1 0 0 62425.06140.0461872480.817374181.352657
TTTGTCAAGATTACCC-1_1C141233754483M1.0695187TTTGTCAAGATTACCC-1_1C141M1O1Y⋯2 1 8 2 1 1 1660.23340.1187372730.139151851.112299
TTTGTCAAGTGGTAAT-1_1C141 41231831M2.5466893TTTGTCAAGTGGTAAT-1_1C141M1O1Y⋯5 3 160 4 3 1208.06580.0792466550.094032042.110114
TTTGTCACAGAAGCAC-1_1C141 89292971M2.5646769TTTGTCACAGAAGCAC-1_1C141M1O1Y⋯1219336 161921351.92770.0121405340.275765231.377534
TTTGTCATCAACCAAC-1_1C141 43351939M2.4913495TTTGTCATCAACCAAC-1_1C141M1O1Y⋯7 2 150 3 2 9693.45560.0057802850.125338642.491349
TTTGTCATCCAAACAC-1_1C141139183531M3.2332232TTTGTCATCCAAACAC-1_1C141M1O1Y⋯129 237 8 9 26399.65010.0467696560.372804021.501653
TTTGTCATCGCGTTTC-1_1C141255314537M2.2834985TTTGTCATCGCGTTTC-1_1C141M1O1Y⋯1 0 5 1 0 0 51298.68990.0064751670.640135331.335631
Quality control =============== .. code:: r i=1 o(20,20) ggarrange( DimPlot(nCoV.list[[i]], label=T,repel=T, group.by="RNA_snn_res.1.2")&NoLegend()& theme(axis.line = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks=element_blank() ), FeaturePlot(nCoV.list[[i]], features = 'hybrid_score')& theme(legend.position=c(0.8,0.8), axis.line = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks=element_blank() ), FeaturePlot(nCoV.list[[i]], features = 'percent.mt')& theme(legend.position=c(0.8,0.8), axis.line = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks=element_blank() ), FeaturePlot(nCoV.list[[i]], features = 'percent.disso')& theme(legend.position=c(0.8,0.8), axis.line = element_blank(), axis.title = element_blank(), axis.text = element_blank(), axis.ticks=element_blank() ), ncol=2,nrow=2 ) o(20,15) ggarrange( VlnPlot(nCoV.list[[i]], features = 'hybrid_score')&NoLegend(), VlnPlot(nCoV.list[[i]], features = 'nCount_RNA')&NoLegend(), VlnPlot(nCoV.list[[i]], features = 'nFeature_RNA')&NoLegend(), VlnPlot(nCoV.list[[i]], features = 'percent.mt')&NoLegend(), VlnPlot(nCoV.list[[i]], features = 'percent.disso')&NoLegend(), ncol=2, nrow=3 ) .. image:: output_30_0.png :width: 1200px :height: 1200px .. image:: output_30_1.png :width: 1200px :height: 900px