本文是参考学习 CNS图表复现02—Seurat标准流程之聚类分群的学习笔记。可能根据学习情况有所改动。
今天讲解第二步：完成Seurat标准流程之聚类分群。
直接上代码：
> load(file = "main_tiss_filtered.RData") #加载 load之后右侧environment就可以看到变量名20210109
Loading required package: Seurat
Error: package or namespace load failed for ‘Seurat’ in .doLoadActions(where, attach):
error in load action .__A__.1 for package RcppAnnoy: loadModule(module = "AnnoyAngular", what = TRUE, env = ns, loadNow = TRUE): Unable to load module "AnnoyAngular": attempt to apply non-function
Error in .requirePackage(package) :
unable to find required package ‘Seurat’
In addition: Warning message:
package ‘Seurat’ was built under R version 4.0.3
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
错了，重来,加上library(Seurat)
今天讲解第二步：完成Seurat标准流程之聚类分群。
直接上代码：
> library(Seurat)
Seurat v4 will be going to CRAN in the near future;
for more details, please visit https://satijalab.org/seurat/v4_changes
Warning message:
程辑包‘Seurat’是用R版本4.0.3 来建造的
> load(file = "main_tiss_filtered.RData") #加载 load之后右侧environment就可以看到变量名20210109
> raw_sce <- main_tiss_filtered
> raw_sce
An object of class Seurat
26577 features across 21620 samples within 1 assay
Active assay: RNA (26577 features, 0 variable features)
> rownames(raw_sce)[grepl('^mt-',rownames(raw_sce),ignore.case = T)]
character(0)
> rownames(raw_sce)[grepl('^Rp[sl]',rownames(raw_sce),ignore.case = T)]
[1] "RPL10" "RPL10A" "RPL10L" "RPL11" "RPL12"
[6] "RPL13" "RPL13A" "RPL13AP17" "RPL13AP20" "RPL13AP3"
[11] "RPL13AP5" "RPL13AP6" "RPL13P5" "RPL14" "RPL15"
[16] "RPL17" "RPL17-C18orf32" "RPL18" "RPL18A" "RPL19"
[21] "RPL19P12" "RPL21" "RPL21P28" "RPL21P44" "RPL22"
[26] "RPL22L1" "RPL23" "RPL23A" "RPL23AP32" "RPL23AP53"
[31] "RPL23AP64" "RPL23AP7" "RPL23AP82" "RPL23AP87" "RPL23P8"
[36] "RPL24" "RPL26" "RPL26L1" "RPL27" "RPL27A"
[41] "RPL28" "RPL29" "RPL29P2" "RPL3" "RPL30"
[46] "RPL31" "RPL31P11" "RPL32" "RPL32P3" "RPL34"
[51] "RPL34-AS1" "RPL35" "RPL35A" "RPL36" "RPL36A"
[56] "RPL36A-HNRNPH2" "RPL36AL" "RPL37" "RPL37A" "RPL38"
[61] "RPL39" "RPL39L" "RPL3L" "RPL4" "RPL41"
[66] "RPL5" "RPL6" "RPL7" "RPL7A" "RPL7L1"
[71] "RPL8" "RPL9" "RPLP0" "RPLP0P2" "RPLP1"
[76] "RPLP2" "RPS10" "RPS10-NUDT3" "RPS10P7" "RPS11"
[81] "RPS12" "RPS13" "RPS14" "RPS14P3" "RPS15"
[86] "RPS15A" "RPS15AP10" "RPS16" "RPS16P5" "RPS17"
[91] "RPS18" "RPS18P9" "RPS19" "RPS19BP1" "RPS2"
[96] "RPS20" "RPS21" "RPS23" "RPS24" "RPS25"
[101] "RPS26" "RPS26P11" "RPS27" "RPS27A" "RPS27L"
[106] "RPS28" "RPS29" "RPS2P32" "RPS3" "RPS3A"
