前情回顧：讓你的單細(xì)胞數(shù)據(jù)動(dòng)起來！|iCellR(一)
在上文我們已經(jīng)基本完成了聚類分析和細(xì)胞在不同條件下的比例分析，下面繼續(xù)開始啦，讓你看看iCellR的強(qiáng)大！

1. 每個(gè)cluster的平均表達(dá)量

my.obj <- clust.avg.exp(my.obj)head(my.obj@clust.avg) # gene cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 cluster_6 #1 A1BG 0.072214723 0.092648973 0.08258609 0 0.027183115 0.072291636 #2 A1BG.AS1 0.014380756 0.003280237 0.01817982 0 0.000000000 0.011545546 #3 A1CF 0.000000000 0.000000000 0.00000000 0 0.000000000 0.000000000 #4 A2M 0.000000000 0.000000000 0.00000000 0 0.007004131 0.004672857 #5 A2M.AS1 0.003520828 0.039985296 0.00876364 0 0.056596203 0.018445562 #6 A2ML1 0.000000000 0.000000000 0.00000000 0 0.000000000 0.000000000 # cluster_7 cluster_8 cluster_9 #1 0.09058946 0.04466827 0.027927923 #2 0.00000000 0.01534541 0.005930566 #3 0.00000000 0.00000000 0.000000000 #4 0.00000000 0.00000000 0.003411938 #5 0.00000000 0.00000000 0.000000000 #6 0.00000000 0.00000000 0.000000000

保存object

save(my.obj, file = "my.obj.Robj")

2. 尋找marker基因

marker.genes <- findMarkers(my.obj,fold.change = 2,padjval = 0.1) # 篩選條件dim(marker.genes) # [1] 1070 17head(marker.genes) # row baseMean baseSD AvExpInCluster AvExpInOtherClusters #LRRN3 LRRN3 0.01428477 0.1282046 0.05537243 0.003437002 #LINC00176 LINC00176 0.06757573 0.2949763 0.21404151 0.028906516 #FHIT FHIT 0.10195359 0.3885343 0.31404936 0.045957058 #TSHZ2 TSHZ2 0.04831334 0.2628778 0.14300998 0.023311970 #CCR7 CCR7 0.28132627 0.6847417 0.81386444 0.140728033 #SCGB3A1 SCGB3A1 0.06319598 0.3554273 0.18130557 0.032013232 # foldChange log2FoldChange pval padj clusters #LRRN3 16.110677 4.009945 1.707232e-06 2.847662e-03 1 #LINC00176 7.404611 2.888424 4.189197e-16 7.117446e-13 1 #FHIT 6.833539 2.772633 1.576339e-19 2.681353e-16 1 #TSHZ2 6.134616 2.616973 8.613622e-10 1.455702e-06 1 #CCR7 5.783243 2.531879 1.994533e-42 3.400679e-39 1 #SCGB3A1 5.663457 2.501683 2.578484e-07 4.313805e-04 1 # gene cluster_1 cluster_2 cluster_3 cluster_4 cluster_5 #LRRN3 LRRN3 0.05537243 0.004102916 0.002190847 0 0.010902326 #LINC00176 LINC00176 0.21404151 0.016772401 0.005203161 0 0.009293024 #FHIT FHIT 0.31404936 0.008713243 0.022934924 0 0.035701186 #TSHZ2 TSHZ2 0.14300998 0.008996236 0.009444180 0 0.000000000 #CCR7 CCR7 0.81386444 0.075719109 0.034017494 0 0.021492756 #SCGB3A1 SCGB3A1 0.18130557 0.039644151 0.001183264 0 0.000000000 # cluster_6 cluster_7 cluster_8 cluster_9 #LRRN3 0.002087831 0.00000000 0.000000000 0.012113258 #LINC00176 0.086762509 0.01198777 0.003501552 0.003560614 #FHIT 0.104189143 0.04144293 0.041064681 0.007218861 #TSHZ2 0.065509372 0.01690584 0.002352707 0.015350123 #CCR7 0.272580821 0.06523324 0.257130255 0.031304151 #SCGB3A1 0.078878071 0.01198777 0.000000000 0.043410608# baseMean: average expression in all the cells 所有細(xì)胞的平均表達(dá)值 # baseSD: Standard Deviation 標(biāo)準(zhǔn)偏差 # AvExpInCluster: average expression in cluster number (see clusters) 該cluster中的平均表達(dá)值 # AvExpInOtherClusters: average expression in all the other clusters 在所有其他cluster的平均表達(dá)值 # foldChange: AvExpInCluster/AvExpInOtherClusters 表達(dá)差異倍數(shù) # log2FoldChange: log2(AvExpInCluster/AvExpInOtherClusters) 取表達(dá)差異倍數(shù)的log2的值 # pval: P value # padj: Adjusted P value # clusters: marker for cluster number # gene: marker gene for the cluster cluster的marker gene # the rest are the average expression for each cluster

