Running data.table in English; package support is available in English only. When searching for online help, be sure to also check for the English error message. This can be obtained by looking at the po/R-<locale>.po and po/<locale>.po files in the package source, where the native language and English error messages can be found side-by-side. You can also try calling Sys.setLanguage('en') prior to reproducing the error message.
**********
载入程序包：‘data.table’
The following object is masked from ‘package:SummarizedExperiment’:
shift
The following object is masked from ‘package:GenomicRanges’:
shift
The following object is masked from ‘package:IRanges’:
shift
The following objects are masked from ‘package:S4Vectors’:
first, second
The following objects are masked from ‘package:lubridate’:
hour, isoweek, mday, minute, month, quarter, second, wday,
week, yday, year
The following objects are masked from ‘package:dplyr’:
between, first, last
The following object is masked from ‘package:purrr’:
transpose
> library(ggpubr)
> library(glmGamPoi)# 提高doubletFinder速度
载入程序包：‘glmGamPoi’
The following object is masked from ‘package:dplyr’:
vars
The following object is masked from ‘package:ggplot2’:
vars
> getwd()
[1] "C:/Users/lenovo/Documents"
> setwd("D:/FQY_NELL1")
> options(future.globals.maxSize = 4 * 1024^3) # 设置为 4GB
> set.seed(123)
> dirs = c('./data/shNELL1_1/',
+ './data/shNELL1_2/',
+ './data/zipctl_1/',
+ './data/zipctl_2/')
> ## SoupX ####
> loadSoupX <- function(dir){
+ tod <- Read10X(paste(dir, '/raw_feature_bc_matrix', sep = ''), gene.column = 1)
+ toc <- Read10X(paste(dir, '/filtered_feature_bc_matrix', sep = ''), gene.column = 1)
+ tod <- tod[rownames(toc),]
+
+ all <- toc
+ all <- CreateSeuratObject(all)
+ all <- NormalizeData(all, normalization.method = "LogNormalize", scale.factor = 10000)
+ all <- FindVariableFeatures(all, selection.method = "vst", nfeatures = 3000)
+ all.genes <- rownames(all)
+ all <- ScaleData(all, features = all.genes)
+ all <- RunPCA(all, features = VariableFeatures(all), npcs = 40, verbose = F)
+ all <- FindNeighbors(all, dims = 1:30)
+ all <- FindClusters(all, resolution = 0.5)
+ all <- RunUMAP(all, dims = 1:30)
+ matx <- all@meta.data
+ sc = SoupChannel(tod, toc)
+ sc = setClusters(sc, setNames(matx$seurat_clusters, rownames(matx)))
+
+ print(paste(dir, 'SOUPX Finish!', sep = ' '))
+ return(sc)
+ }
> Soupx_list <- list()
> for (i in 1:length(dirs)) {
+ sc = loadSoupX(dirs[i])
+ sc = autoEstCont(sc)
+ out = adjustCounts(sc)
+ Soupx_list[i] <- out
+ print(paste(i, 'is over', sep = ' '))
+ }
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|==============================================================| 100%
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 9191
Number of edges: 367645
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9424
Number of communities: 22
Elapsed time: 0 seconds
警告: 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
17:01:34 UMAP embedding parameters a = 0.9922 b = 1.112
17:01:34 Read 9191 rows and found 30 numeric columns
17:01:34 Using Annoy for neighbor search, n_neighbors = 30
17:01:34 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:01:35 Writing NN index file to temp file C:\Users\lenovo\AppData\Local\Temp\RtmpSkA7CH\file240845ab5acb
17:01:35 Searching Annoy index using 1 thread, search_k = 3000
17:01:36 Annoy recall = 100%
17:01:37 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:01:39 Initializing from normalized Laplacian + noise (using RSpectra)
17:01:39 Commencing optimization for 500 epochs, with 407622 positive edges
17:01:39 Using rng type: pcg
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:01:55 Optimization finished
[1] "./data/shNELL1_1/ SOUPX Finish!"
2970 genes passed tf-idf cut-off and 417 soup quantile filter. Taking the top 100.
Using 1121 independent estimates of rho.
Estimated global rho of 0.12
Expanding counts from 22 clusters to 9191 cells.
