前言
Immugent在前一篇推文：SingCellaR：一站式研究细胞分化轨迹的功能调节的利器中，基于一项造血干细胞的研究结果初步介绍了SingCellaR的功能框架。从本节推文开始后续会有5篇有关SingCellaR代码实操的教程，分别不同角度分析多种来源的数据，以解决和发现不同的科学问题。
为了方便大家复现本次展示的流程，Immugent使用的是10X官网的5k_pbmc_v3数据，大家可以直接到10x genomics官网下载相应的数据，即可以进行本节代码的复现。
分析流程
首先是将数据导入到SingCellaR中。
library(SingCellaR)
data_matrices_dir<-"../SingCellaR_example_datasets/10x_genomics_5K_PBMCs/filtered_feature_bc_matrix/"
PBMCs<-new("SingCellaR")
PBMCs@dir_path_10x_matrix<-data_matrices_dir
PBMCs@sample_uniq_id<-"PBMCs"
load_matrices_from_cellranger(PBMCs,cellranger.version = 3)
PBMCs
## An object of class SingCellaR with a matrix of : 33538 genes across 5025 samples.
一行代码实现QC结果的展示，顶！
plot_cells_annotation(PBMCs,type="histogram")
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还有其它QC结果展示的图，都很美观。
plot_cells_annotation(PBMCs,type="boxplot")
plot_UMIs_vs_Detected_genes(PBMCs)
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检测双细胞和过滤双细胞。
filter_cells_and_genes(PBMCs,
min_UMIs=1000,
max_UMIs=30000,
min_detected_genes=300,
max_detected_genes=5000,
max_percent_mito=15,
genes_with_expressing_cells = 10,isRemovedDoublets = FALSE)
DoubletDetection_with_scrublet(PBMCs)
table(PBMCs@meta.data$Scrublet_type)
##
## Doublets Singlet
## 96 4929
filter_cells_and_genes(PBMCs,
min_UMIs=1000,
max_UMIs=30000,
min_detected_genes=300,
max_detected_genes=5000,
max_percent_mito=15,
genes_with_expressing_cells = 10,isRemovedDoublets = TRUE)
以下就是和Seurat类似的单细胞常规处理流程。
normalize_UMIs(PBMCs,use.scaled.factor = T)
remove_unwanted_confounders(PBMCs,residualModelFormulaStr="~UMI_count+percent_mito")
get_variable_genes_by_fitting_GLM_model(PBMCs,mean_expr_cutoff = 0.05,disp_zscore_cutoff = 0.05)
remove_unwanted_genes_from_variable_gene_set(PBMCs,gmt.file = "../SingCellaR_example_datasets//Human_genesets/human.ribosomal-mitocondrial.genes.gmt",
removed_gene_sets=c("Ribosomal_gene","Mitocondrial_gene"))
plot_variable_genes(PBMCs)
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runPCA(PBMCs,use.components=30,use.regressout.data = T)
plot_PCA_Elbowplot(PBMCs)
runTSNE(PBMCs,dim_reduction_method = "pca",n.dims.use = 10)
plot_tsne_label_by_qc(PBMCs,point.size = 0.1)
runUMAP(PBMCs,dim_reduction_method = "pca",n.dims.use = 10,n.neighbors = 20,
uwot.metric = "euclidean")
plot_umap_label_by_a_feature_of_interest(PBMCs,feature = "UMI_count",point.size = 0.1,mark.feature = F)
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identifyClusters(PBMCs,n.dims.use = 10,n.neighbors = 100,knn.metric = "euclidean")
plot_tsne_label_by_clusters(PBMCs,show_method = "louvain",point.size = 2)
plot_umap_label_by_clusters(PBMCs,show_method = "louvain",point.size = 0.80)
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下面介绍SingCellaR的一项特有功能分析--Force-directed graph analysis，它结合了力定向图来可视化细胞轨迹。
runFA2_ForceDirectedGraph(PBMCs,n.dims.use = 10,
n.neighbors = 5,n.seed = 1,fa2_n_iter = 1000)
plot_forceDirectedGraph_label_by_clusters(PBMCs,show_method = "louvain",vertex.size = 1,