• 單細(xì)胞分群后，怎么找到Marker基因定義每一類群？

3. 繪制基因表達(dá)情況

基因MS4A1：tSNE 2D,PCA 2D展示該基因在兩種降維方式下，每個(gè)組的平均表達(dá)分布圖。Box Plot和Bar plot展示了該基因在每個(gè)簇中的表達(dá)箱線圖和條形圖

# tSNE 2D A <- gene.plot(my.obj, gene = "MS4A1",plot.type = "scatterplot",interactive = F,out.name = "scatter_plot") # PCA 2D B <- gene.plot(my.obj, gene = "MS4A1",plot.type = "scatterplot",interactive = F,out.name = "scatter_plot",plot.data.type = "pca")# Box Plot C <- gene.plot(my.obj, gene = "MS4A1",box.to.test = 0,box.pval = "sig.signs",col.by = "clusters",plot.type = "boxplot",interactive = F,out.name = "box_plot")# Bar plot (to visualize fold changes) D <- gene.plot(my.obj, gene = "MS4A1",col.by = "clusters",plot.type = "barplot",interactive = F,out.name = "bar_plot")library(gridExtra) grid.arrange(A,B,C,D) # 4個(gè)圖合并展示

• R語言 - 箱線圖（小提琴圖、抖動(dòng)圖、區(qū)域散點(diǎn)圖）

• R語言 - 柱狀圖

展示多個(gè)基因的plots:

genelist = c("PPBP","LYZ","MS4A1","GNLY","LTB","NKG7","IFITM2","CD14","S100A9") ### for(i in genelist){MyPlot <- gene.plot(my.obj, gene = i,interactive = F,plot.data.type = "umap",cell.transparency = 1)NameCol=paste("PL",i,sep="_")eval(call("<-", as.name(NameCol), MyPlot)) } ### library(cowplot) filenames <- ls(pattern="PL_") plot_grid(plotlist=mget(filenames[1:9]))

熱圖分析:

# 獲取top10高變基因 MyGenes <- top.markers(marker.genes, topde = 10, min.base.mean = 0.2,filt.ambig = F) MyGenes <- unique(MyGenes) # plot heatmap.gg.plot(my.obj, gene = MyGenes, interactive = T, out.name = "plot", cluster.by = "clusters") # or heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "clusters")

• R語言 - 熱圖美化

4. 使用ImmGen進(jìn)行細(xì)胞類型預(yù)測(cè)

注意：ImmGen是小鼠基因組數(shù)據(jù)，此處的樣本數(shù)據(jù)是人的。對(duì)于157個(gè)ULI-RNA-Seq(Ultra-low-input RNA-seq)樣本,使用的metadata是：

https://github.com/rezakj/scSeqR/blob/dev/doc/uli_RNA_metadat.txt

# 繪制cluster8中top40基因(平均表達(dá)值最小為0.2)在不同細(xì)胞的表達(dá)分布圖 Cluster = 8 MyGenes <- top.markers(marker.genes, topde = 40, min.base.mean = 0.2, cluster = Cluster) # 畫圖 cell.type.pred(immgen.data = "rna", gene = MyGenes, plot.type = "point.plot") # and cell.type.pred(immgen.data = "uli.rna", gene = MyGenes, plot.type = "point.plot", top.cell.types = 50) # or cell.type.pred(immgen.data = "rna", gene = MyGenes, plot.type = "heatmap") # and cell.type.pred(immgen.data = "uli.rna", gene = MyGenes, plot.type = "heatmap")# And finally check the genes in the cells and find the common ones to predict # heatmap.gg.plot(my.obj, gene = MyGenes, interactive = F, cluster.by = "clusters")# 可以看出來cluster 8更像B細(xì)胞！# for tissue type prediction use this: #cell.type.pred(immgen.data = "mca", gene = MyGenes, plot.type = "point.plot")

