在咱们分析单细胞数据的时候，需求想象力的一点就是要理解数据结构。平时咱们都是如何看数据结构的呢？
library(Seurat) library(tidyverse) pbmc<-CreateSeuratObject(pbmc_small@assays$RNA@counts) pbmc%>% NormalizeData() %>% FindVariableFeatures() %>% ScaleData() %>% RunPCA() %>% FindNeighbors() %>% RunUMAP(1:10) %>% FindClusters(dims=1:0)-> pbmc pbmc An object of class Seurat 230 features across 80 samples within 1 assay Active assay: RNA (230 features) 2 dimensional reductions calculated: pca, umap
在R里边咱们用的是str(...),如：
str(pbmc) Formal class \'Seurat\' [package \"Seurat\"] with 13 slots ..@ assays :List of 1 .. ..$ RNA:Formal class \'Assay\' [package \"Seurat\"] with 8 slots .. .. .. ..@ counts :Formal class \'dgCMatrix\' [package \"Matrix\"] with 6 slots .. .. .. .. .. ..@ i : int [1:4456] 1 5 8 11 22 30 33 34 36 38 ... .. .. .. .. .. ..@ p : int [1:81] 0 47 99 149 205 258 306 342 387 423 ... .. .. .. .. .. ..@ Dim : int [1:2] 230 80 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:230] \"MS4A1\" \"CD79B\" \"CD79A\" \"HLA-DRA\" ... .. .. .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. .. ..@ x : num [1:4456] 1 1 3 1 1 4 1 5 1 1 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ data :Formal class \'dgCMatrix\' [package \"Matrix\"] with 6 slots .. .. .. .. .. ..@ i : int [1:4456] 1 5 8 11 22 30 33 34 36 38 ... .. .. .. .. .. ..@ p : int [1:81] 0 47 99 149 205 258 306 342 387 423 ... .. .. .. .. .. ..@ Dim : int [1:2] 230 80 .. .. .. .. .. ..@ Dimnames:List of 2 .. .. .. .. .. .. ..$ : chr [1:230] \"MS4A1\" \"CD79B\" \"CD79A\" \"HLA-DRA\" ... .. .. .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. .. ..@ x : num [1:4456] 4.97 4.97 6.06 4.97 4.97 ... .. .. .. .. .. ..@ factors : list() .. .. .. ..@ scale.data : num [1:230, 1:80] -0.409 1.64 -0.428 -1.375 -0.329 ... .. .. .. .. ..- attr(*, \"dimnames\")=List of 2 .. .. .. .. .. ..$ : chr [1:230] \"MS4A1\" \"CD79B\" \"CD79A\" \"HLA-DRA\" ... .. .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. ..@ key : chr \"rna_\" .. .. .. ..@ assay.orig : NULL .. .. .. ..@ var.features : chr [1:230] \"PPBP\" \"IGLL5\" \"VDAC3\" \"CD1C\" ... .. .. .. ..@ meta.features:\'data.frame\': 230 obs. of 5 variables: .. .. .. .. ..$ vst.mean : num [1:230] 0.388 0.6 0.7 13.425 0.3 ... .. .. .. .. ..$ vst.variance : num [1:230] 1.025 1.281 4.365 725.463 0.871 ... .. .. .. .. ..$ vst.variance.expected : num [1:230] 1.141 2.664 4.029 745.145 0.642 ... .. .. .. .. ..$ vst.variance.standardized: num [1:230] 0.898 0.481 1.083 0.974 1.356 ... .. .. .. .. ..$ vst.variable : logi [1:230] TRUE TRUE TRUE TRUE TRUE TRUE ... .. .. .. ..@ misc : NULL ..@ meta.data :\'data.frame\': 80 obs. of 5 variables: .. ..$ orig.ident : Factor w/ 1 level \"SeuratProject\": 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nCount_RNA : num [1:80] 70 85 87 127 173 70 64 72 52 100 ... .. ..$ nFeature_RNA : int [1:80] 47 52 50 56 53 48 36 45 36 41 ... .. ..$ RNA_snn_res.0.8: Factor w/ 3 levels \"0\",\"1\",\"2\": 2 2 2 2 2 2 2 2 2 2 ... .. ..$ seurat_clusters: Factor w/ 3 levels \"0\",\"1\",\"2\": 2 2 2 2 2 2 2 2 2 2 ... ..