单细胞Seurat数据对象创建思维导图

在我们分析单细胞数据的时候,需要想象力的一点就是要理解数据结构。平时我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里面我们用的是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()

别说看了,拉鼠标手都能拉疼。那么我们能不能基于str(pbmc)的结果做一个思维导图呢?就像这样:

如果能够这样查看,那不是美滋滋的吗?

需求有了,就差行动了,我们来找代码:

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数据对象做了什么一目了然。

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