• cellassign：用于腫瘤微環(huán)境分析的單細(xì)胞注釋工具（9月Nature）

5. Pathway analysis

# cluster7的KEGG Pathway # pathways.kegg(my.obj, clust.num = 7) # this function is being improved and soon will be available 這個(gè)函數(shù)還要改進(jìn)，很快就能用了

• Pathview包：整合表達(dá)譜數(shù)據(jù)可視化KEGG通路

• KEGG在線數(shù)據(jù)庫使用攻略

對(duì)cluster進(jìn)行QC：

clust.stats.plot(my.obj, plot.type = "box.mito", interactive = F) # 每個(gè)cluster中細(xì)胞的線粒體基因分布圖 clust.stats.plot(my.obj, plot.type = "box.gene", interactive = F) # 每個(gè)cluster中細(xì)胞的基因分布圖

6. 差異表達(dá)分析

diff.res <- run.diff.exp(my.obj, de.by = "clusters", cond.1 = c(1,4), cond.2 = c(2)) # cluster之間的比較得到差異表達(dá)基因 diff.res1 <- as.data.frame(diff.res) diff.res1 <- subset(diff.res1, padj < 0.05) # 篩選padj < 0.05的基因 head(diff.res1) # baseMean 1_4 2 foldChange log2FoldChange pval #AAK1 0.19554589 0.26338228 0.041792762 0.15867719 -2.655833 8.497012e-33 #ABHD14A 0.09645732 0.12708519 0.027038379 0.21275791 -2.232715 1.151865e-11 #ABHD14B 0.19132829 0.23177944 0.099644572 0.42991118 -1.217889 3.163623e-09 #ABLIM1 0.06901900 0.08749258 0.027148089 0.31029018 -1.688310 1.076382e-06 #AC013264.2 0.07383608 0.10584821 0.001279649 0.01208947 -6.370105 1.291674e-19 #AC092580.4 0.03730859 0.05112053 0.006003441 0.11743700 -3.090041 5.048838e-07padj #AAK1 1.294690e-28 #ABHD14A 1.708446e-07 #ABHD14B 4.636290e-05 #ABLIM1 1.540087e-02 #AC013264.2 1.950557e-15 #AC092580.4 7.254675e-03# more examples 注意參數(shù)“de.by”設(shè)置的是不同差異比較方式 diff.res <- run.diff.exp(my.obj, de.by = "conditions", cond.1 = c("WT"), cond.2 = c("KO")) # WT vs KO diff.res <- run.diff.exp(my.obj, de.by = "clusters", cond.1 = c(1,4), cond.2 = c(2)) # cluster 1 and 2 vs. 4 diff.res <- run.diff.exp(my.obj, de.by = "clustBase.condComp", cond.1 = c("WT"), cond.2 = c("KO"), base.cond = 1) # cluster 1 WT vs cluster 1 KO diff.res <- run.diff.exp(my.obj, de.by = "condBase.clustComp", cond.1 = c(1), cond.2 = c(2), base.cond = "WT") # cluster 1 vs cluster 2 but only the WT sample

畫差異表達(dá)基因的火山圖和MA圖：

# Volcano Plot volcano.ma.plot(diff.res,sig.value = "pval",sig.line = 0.05,plot.type = "volcano",interactive = F)# MA Plot volcano.ma.plot(diff.res,sig.value = "pval",sig.line = 0.05,plot.type = "ma",interactive = F)