[111] "RPS4X" "RPS4Y1" "RPS4Y2" "RPS5" "RPS6"
[116] "RPS6KA1" "RPS6KA2" "RPS6KA2-AS1" "RPS6KA2-IT1" "RPS6KA3"
[121] "RPS6KA4" "RPS6KA5" "RPS6KA6" "RPS6KB1" "RPS6KB2"
[126] "RPS6KC1" "RPS6KL1" "RPS7" "RPS7P5" "RPS8"
[131] "RPS9" "RPSA" "RPSAP52" "RPSAP58" "RPSAP9"
> rownames(raw_sce)[grepl('^MT-',rownames(raw_sce),ignore.case = T)]
character(0)
> rownames(raw_sce)[grepl('^Rp[sl]',rownames(raw_sce),ignore.case = T)]
[1] "RPL10" "RPL10A" "RPL10L" "RPL11" "RPL12"
[6] "RPL13" "RPL13A" "RPL13AP17" "RPL13AP20" "RPL13AP3"
[11] "RPL13AP5" "RPL13AP6" "RPL13P5" "RPL14" "RPL15"
[16] "RPL17" "RPL17-C18orf32" "RPL18" "RPL18A" "RPL19"
[21] "RPL19P12" "RPL21" "RPL21P28" "RPL21P44" "RPL22"
[26] "RPL22L1" "RPL23" "RPL23A" "RPL23AP32" "RPL23AP53"
[31] "RPL23AP64" "RPL23AP7" "RPL23AP82" "RPL23AP87" "RPL23P8"
[36] "RPL24" "RPL26" "RPL26L1" "RPL27" "RPL27A"
[41] "RPL28" "RPL29" "RPL29P2" "RPL3" "RPL30"
[46] "RPL31" "RPL31P11" "RPL32" "RPL32P3" "RPL34"
[51] "RPL34-AS1" "RPL35" "RPL35A" "RPL36" "RPL36A"
[56] "RPL36A-HNRNPH2" "RPL36AL" "RPL37" "RPL37A" "RPL38"
[61] "RPL39" "RPL39L" "RPL3L" "RPL4" "RPL41"
[66] "RPL5" "RPL6" "RPL7" "RPL7A" "RPL7L1"
[71] "RPL8" "RPL9" "RPLP0" "RPLP0P2" "RPLP1"
[76] "RPLP2" "RPS10" "RPS10-NUDT3" "RPS10P7" "RPS11"
[81] "RPS12" "RPS13" "RPS14" "RPS14P3" "RPS15"
[86] "RPS15A" "RPS15AP10" "RPS16" "RPS16P5" "RPS17"
[91] "RPS18" "RPS18P9" "RPS19" "RPS19BP1" "RPS2"
[96] "RPS20" "RPS21" "RPS23" "RPS24" "RPS25"
[101] "RPS26" "RPS26P11" "RPS27" "RPS27A" "RPS27L"
[106] "RPS28" "RPS29" "RPS2P32" "RPS3" "RPS3A"
[111] "RPS4X" "RPS4Y1" "RPS4Y2" "RPS5" "RPS6"
[116] "RPS6KA1" "RPS6KA2" "RPS6KA2-AS1" "RPS6KA2-IT1" "RPS6KA3"
[121] "RPS6KA4" "RPS6KA5" "RPS6KA6" "RPS6KB1" "RPS6KB2"
[126] "RPS6KC1" "RPS6KL1" "RPS7" "RPS7P5" "RPS8"
[131] "RPS9" "RPSA" "RPSAP52" "RPSAP58" "RPSAP9"
> raw_sce[["percent.mt"]] <- PercentageFeatureSet(raw_sce, pattern = "^MT-")
> fivenum(raw_sce[["percent.mt"]][,1])
[1] 0 0 0 0 0
> rb.genes <- rownames(raw_sce)[grep("^RP[SL]",rownames(raw_sce),ignore.case = T)]
> C<-GetAssayData(object = raw_sce, slot = "counts")
> percent.ribo <- Matrix::colSums(C[rb.genes,])/Matrix::colSums(C)*100
> fivenum(percent.ribo)
A12_B001464 L19_B003105 M3_B001543 I21_B003528 E5_B003659
0.000000 2.196870 3.409555 5.444660 49.341911
> raw_sce <- AddMetaData(raw_sce, percent.ribo, col.name = "percent.ribo")
> plot1 <- FeatureScatter(raw_sce, feature1 = "nCount_RNA", feature2 = "percent.ercc")
Error: Feature 2 (percent.ercc) not found.