@ active.assay: chr \"RNA\" ..@ active.ident: Factor w/ 3 levels \"0\",\"1\",\"2\": 2 2 2 2 2 2 2 2 2 2 ... .. ..- attr(*, \"names\")= chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... ..@ graphs :List of 2 .. ..$ RNA_nn :Formal class \'Graph\' [package \"Seurat\"] with 7 slots .. .. .. ..@ assay.used: chr \"RNA\" .. .. .. ..@ i : int [1:1600] 0 1 2 3 4 5 6 7 8 9 ... .. .. .. ..@ p : int [1:81] 0 10 17 40 57 101 124 141 153 178 ... .. .. .. ..@ Dim : int [1:2] 80 80 .. .. .. ..@ Dimnames :List of 2 .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. ..@ x : num [1:1600] 1 1 1 1 1 1 1 1 1 1 ... .. .. .. ..@ factors : list() .. ..$ RNA_snn:Formal class \'Graph\' [package \"Seurat\"] with 7 slots .. .. .. ..@ assay.used: chr \"RNA\" .. .. .. ..@ i : int [1:4174] 0 1 2 3 4 5 6 7 8 9 ... .. .. .. ..@ p : int [1:81] 0 68 132 181 230 277 326 375 424 487 ... .. .. .. ..@ Dim : int [1:2] 80 80 .. .. .. ..@ Dimnames :List of 2 .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. ..@ x : num [1:4174] 1 0.6 0.6 0.6 0.538 ... .. .. .. ..@ factors : list() ..@ neighbors : list() ..@ reductions :List of 2 .. ..$ pca :Formal class \'DimReduc\' [package \"Seurat\"] with 9 slots .. .. .. ..@ cell.embeddings : num [1:80, 1:50] 3.12 3.56 2.4 3.43 2.78 ... .. .. .. .. ..- attr(*, \"dimnames\")=List of 2 .. .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. .. ..$ : chr [1:50] \"PC_1\" \"PC_2\" \"PC_3\" \"PC_4\" ... .. .. .. ..@ feature.loadings : num [1:230, 1:50] 0.05711 0.00738 0.03005 -0.04766 0.05598 ... .. .. .. .. ..- attr(*, \"dimnames\")=List of 2 .. .. .. .. .. ..$ : chr [1:230] \"PPBP\" \"IGLL5\" \"VDAC3\" \"CD1C\" ... .. .. .. .. .. ..$ : chr [1:50] \"PC_1\" \"PC_2\" \"PC_3\" \"PC_4\" ... .. .. .. ..@ feature.loadings.projected: num[0 , 0 ] .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ global : logi FALSE .. .. .. ..@ stdev : num [1:50] 5.75 5.21 4.32 3.62 2.77 ... .. .. .. ..@ key : chr \"PC_\" .. .. .. ..@ jackstraw :Formal class \'JackStrawData\' [package \"Seurat\"] with 4 slots .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ] .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ] .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ] .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ] .. .. .. ..@ misc :List of 1 .. .. .. .. ..$ total.variance: num 230 .. ..$ umap:Formal class \'DimReduc\' [package \"Seurat\"] with 9 slots .. .. .. ..@ cell.embeddings : num [1:80, 1:2] 5.07 5.31 4.72 5.06 5.45 ... .. .. .. .. ..- attr(*, \"scaled:center\")= num [1:2] 1.78 -8.75 .. .. .. .. ..- attr(*, \"dimnames\")=List of 2 .. .. .. .. .. ..$ : chr [1:80] \"ATGCCAGAACGACT\" \"CATGGCCTGTGCAT\" \"GAACCTGATGAACC\" \"TGACTGGATTCTCA\" ... .. .. .. .. .. ..$ : chr [1:2] \"UMAP_1\" \"UMAP_2\" .. .. .. ..@ feature.loadings : num[0 , 0 ] .. .. .. ..@ feature.loadings.projected: num[0 , 0 ] .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ global : logi TRUE .. .. .. ..@ stdev : num(0) .. .. .. ..@ key : chr \"UMAP_\" .. .. .. ..