• 什么？你做的差異基因方法不合適？

7. 合并，重置，重命名和刪除cluster

# 如果你想要合并cluster 2和cluster 3 my.obj <- change.clust(my.obj, change.clust = 3, to.clust = 2)# 重置為原來的分類 my.obj <- change.clust(my.obj, clust.reset = T)# 可以將cluster7編號(hào)重命名為細(xì)胞類型-"B Cell". my.obj <- change.clust(my.obj, change.clust = 7, to.clust = "B Cell")# 刪除cluster1 my.obj <- clust.rm(my.obj, clust.to.rm = 1)# 進(jìn)行tSNE對(duì)細(xì)胞重新定位 my.obj <- run.tsne(my.obj, clust.method = "gene.model", gene.list = "my_model_genes.txt")# Use this for plotting as you make the changes cluster.plot(my.obj,cell.size = 1,plot.type = "tsne",cell.color = "black",back.col = "white",col.by = "clusters",cell.transparency = 0.5,clust.dim = 2,interactive = F)

Cell gating：可以框出指定的信息

my.plot <- gene.plot(my.obj, gene = "GNLY",plot.type = "scatterplot",clust.dim = 2,interactive = F)cell.gating(my.obj, my.plot = my.plot)# or#my.plot <- cluster.plot(my.obj, # cell.size = 1, # cell.transparency = 0.5, # clust.dim = 2, # interactive = F)

下載細(xì)胞ID之后，用下面的命令重命名這些cluste****r

my.obj <- gate.to.clust(my.obj, my.gate = "cellGating.txt", to.clust = 10)

8. 偽時(shí)序分析

注意參數(shù)“type”

MyGenes <- top.markers(marker.genes, topde = 50, min.base.mean = 0.2) MyGenes <- unique(MyGenes)pseudotime.tree(my.obj,marker.genes = MyGenes,type = "unrooted",clust.method = "complete")# orpseudotime.tree(my.obj,marker.genes = MyGenes,type = "classic",clust.method = "complete")

9. 應(yīng)用monocle進(jìn)行偽時(shí)序分析

library(monocle)MyMTX <- my.obj@main.data GeneAnno <- as.data.frame(row.names(MyMTX)) colnames(GeneAnno) <- "gene_short_name" row.names(GeneAnno) <- GeneAnno$gene_short_name cell.cluster <- (my.obj@best.clust) Ha <- data.frame(do.call('rbind', strsplit(as.character(row.names(cell.cluster)),'_',fixed=TRUE)))[1] clusts <- paste("cl.",as.character(cell.cluster$clusters),sep="") cell.cluster <- cbind(cell.cluster,Ha,clusts) colnames(cell.cluster) <- c("Clusts","iCellR.Conds","iCellR.Clusts") Samp <- new("AnnotatedDataFrame", data = cell.cluster) Anno <- new("AnnotatedDataFrame", data = GeneAnno) my.monoc.obj <- newCellDataSet(as.matrix(MyMTX),phenoData = Samp, featureData = Anno)## find disperesedgenes my.monoc.obj <- estimateSizeFactors(my.monoc.obj) my.monoc.obj <- estimateDispersions(my.monoc.obj) disp_table <- dispersionTable(my.monoc.obj)unsup_clustering_genes <- subset(disp_table, mean_expression >= 0.1) my.monoc.obj <- setOrderingFilter(my.monoc.obj, unsup_clustering_genes$gene_id)# tSNE my.monoc.obj <- reduceDimension(my.monoc.obj, max_components = 2, num_dim = 10,reduction_method = 'tSNE', verbose = T) # cluster my.monoc.obj <- clusterCells(my.monoc.obj, num_clusters = 10)## plot conditions and clusters based on iCellR analysis A <- plot_cell_clusters(my.monoc.obj, 1, 2, color = "iCellR.Conds") B <- plot_cell_clusters(my.monoc.obj, 1, 2, color = "iCellR.Clusts")## plot clusters based monocle analysis C <- plot_cell_clusters(my.monoc.obj, 1, 2, color = "Cluster")# get marker genes from iCellR analysis MyGenes <- top.markers(marker.genes, topde = 30, min.base.mean = 0.2) my.monoc.obj <- setOrderingFilter(my.monoc.obj, MyGenes)my.monoc.obj <- reduceDimension(my.monoc.obj, max_components = 2,method = 'DDRTree') # order cells my.monoc.obj <- orderCells(my.monoc.obj)# plot based on iCellR analysis and marker genes from iCellR D <- plot_cell_trajectory(my.monoc.obj, color_by = "iCellR.Clusts")## heatmap genes from iCellRplot_pseudotime_heatmap(my.monoc.obj[MyGenes,],cores = 1,cluster_rows = F,use_gene_short_name = T,show_rownames = T)