In addition: Warning message:
In FetchData(object = object, vars = c(feature1, feature2, group.by), :
The following requested variables were not found: percent.ercc
> plot2 <- FeatureScatter(raw_sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
> CombinePlots(plots = list(plot1, plot2))
Error in CombinePlots(plots = list(plot1, plot2)) :
object 'plot1' not found
In addition: Warning message:
CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
> VlnPlot(raw_sce, features = c("percent.ribo", "percent.ercc"), ncol = 2)
Warning message:
In FetchData(object = object, vars = features, slot = slot) :
The following requested variables were not found: percent.ercc
> VlnPlot(raw_sce, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
> VlnPlot(raw_sce, features = c("percent.ribo", "nCount_RNA"), ncol = 2)
> raw_sce
An object of class Seurat
26577 features across 21620 samples within 1 assay
Active assay: RNA (26577 features, 0 variable features)
> sce=raw_sce
> sce
An object of class Seurat
26577 features across 21620 samples within 1 assay
Active assay: RNA (26577 features, 0 variable features)
> sce <- NormalizeData(sce, normalization.method = "LogNormalize",
+ scale.factor = 10000)
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
> GetAssay(sce,assay = "RNA")
Assay data with 26577 features for 21620 cells
First 10 features:
A1BG, A1BG-AS1, A1CF, A2M, A2M-AS1, A2ML1, A2MP1, A3GALT2, A4GALT, A4GNT
> sce <- FindVariableFeatures(sce,
+ selection.method = "vst", nfeatures = 2000)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
> # 步骤 ScaleData 的耗时取决于电脑系统配置（保守估计大于一分钟）
> sce <- ScaleData(sce)
Centering and scaling data matrix
|=======================================================================================| 100%
> sce <- RunPCA(object = sce, pc.genes = VariableFeatures(sce))
PC_ 1
Positive: CORO1A, CXCR4, IL2RG, CD52, RHOH, ALOX5AP, GPR183, CD69, ISG20, LTB
CST7, CCL5, LCK, UCP2, FERMT3, SERPINB9, ARHGAP30, TUBA4A, CD247, AMICA1
CD27, CCR7, NKG7, TBC1D10C, SASH3, S1PR4, SELL, INPP5D, CTSW, TRAF3IP3
Negative: SPARC, CALD1, DCN, COL1A2, IGFBP7, LUM, MGP, COL3A1, THY1, RARRES2
FBLN1, MFAP4, SPARCL1, COL1A1, TIMP3, TPM2, CNN3, CYR61, TAGLN, SERPINF1
LHFP, CTSK, PDGFRB, CTGF, CD248, CRISPLD2, PRELP, COL5A1, ACTA2, OLFML3
PC_ 2
Positive: TSPAN1, SLPI, KRT18, PIGR, RSPH1, SLC34A2, PSCA, PIFO, SNTN, AGR2
C20orf85, FAM183A, CAPS, C9orf24, TMEM190, LDLRAD1, CAPSL, C1orf194, ZMYND10, CCDC78
C11orf88, TEKT1, WDR38, ROPN1L, RSPH9, FAM92B, TEKT2, DEGS2, TUBA4B, LCN2
Negative: CORO1A, CXCR4, DCN, COL1A2, A2M, LUM, SPARC, ZEB2, COL3A1, FN1
THY1, IL2RG, CD52, SERPINB9, MFAP4, PDGFRB, ALOX5AP, CTSK, TAGLN, CALD1
SERPINF1, ERCC-00171, GPR183, OLFML3, SPARCL1, CD248, PRELP, RHOH, SFRP2, CCND2
PC_ 3
Positive: NAPSA, SFTPB, RNASE1, SERPINA1, SFTPA1, SFTPA2, SFTPD, SFTA3, C4BPA, CEACAM6
C16orf89, SLC22A31, PON3, SCGB3A2, EFNA1, SLC34A2, LGMN, AQP4, ABCA3, PEBP4
SCGB3A1, S100A9, SFTPC, SCD, PGC, HSD17B6, CTSE, FTL, SUSD2, PON2
Negative: C20orf85, C9orf24, TMEM190, SNTN, CAPSL, C1orf194, FAM183A, RSPH1, C11orf88, ZMYND10
LDLRAD1, TEKT1, WDR38, TUBA4B, FAM92B, ROPN1L, RSPH9, CCDC78, PIFO, C2orf40
TEKT2, CAPS, C22orf15, SPAG8, PSCA, TPPP3, WDR54, GSTA1, CRIP1, SCGB2A1
PC_ 4
Positive: FCGR2A, MS4A7, FTL, IL1B, TREM2, MS4A4A, OLR1, CLEC7A, APOE, APOC1
MARCKS, GPNMB, TGFBI, FOLR2, CPVL, IL1RN, CXCL3, SPP1, HMOX1, HLA-DMB
ZEB2, FCN1, CFD, NLRP3, CCL2, S100A8, PLA2G7, SLC8A1, C20orf85, TMEM190