@ jackstraw :Formal class \'JackStrawData\' [package \"Seurat\"] with 4 slots .. .. .. .. .. ..@ empirical.p.values : num[0 , 0 ] .. .. .. .. .. ..@ fake.reduction.scores : num[0 , 0 ] .. .. .. .. .. ..@ empirical.p.values.full: num[0 , 0 ] .. .. .. .. .. ..@ overall.p.values : num[0 , 0 ] .. .. .. ..@ misc : list() ..@ images : list() ..@ project.name: chr \"SeuratProject\" ..@ misc : list() ..@ version :Classes \'package_version\', \'numeric_version\' hidden list of 1 .. ..$ : int [1:3] 3 1 2 ..@ commands :List of 7 .. ..$ NormalizeData.RNA :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"NormalizeData.RNA\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:27\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"NormalizeData(.)\" .. .. .. ..@ params :List of 5 .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ normalization.method: chr \"LogNormalize\" .. .. .. .. ..$ scale.factor : num 10000 .. .. .. .. ..$ margin : num 1 .. .. .. .. ..$ verbose : logi TRUE .. ..$ FindVariableFeatures.RNA:Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"FindVariableFeatures.RNA\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:28\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"FindVariableFeatures(.)\" .. .. .. ..@ params :List of 12 .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ selection.method : chr \"vst\" .. .. .. .. ..$ loess.span : num 0.3 .. .. .. .. ..$ clip.max : chr \"auto\" .. .. .. .. ..$ mean.function :function (mat, display_progress) .. .. .. .. ..$ dispersion.function:function (mat, display_progress) .. .. .. .. ..$ num.bin : num 20 .. .. .. .. ..$ binning.method : chr \"equal_width\" .. .. .. .. ..$ nfeatures : num 2000 .. .. .. .. ..$ mean.cutoff : num [1:2] 0.1 8 .. .. .. .. ..$ dispersion.cutoff : num [1:2] 1 Inf .. .. .. .. ..$ verbose : logi TRUE .. ..$ ScaleData.RNA :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"ScaleData.RNA\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:28\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"ScaleData(.)\" .. .. .. ..@ params :List of 10 .. .. .. .. ..$ features : chr [1:230] \"PPBP\" \"IGLL5\" \"VDAC3\" \"CD1C\" ... .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ model.use : chr \"linear\" .. .. .. .. ..$ use.umi : logi FALSE .. .. .. .. ..$ do.scale : logi TRUE .. .. .. .. ..$ do.center : logi TRUE .. .. .. .. ..$ scale.max : num 10 .. .. .. .. ..$ block.size : num 1000 .. .. .. .. ..$ min.cells.to.block: num 80 .. .. .. .. ..$ verbose : logi TRUE .. ..$ RunPCA.RNA :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"RunPCA.RNA\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:29\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"RunPCA(.)\" .. .. .. ..@ params :List of 10 .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ npcs : num 50 .. .. .. .. ..$ rev.pca : logi FALSE .. .. .. .. ..$ weight.by.var : logi TRUE .. .. .. .. ..$ verbose : logi TRUE .. .. .. .. ..$ ndims.print : int [1:5] 1 2 3 4 5 .. .. .. .. ..$ nfeatures.print: num 30 .. .. .. .. ..$ reduction.name : chr \"pca\" .. .. .. .. ..