• NBT|45種單細(xì)胞軌跡推斷方法比較，110個(gè)實(shí)際數(shù)據(jù)集和229個(gè)合成數(shù)據(jù)集

10. iCellR應(yīng)用于scVDJ-seq數(shù)據(jù)

# first prepare the files. # this function would filter the files, calculate clonotype frequencies and proportions and add conditions to the cell ids. my.vdj.1 <- prep.vdj(vdj.data = "all_contig_annotations.csv", cond.name = "WT") my.vdj.2 <- prep.vdj(vdj.data = "all_contig_annotations.csv", cond.name = "KO") my.vdj.3 <- prep.vdj(vdj.data = "all_contig_annotations.csv", cond.name = "Ctrl")# concatenate all the conditions my.vdj.data <- rbind(my.vdj.1, my.vdj.2, my.vdj.3)# see head of the file head(my.vdj.data) # raw_clonotype_id barcode is_cell contig_id #1 clonotype1 WT_AAACCTGAGCTAACTC-1 True AAACCTGAGCTAACTC-1_contig_1 #2 clonotype1 WT_AAACCTGAGCTAACTC-1 True AAACCTGAGCTAACTC-1_contig_2 #3 clonotype1 WT_AGTTGGTTCTCGCATC-1 True AGTTGGTTCTCGCATC-1_contig_3 #4 clonotype1 WT_TGACAACCAACTGCTA-1 True TGACAACCAACTGCTA-1_contig_1 #5 clonotype1 WT_TGTCCCAGTCAAACTC-1 True TGTCCCAGTCAAACTC-1_contig_1 #6 clonotype1 WT_TGTCCCAGTCAAACTC-1 True TGTCCCAGTCAAACTC-1_contig_2 # high_confidence length chain v_gene d_gene j_gene c_gene full_length #1 True 693 TRA TRAV8-1 None TRAJ21 TRAC True #2 True 744 TRB TRBV28 TRBD1 TRBJ2-1 TRBC2 True #3 True 647 TRA TRAV8-1 None TRAJ21 TRAC True #4 True 508 TRB TRBV28 TRBD1 TRBJ2-1 TRBC2 True #5 True 660 TRA TRAV8-1 None TRAJ21 TRAC True #6 True 770 TRB TRBV28 TRBD1 TRBJ2-1 TRBC2 True # productive cdr3 cdr3_nt #1 True CAVKDFNKFYF TGTGCCGTGAAAGACTTCAACAAATTTTACTTT #2 True CASSLFSGTGTNEQFF TGTGCCAGCAGTTTATTTTCCGGGACAGGGACGAATGAGCAGTTCTTC #3 True CAVKDFNKFYF TGTGCCGTGAAAGACTTCAACAAATTTTACTTT #4 True CASSLFSGTGTNEQFF TGTGCCAGCAGTTTATTTTCCGGGACAGGGACGAATGAGCAGTTCTTC #5 True CAVKDFNKFYF TGTGCCGTGAAAGACTTCAACAAATTTTACTTT #6 True CASSLFSGTGTNEQFF TGTGCCAGCAGTTTATTTTCCGGGACAGGGACGAATGAGCAGTTCTTC # reads umis raw_consensus_id my.raw_clonotype_id clonotype.Freq #1 1241 2 clonotype1_consensus_1 clonotype1 120 #2 2400 4 clonotype1_consensus_2 clonotype1 120 #3 1090 2 clonotype1_consensus_1 clonotype1 120 #4 2455 4 clonotype1_consensus_2 clonotype1 120 #5 1346 2 clonotype1_consensus_1 clonotype1 120 #6 3073 8 clonotype1_consensus_2 clonotype1 120 # proportion total.colonotype #1 0.04098361 1292 #2 0.04098361 1292 #3 0.04098361 1292 #4 0.04098361 1292 #5 0.04098361 1292 #6 0.04098361 1292# add it to iCellR object add.vdj(my.obj, vdj.data = my.vdj.data)

reference

如果想嘗試一下，這里有傳送門哦！

• https://github.com/rezakj/iCellR

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