Negative: IL32, TUBA4A, LCK, CD247, PRF1, IL2RG, CCL5, NKG7, OCIAD2, CD96
CTSW, RHOH, GZMM, HOPX, ZAP70, GZMA, SH2D1A, CD8A, CST7, CCND3
CXCR6, FAM46C, CXCR3, TBCC, CD27, TBC1D10C, EFNA1, LEPROTL1, CD69, GZMH
PC_ 5
Positive: FBLN1, DCN, SFRP2, SERPINF1, LUM, CTSK, COL1A2, COL3A1, COL1A1, RARRES2
SFRP4, OLFML3, MFAP4, CXCL14, EFEMP1, IGF1, ADH1B, MFAP5, COL5A1, DPT
HTRA3, FGF7, SULF1, CRISPLD2, FNDC1, SLIT2, COL12A1, FBLN5, WISP2, PRELP
Negative: CLEC14A, RAMP3, CLDN5, RAMP2, VWF, CDH5, ESAM, ECSCR, PLVAP, SOX18
EGFL7, HYAL2, GNG11, FAM107A, AQP1, CXorf36, CD34, FCN3, SDPR, ACKR1
CLEC3B, PODXL, KANK3, TEK, COL4A1, NES, AFAP1L1, TINAGL1, ARHGEF15, C2CD4B
> DimHeatmap(sce, dims = 1:12, cells = 100, balanced = TRUE)
> ElbowPlot(sce)
> sce <- FindNeighbors(sce, dims = 1:15)
Computing nearest neighbor graph
Computing SNN
> sce <- FindClusters(sce, resolution = 0.2)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 21620
Number of edges: 759616
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9684
Number of communities: 17
Elapsed time: 4 seconds
> table([email protected]$RNA_snn_res.0.2)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
4447 4001 2661 2381 1881 1406 997 957 740 554 434 407 219 184 173 130 48
> sce <- FindClusters(sce, resolution = 0.8)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 21620
Number of edges: 759616
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9276
Number of communities: 30
Elapsed time: 5 seconds
> table([email protected]$RNA_snn_res.0.8)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
2148 2075 1652 1607 1208 1045 1020 999 997 971 957 891 739 725 623 554 511 434 405
19 20 21 22 23 24 25 26 27 28 29
380 333 231 219 189 173 136 130 118 102 48
> library(gplots)
载入程辑包：‘gplots’
The following object is masked from ‘package:stats’:
lowess
Warning message:
程辑包‘gplots’是用R版本4.0.3 来建造的
> tab.1=table([email protected]$RNA_snn_res.0.2,[email protected]$RNA_snn_res.0.8)
> balloonplot(tab.1)
> set.seed(123)
> sce <- RunTSNE(object = sce, dims = 1:15, do.fast = TRUE)
> DimPlot(sce,reduction = "tsne",label=T)
Warning message:
Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
> phe=data.frame(cell=rownames([email protected]),
+ cluster [email protected]$seurat_clusters)
> head(phe)
cell cluster
1 A10_1001000329 2
2 A10_1001000407 10
3 A10_1001000408 10
4 A10_1001000410 1
5 A10_1001000412 10
6 A10_B000420 16
> table(phe$cluster)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
2148 2075 1652 1607 1208 1045 1020 999 997 971 957 891 739 725 623 554 511 434 405
19 20 21 22 23 24 25 26 27 28 29
380 333 231 219 189 173 136 130 118 102 48
> tsne_pos=Embeddings(sce,'tsne')
> DimPlot(sce,reduction = "tsne",group.by ='orig.ident')
> DimPlot(sce,reduction = "tsne",label=T,split.by ='orig.ident')
> head(phe)
cell cluster
1 A10_1001000329 2
2 A10_1001000407 10
3 A10_1001000408 10
4 A10_1001000410 1
5 A10_1001000412 10
6 A10_B000420 16
> table(phe$cluster)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
2148 2075 1652 1607 1208 1045 1020 999 997 971 957 891 739 725 623 554 511 434 405
19 20 21 22 23 24 25 26 27 28 29
380 333 231 219 189 173 136 130 118 102 48
> head(tsne_pos)
tSNE_1 tSNE_2
A10_1001000329 -19.916177 -20.300083
A10_1001000407 -26.883484 -8.166466
A10_1001000408 -37.016645 -14.658582
A10_1001000410 23.665744 -15.812134
A10_1001000412 -37.017060 -11.048993
A10_B000420 -6.313442 -18.903210
> dat=cbind(tsne_pos,phe)
> pro='first'
> save(dat,file=paste0(pro,'_for_tSNE.pos.Rdata'))