background.color = "black")
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findMarkerGenes(PBMCs,cluster.type = "louvain")
plot_heatmap_for_marker_genes(PBMCs,cluster.type = "louvain",n.TopGenes = 10,rowFont.size = 5)
cl3_marker_genes<-getMarkerGenesInfo(PBMCs,cluster.type = "louvain",cluster_id = "cl3")
cl3_marker_genes<-cl3_marker_genes[order(cl3_marker_genes$log2FC,decreasing = T),]
head(cl3_marker_genes)
plot_tsne_label_by_genes(PBMCs,gene_list = c("CCR7","CD163","GNLY","CD72"))
plot_forceDirectedGraph_label_by_genes(PBMCs,gene_list = c("CCR7","CD163","GNLY","CD72"))
plot_violin_for_marker_genes(PBMCs,gene_list = c("CCR7","CD163","GNLY","CD72"))
plot_bubble_for_genes_per_cluster(PBMCs,cluster.type = "louvain",IsApplyClustering = T,
IsClusterByRow = T,IsClusterByColumn = T,
gene_list=c("CCR7","CD163",
"GNLY","CD72",
"CD8A","CD8B","IL3RA"
),show.percent = T,buble.scale = 12,point.color1 = "gray",
point.color2 = "firebrick1")
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runKNN_Graph(PBMCs,n.dims.use = 10,n.neighbors = 10)
library(threejs)
plot_3D_knn_graph_label_by_clusters(PBMCs,show_method = "louvain",vertext.size = 0.25)
plot_3D_knn_graph_label_by_gene(PBMCs,show.gene = "GNLY",vertext.size = 0.30)
clustering_KMeansOnKNN_Graph(PBMCs, k=10, iter.max = 100）
plot_tsne_label_by_clusters(PBMCs,show_method = "kmeans",point.size = 1)
plot_umap_label_by_clusters(PBMCs,show_method = "kmeans",point.size = 1)
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最后，SingCellaR还内置了GSEA分析流程，并且将结果可视化。
pre_rankedGenes_for_GSEA<-identifyGSEAPrerankedGenes_for_all_clusters(PBMCs,
cluster.type = "louvain")
save(pre_rankedGenes_for_GSEA,file="../SingCellaR_example_datasets/10x_genomics_5K_PBMCs/PBMCs_preRankedGenes_for_GSEA.rdata")
#Run fGSEA
load("../SingCellaR_example_datasets/10x_genomics_5K_PBMCs/PBMCs_preRankedGenes_for_GSEA.rdata")
fgsea_Results<-Run_fGSEA_for_multiple_comparisons(GSEAPrerankedGenes_list = pre_rankedGenes_for_GSEA, eps = 0,
gmt.file = "../SingCellaR_example_datasets/Human_genesets/human.hematopoiesis.signature.genes.gmt")
plot_heatmap_for_fGSEA_all_clusters(fgsea_Results,isApplyCutoff = TRUE,
use_pvalues_for_clustering=T,
show_NES_score = T,fontsize_row = 10,
adjusted_pval = 0.1,
show_only_NES_positive_score = T,format.digits = 3,
clustering_method = "ward.D",
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",show_text_for_ns = F)
save(PBMCs,file="../SingCellaR_example_datasets/10x_genomics_5K_PBMCs/PBMCs_v0.1.SingCellaR.rdata")
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说在最后
从上面的分析流程大家也能感受到SingCellaR的功能还是十分齐全的，相对于Seurat包的分析结果，它在处理单细胞数据标准流程上有以下优点：
- QC过程简单粗暴，而且结果很容易理解；
- 可以通过一行代码鉴定双细胞并去除；
- 能进行Force-directed graph analysis，这种分析在有些数据中表现很好；
- 可以进行GSEA分析，内置的免疫细胞gmt文件很方便大家进行细胞注释。
好啦，本期推文到这就结束了，下期Immugent将会展示使用SingCellaR分析一个公共的疾病数据，并结合具体的科学问题进行解读。
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