$ reduction.key : chr \"PC_\" .. .. .. .. ..$ seed.use : num 42 .. ..$ FindNeighbors.RNA.pca :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"FindNeighbors.RNA.pca\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:29\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"FindNeighbors(.)\" .. .. .. ..@ params :List of 13 .. .. .. .. ..$ reduction : chr \"pca\" .. .. .. .. ..$ dims : int [1:10] 1 2 3 4 5 6 7 8 9 10 .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ k.param : num 20 .. .. .. .. ..$ compute.SNN : logi TRUE .. .. .. .. ..$ prune.SNN : num 0.0667 .. .. .. .. ..$ nn.method : chr \"rann\" .. .. .. .. ..$ annoy.metric: chr \"euclidean\" .. .. .. .. ..$ nn.eps : num 0 .. .. .. .. ..$ verbose : logi TRUE .. .. .. .. ..$ force.recalc: logi FALSE .. .. .. .. ..$ do.plot : logi FALSE .. .. .. .. ..$ graph.name : chr [1:2] \"RNA_nn\" \"RNA_snn\" .. ..$ RunUMAP.RNA.pca :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"RunUMAP.RNA.pca\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:33\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"RunUMAP(., 1:10)\" .. .. .. ..@ params :List of 20 .. .. .. .. ..$ dims : int [1:10] 1 2 3 4 5 6 7 8 9 10 .. .. .. .. ..$ reduction : chr \"pca\" .. .. .. .. ..$ assay : chr \"RNA\" .. .. .. .. ..$ umap.method : chr \"uwot\" .. .. .. .. ..$ n.neighbors : int 30 .. .. .. .. ..$ n.components : int 2 .. .. .. .. ..$ metric : chr \"cosine\" .. .. .. .. ..$ learning.rate : num 1 .. .. .. .. ..$ min.dist : num 0.3 .. .. .. .. ..$ spread : num 1 .. .. .. .. ..$ set.op.mix.ratio : num 1 .. .. .. .. ..$ local.connectivity : int 1 .. .. .. .. ..$ repulsion.strength : num 1 .. .. .. .. ..$ negative.sample.rate: int 5 .. .. .. .. ..$ uwot.sgd : logi FALSE .. .. .. .. ..$ seed.use : int 42 .. .. .. .. ..$ angular.rp.forest : logi FALSE .. .. .. .. ..$ verbose : logi TRUE .. .. .. .. ..$ reduction.name : chr \"umap\" .. .. .. .. ..$ reduction.key : chr \"UMAP_\" .. ..$ FindClusters :Formal class \'SeuratCommand\' [package \"Seurat\"] with 5 slots .. .. .. ..@ name : chr \"FindClusters\" .. .. .. ..@ time.stamp : POSIXct[1:1], format: \"2020-06-01 22:43:33\" .. .. .. ..@ assay.used : chr \"RNA\" .. .. .. ..@ call.string: chr \"FindClusters(., dims = 1:0)\" .. .. .. ..@ params :List of 10 .. .. .. .. ..$ graph.name : chr \"RNA_snn\" .. .. .. .. ..$ modularity.fxn : num 1 .. .. .. .. ..$ resolution : num 0.8 .. .. .. .. ..$ method : chr \"matrix\" .. .. .. .. ..$ algorithm : num 1 .. .. .. .. ..$ n.start : num 10 .. .. .. .. ..$ n.iter : num 10 .. .. .. .. ..$ random.seed : num 0 .. .. .. .. ..$ group.singletons: logi TRUE .. .. .. .. ..$ verbose : logi TRUE ..@ tools : list()
甭说看了，拉鼠标手都能拉疼。那么咱们能不能基于str(pbmc)的结果做一个思想导图呢？就像这样：
假如可以这样检查，那不是美滋滋的吗？
需求有了，就差行动了，咱们来找代码：
library(mindr) (out <- capture.output(str(pbmc))) out2 <- paste(out, collapse=\"n\") mm(gsub(\"\\.\\.@\",\"# \",gsub(\"\\.\\. \",\"#\",out2)),type =\"text\",root= \"Seurat\")
这下好了，你对单细胞Seurat数据目标做了什么一目了然。
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