> load(file=paste0(pro,'_for_tSNE.pos.Rdata'))
> library(ggplot2)
> p=ggplot(dat,aes(x=tSNE_1,y=tSNE_2,color=cluster))+geom_point(size=0.95)
> p=p+stat_ellipse(data=dat,aes(x=tSNE_1,y=tSNE_2,fill=cluster,color=cluster),
+ geom = "polygon",alpha=0.2,level=0.9,type="t",linetype = 2,show.legend = F)+coord_fixed()
> print(p)
Warning message:
In MASS::cov.trob(data[, vars]) : Probable convergence failure
> theme= theme(panel.grid =element_blank()) + ## 删去网格
+ theme(panel.border = element_blank(),panel.background = element_blank()) + ## 删去外层边框
+ theme(axis.line = element_line(size=1, colour = "black"))
> p=p+theme+guides(colour = guide_legend(override.aes = list(size=5)))
> print(p)
Warning message:
In MASS::cov.trob(data[, vars]) : Probable convergence failure
> ggplot2::ggsave(filename = paste0(pro,'_tsne_res0.8.pdf'))
Saving 6.4 x 3.77 in image
Warning message:
In MASS::cov.trob(data[, vars]) : Probable convergence failure
> sce <- RunUMAP(object = sce, dims = 1:15, do.fast = TRUE)
Warning: The following arguments are not used: do.fast
Warning: 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
20:53:01 UMAP embedding parameters a = 0.9922 b = 1.112
20:53:01 Read 21620 rows and found 15 numeric columns
20:53:01 Using Annoy for neighbor search, n_neighbors = 30
20:53:01 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:53:07 Writing NN index file to temp file C:\Users\Nano\AppData\Local\Temp\RtmpGkHwMb\file2032223c6e
20:53:07 Searching Annoy index using 1 thread, search_k = 3000
20:53:18 Annoy recall = 100%
20:53:18 Commencing smooth kNN distance calibration using 1 thread
20:53:21 Initializing from normalized Laplacian + noise
20:53:27 Commencing optimization for 200 epochs, with 909916 positive edges
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:54:05 Optimization finished
> DimPlot(sce,reduction = "umap",label=T)
> DimPlot(sce,reduction = "umap",group.by = 'orig.ident')
> plot1 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "percent.mt")
Warning message:
In cor(x = data[, 1], y = data[, 2]) : 标准差为零
> plot2 <- FeatureScatter(sce, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
> CombinePlots(plots = list(plot1, plot2))
Warning message:
CombinePlots is being deprecated. Plots should now be combined using the patchwork system.
> ggplot2::ggsave(filename = paste0(pro,'_CombinePlots.pdf'))
Saving 6.4 x 3.77 in image
> VlnPlot(sce, features = c("percent.ribo", "percent.mt"), ncol = 2)
Warning message:
In SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents = idents, :
All cells have the same value of percent.mt.
> ggplot2::ggsave(filename = paste0(pro,'_mt-and-ribo.pdf'))
Saving 6.4 x 3.77 in image
> VlnPlot(sce, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
> ggplot2::ggsave(filename = paste0(pro,'_counts-and-feature.pdf'))
Saving 6.4 x 3.77 in image
> VlnPlot(sce, features = c("percent.ribo", "nCount_RNA"), ncol = 2)
> table([email protected]$seurat_clusters)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
2148 2075 1652 1607 1208 1045 1020 999 997 971 957 891 739 725 623 554 511 434 405
19 20 21 22 23 24 25 26 27 28 29
380 333 231 219 189 173 136 130 118 102 48
下面这一步时间较长
16G内存电脑跑了2个小时
> for( i in unique([email protected]$seurat_clusters) ){
+ markers_df <- FindMarkers(object = sce, ident.1 = i, min.pct = 0.25)
+ print(x = head(markers_df))
+ markers_genes = rownames(head(x = markers_df, n = 5))
+ VlnPlot(object = sce, features =markers_genes,log =T )
+ ggsave(filename=paste0(pro,'_VlnPlot_subcluster_',i,'_sce.markers_heatmap.pdf'))
+ FeaturePlot(object = sce, features=markers_genes )
+ ggsave(filename=paste0(pro,'_FeaturePlot_subcluster_',i,'_sce.markers_heatmap.pdf'))
+ }
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 24s
p_val avg_logFC pct.1 pct.2 p_val_adj
LYZ 0 2.371829 0.973 0.240 0
FCN1 0 2.295688 0.651 0.063 0
IL1B 0 2.164884 0.809 0.163 0
EREG 0 1.880129 0.539 0.081 0
OLR1 0 1.755714 0.697 0.119 0
CXCL3 0 1.593757 0.604 0.210 0
Saving 6.4 x 3.77 in image
Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07m 48s
p_val avg_logFC pct.1 pct.2 p_val_adj
LCN2 0 3.340067 1 0.093 0
MUC20 0 2.946946 1 0.133 0
CD24 0 2.855278 1 0.153 0
KRT7 0 2.848640 1 0.168 0
SCCPDH 0 2.700061 1 0.220 0
WFDC2 0 2.677898 1 0.202 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 44s
p_val avg_logFC pct.1 pct.2 p_val_adj
IL7R 0 2.109133 0.813 0.144 0
CCR7 0 1.834167 0.598 0.105 0
LCK 0 1.677304 0.708 0.093 0
CXCR4 0 1.622574 0.893 0.395 0
SARAF 0 1.604799 0.971 0.756 0
SPOCK2 0 1.527563 0.731 0.107 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 54s
p_val avg_logFC pct.1 pct.2 p_val_adj
CD1C 0 2.012028 0.499 0.022 0
NAPSB 0 1.974966 0.822 0.134 0
HLA-DQA1 0 1.857886 0.975 0.353 0
CD1E 0 1.829083 0.472 0.014 0
FCER1A 0 1.728109 0.419 0.028 0
CLEC10A 0 1.233114 0.499 0.058 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 13s
p_val avg_logFC pct.1 pct.2 p_val_adj
SPP1 0 3.180647 0.731 0.120 0
C1QB 0 2.705537 0.924 0.088 0
APOE 0 2.656615 0.878 0.229 0
C1QA 0 2.558506 0.942 0.090 0
CD14 0 2.246694 0.975 0.175 0
C1QC 0 2.182844 0.932 0.074 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 56s
p_val avg_logFC pct.1 pct.2 p_val_adj
NAPSA 0 2.023432 0.621 0.107 0
AZGP1 0 1.741359 0.336 0.027 0
EPCAM 0 1.716374 0.769 0.196 0
KRT19 0 1.706181 0.842 0.238 0
CEACAM6 0 1.678523 0.709 0.158 0
KRT18 0 1.625329 0.812 0.260 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 53s
p_val avg_logFC pct.1 pct.2 p_val_adj
GNLY 0 3.114463 0.322 0.019 0
CCL5 0 2.708346 0.949 0.133 0
NKG7 0 2.681242 0.801 0.058 0
PRF1 0 2.647103 0.706 0.052 0
CTSW 0 2.243186 0.660 0.058 0
GZMB 0 2.220752 0.441 0.036 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 09s
p_val avg_logFC pct.1 pct.2 p_val_adj
SCGB3A2 0 2.781470 0.608 0.042 0
SCGB3A1 0 2.650964 0.829 0.067 0
SFTPB 0 2.519461 0.978 0.104 0
AQP4 0 2.478347 0.825 0.043 0
SFTPD 0 2.159180 0.832 0.052 0
C4BPA 0 2.063080 0.819 0.052 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 10s
p_val avg_logFC pct.1 pct.2 p_val_adj
RGS5 0 3.199030 0.850 0.046 0
ACTA2 0 3.025543 0.845 0.150 0
COL4A1 0 2.565525 0.863 0.108 0
THY1 0 2.555382 0.692 0.091 0
IGFBP7 0 2.550436 1.000 0.315 0
TAGLN 0 2.445983 0.824 0.159 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 57s
p_val avg_logFC pct.1 pct.2 p_val_adj
CLDN5 0 3.735707 0.854 0.010 0
ACKR1 0 3.552193 0.356 0.007 0
RAMP3 0 2.997716 0.799 0.009 0
HYAL2 0 2.955281 0.877 0.196 0
VWF 0 2.936765 0.788 0.018 0
AQP1 0 2.864674 0.773 0.096 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 49s
p_val avg_logFC pct.1 pct.2 p_val_adj
IGLL5 0 5.361883 0.962 0.033 0
JCHAIN 0 5.272503 0.866 0.056 0
MZB1 0 4.222333 0.994 0.047 0
DERL3 0 3.083926 0.981 0.089 0
HERPUD1 0 2.982221 0.984 0.645 0
SSR4 0 2.940790 0.995 0.776 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 44s
p_val avg_logFC pct.1 pct.2 p_val_adj
TPSAB1 0 6.019722 1.000 0.010 0
TPSB2 0 5.329733 0.998 0.008 0
CPA3 0 3.712487 0.998 0.005 0
MS4A2 0 3.448981 0.991 0.006 0
CTSG 0 3.240622 0.502 0.003 0
TPSD1 0 2.874191 0.713 0.003 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 07s
p_val avg_logFC pct.1 pct.2 p_val_adj
MMP11 0 3.735834 0.684 0.034 0
COL3A1 0 3.715876 0.987 0.083 0
COL1A2 0 3.642927 0.996 0.107 0
SPARC 0 3.066119 0.994 0.209 0
LUM 0 2.895565 0.870 0.077 0
SFRP2 0 2.722759 0.724 0.037 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 28s
p_val avg_logFC pct.1 pct.2 p_val_adj
MFAP4 0 3.935752 0.954 0.054 0
INMT 0 3.328297 0.742 0.047 0
MGP 0 3.267342 0.970 0.135 0
CFD 0 3.164279 0.738 0.255 0
DCN 0 3.098411 0.959 0.083 0
FBLN1 0 3.043089 0.834 0.137 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08m 37s
p_val avg_logFC pct.1 pct.2 p_val_adj
CSF3R 0 3.018346 0.780 0.157 0
G0S2 0 2.801950 0.729 0.244 0
ADGRG3 0 2.746309 0.500 0.014 0
S100A8 0 2.636231 0.785 0.176 0
PROK2 0 2.427165 0.411 0.018 0
FCGR3B 0 2.365168 0.611 0.037 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~14s Error in UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
In addition: Warning messages:
1: In grid.Call.graphics(C_setviewport, vp, TRUE) :
'layout.pos.row'的值不对
2: In doTryCatch(return(expr), name, parentenv, handler) :
无法弹到最上层的視窗('grid'和'graphics'输出有混合？)
3: In UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
Graphics error: Plot rendering error
Error in UseMethod("depth") :
no applicable method for 'depth' applied to an object of class "NULL"
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
Graphics error: Plot rendering error
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06m 41s
p_val avg_logFC pct.1 pct.2 p_val_adj
KRT13 0 4.230822 0.880 0.016 0
KRT6A 0 3.914537 0.943 0.022 0
ALDH3A1 0 3.790334 0.886 0.049 0
AKR1B10 0 3.135880 0.825 0.021 0
AKR1C2 0 3.017500 0.945 0.062 0
AKR1C3 0 2.991269 0.893 0.107 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 59s
p_val avg_logFC pct.1 pct.2 p_val_adj
MS4A1 0 2.154018 0.583 0.021 0
TCL1A 0 2.086960 0.274 0.007 0
NAPSB 0 1.864919 0.753 0.133 0
SPIB 0 1.775952 0.636 0.046 0
LILRA4 0 1.755557 0.311 0.027 0
BCL11A 0 1.603087 0.772 0.106 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 08s
p_val avg_logFC pct.1 pct.2 p_val_adj
PSAP 2.338026e-220 -1.3965945 0.331 0.850 6.213770e-216
CTSD 5.511924e-209 -1.5798582 0.200 0.743 1.464904e-204
IFITM3 3.069978e-187 -1.8009139 0.350 0.770 8.159080e-183
CD63 1.130407e-183 -0.7787599 0.352 0.822 3.004283e-179
ITM2B 5.809383e-179 -0.7746119 0.474 0.910 1.543960e-174
LAPTM4A 5.368902e-176 -1.0974041 0.217 0.709 1.426893e-171
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 37s
p_val avg_logFC pct.1 pct.2 p_val_adj
ALB 0 5.481862 0.985 0.013 0
FGB 0 4.388274 0.646 0.010 0
AMBP 0 4.179769 0.954 0.010 0
APOA2 0 4.032604 0.631 0.009 0
APOA1 0 4.021442 0.600 0.017 0
VTN 0 3.921150 0.946 0.017 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 13s
p_val avg_logFC pct.1 pct.2 p_val_adj
MKI67 0 1.6254500 0.905 0.053 0
TOP2A 0 1.0759263 0.762 0.059 0
BIRC5 0 0.9886379 0.788 0.060 0
RRM2 0 0.9704843 0.619 0.039 0
TPX2 0 0.9488848 0.709 0.050 0
AURKB 0 0.9137951 0.582 0.031 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06m 06s
p_val avg_logFC pct.1 pct.2 p_val_adj
SFTPC 0 6.187119 0.988 0.039 0
SFTPA2 0 4.745303 0.988 0.063 0
SFTPA1 0 4.371754 0.991 0.059 0
SFTPB 0 3.764590 1.000 0.115 0
SFTPD 0 3.395693 0.991 0.060 0
PGC 0 3.343723 0.933 0.026 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03m 46s
p_val avg_logFC pct.1 pct.2 p_val_adj
SCGB3A2 0 3.150003 0.970 0.052 0
SFTPA1 0 2.937482 0.996 0.068 0
NAPSA 0 2.693990 0.991 0.122 0
C16orf89 0 2.628919 0.970 0.112 0
C4BPA 0 2.539893 0.974 0.068 0
HPGD 0 2.522995 0.987 0.143 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07m 41s
p_val avg_logFC pct.1 pct.2 p_val_adj
TPPP3 0 4.134702 1.000 0.110 0
TSPAN1 0 3.701205 1.000 0.157 0
C20orf85 0 3.642036 1.000 0.006 0
CAPS 0 3.583035 1.000 0.102 0
TMEM190 0 3.490252 0.993 0.007 0
RSPH1 0 3.293992 0.993 0.041 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 19s
p_val avg_logFC pct.1 pct.2 p_val_adj
CXCL13 0 2.965052 0.347 0.013 0
MKI67 0 2.328364 0.973 0.051 0
RRM2 0 2.035326 0.817 0.036 0
AURKB 0 1.594833 0.744 0.028 0
CDC20 0 1.581626 0.626 0.042 0
ASF1B 0 1.557680 0.831 0.058 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 17s
p_val avg_logFC pct.1 pct.2 p_val_adj
MUC5B 0 3.877584 0.985 0.064 0
DMBT1 0 2.888433 0.688 0.037 0
CEACAM6 0 2.716520 0.973 0.172 0
AGR2 0 2.702805 0.979 0.170 0
LRIG3 0 2.116126 0.955 0.085 0
TNC 0 1.950363 0.889 0.082 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 39s
p_val avg_logFC pct.1 pct.2 p_val_adj
AGER 0 5.424912 1.000 0.058 0
CYP4B1 0 3.777285 0.960 0.081 0
CLDN18 0 3.359197 0.983 0.047 0
UPK3B 0 2.764430 0.896 0.045 0
SUSD2 0 2.528722 0.908 0.092 0
RTKN2 0 2.460933 0.896 0.063 0
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06m 04s
p_val avg_logFC pct.1 pct.2 p_val_adj
TCL1A 0.000000e+00 3.1446182 0.792 0.013 0.000000e+00
AICDA 0.000000e+00 1.8957092 0.729 0.006 0.000000e+00
PAX5 0.000000e+00 0.7329505 0.917 0.024 0.000000e+00
SNX29P2 1.053131e-300 0.8000521 0.750 0.018 2.798906e-296
AURKB 7.249962e-298 2.0674165 1.000 0.034 1.926822e-293
DTX1 5.860053e-277 0.5553459 0.750 0.019 1.557426e-272
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12m 12s
p_val avg_logFC pct.1 pct.2 p_val_adj
COL1A1 2.893780e-110 5.474136 0.941 0.204 7.690800e-106
COL3A1 1.432534e-108 1.854252 0.882 0.120 3.807246e-104
TWIST1 1.613721e-108 2.407123 0.490 0.044 4.288785e-104
COL1A2 6.068805e-82 1.138744 0.873 0.143 1.612906e-77
B2M 2.067944e-53 -2.357415 0.843 0.989 5.495975e-49
CFL1 7.346010e-52 -1.995883 0.588 0.951 1.952349e-47
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 13s
p_val avg_logFC pct.1 pct.2 p_val_adj
DLK1 0.000000e+00 2.9797640 0.309 0.003 0.000000e+00
ASCL1 0.000000e+00 0.8161364 0.309 0.002 0.000000e+00
INSM1 0.000000e+00 0.6706563 0.265 0.003 0.000000e+00
SIX3 0.000000e+00 0.4142789 0.257 0.004 0.000000e+00
ZIC2 4.602548e-295 0.8194995 0.279 0.006 1.223219e-290
ADCYAP1 4.918342e-257 0.6012040 0.272 0.007 1.307148e-252
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Saving 6.4 x 3.77 in image
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 09s
p_val avg_logFC pct.1 pct.2 p_val_adj
PMEL 0 5.042566 0.932 0.069 0
MLANA 0 4.037025 0.932 0.050 0
TYRP1 0 4.023393 0.932 0.042 0
DCT 0 3.715292 0.932 0.033 0
TYR 0 3.243960 0.932 0.029 0
BCAN 0 2.605913 0.932 0.033 0
Saving 6.4 x 3.77 in image
Saving 6.4 x 3.77 in image
找marker也耗时近30min
sce.markers <- FindAllMarkers(object = sce, only.pos = TRUE, min.pct = 0.25,
thresh.use = 0.25)
DT::datatable(sce.markers)
write.csv(sce.markers,file=paste0(pro,'_sce.markers.csv'))
library(dplyr)
top10 <- sce.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
DoHeatmap(sce,top10$gene,size=3)
ggsave(filename=paste0(pro,'_sce.markers_heatmap.pdf'))
save(sce,sce.markers,file = 'first_sce.Rdata')