[1] "1 is over"
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|==============================================================| 100%
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 12275
Number of edges: 496718
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9498
Number of communities: 26
Elapsed time: 0 seconds
17:03:00 UMAP embedding parameters a = 0.9922 b = 1.112
17:03:00 Read 12275 rows and found 30 numeric columns
17:03:00 Using Annoy for neighbor search, n_neighbors = 30
17:03:00 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:03:01 Writing NN index file to temp file C:\Users\lenovo\AppData\Local\Temp\RtmpSkA7CH\file24082e643ddb
17:03:01 Searching Annoy index using 1 thread, search_k = 3000
17:03:03 Annoy recall = 100%
17:03:04 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:03:05 Initializing from normalized Laplacian + noise (using RSpectra)
17:03:06 Commencing optimization for 200 epochs, with 557592 positive edges
17:03:06 Using rng type: pcg
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:03:15 Optimization finished
[1] "./data/shNELL1_2/ SOUPX Finish!"
3609 genes passed tf-idf cut-off and 619 soup quantile filter. Taking the top 100.
Using 1448 independent estimates of rho.
Estimated global rho of 0.09
Expanding counts from 26 clusters to 12275 cells.
[1] "2 is over"
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|==============================================================| 100%
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 11587
Number of edges: 455875
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9490
Number of communities: 28
Elapsed time: 0 seconds
17:04:22 UMAP embedding parameters a = 0.9922 b = 1.112
17:04:22 Read 11587 rows and found 30 numeric columns
17:04:22 Using Annoy for neighbor search, n_neighbors = 30
17:04:22 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:04:22 Writing NN index file to temp file C:\Users\lenovo\AppData\Local\Temp\RtmpSkA7CH\file2408d6d5010
17:04:22 Searching Annoy index using 1 thread, search_k = 3000
17:04:24 Annoy recall = 100%
17:04:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:04:27 Initializing from normalized Laplacian + noise (using RSpectra)
17:04:27 Commencing optimization for 200 epochs, with 517796 positive edges
17:04:27 Using rng type: pcg
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:04:35 Optimization finished
[1] "./data/zipctl_1/ SOUPX Finish!"
2851 genes passed tf-idf cut-off and 578 soup quantile filter. Taking the top 100.
Using 1275 independent estimates of rho.
Estimated global rho of 0.11
Expanding counts from 28 clusters to 11587 cells.
[1] "3 is over"
Normalizing layer: counts
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Finding variable features for layer counts
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
|==============================================================| 100%
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 10921
Number of edges: 423789
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9217
Number of communities: 23
Elapsed time: 0 seconds
17:05:41 UMAP embedding parameters a = 0.9922 b = 1.112
17:05:41 Read 10921 rows and found 30 numeric columns
17:05:41 Using Annoy for neighbor search, n_neighbors = 30
17:05:41 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:05:42 Writing NN index file to temp file C:\Users\lenovo\AppData\Local\Temp\RtmpSkA7CH\file24086b4f2bfb
17:05:42 Searching Annoy index using 1 thread, search_k = 3000
17:05:43 Annoy recall = 100%
17:05:44 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:05:46 Initializing from normalized Laplacian + noise (using RSpectra)
17:05:46 Commencing optimization for 200 epochs, with 486162 positive edges
17:05:46 Using rng type: pcg
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:05:54 Optimization finished
[1] "./data/zipctl_2/ SOUPX Finish!"
1736 genes passed tf-idf cut-off and 292 soup quantile filter. Taking the top 100.
Using 1079 independent estimates of rho.
Estimated global rho of 0.13
Expanding counts from 23 clusters to 10921 cells.
[1] "4 is over"
警告信息:
1: In sparseMatrix(i = out@i[w] + 1, j = out@j[w] + 1, x = out@x[w], :
'giveCsparse' is deprecated; setting repr="T" for you
2: In `[<-`(`*tmp*`, i, value = out) :
implicit list embedding of S4 objects is deprecated
3: In sparseMatrix(i = out@i[w] + 1, j = out@j[w] + 1, x = out@x[w], :
'giveCsparse' is deprecated; setting repr="T" for you
4: In `[<-`(`*tmp*`, i, value = out) :
implicit list embedding of S4 objects is deprecated
5: In sparseMatrix(i = out@i[w] + 1, j = out@j[w] + 1, x = out@x[w], :
'giveCsparse' is deprecated; setting repr="T" for you
6: In `[<-`(`*tmp*`, i, value = out) :
implicit list embedding of S4 objects is deprecated
7: In sparseMatrix(i = out@i[w] + 1, j = out@j[w] + 1, x = out@x[w], :
'giveCsparse' is deprecated; setting repr="T" for you
8: In `[<-`(`*tmp*`, i, value = out) :
implicit list embedding of S4 objects is deprecated
> names(Soupx_list) = c('shNELL1_1', 'shNELL1_2', 'zipctl_1', 'zipctl_2')
> sample1 <- Soupx_list[['shNELL1_1']]
> sample2 <- Soupx_list[['shNELL1_2']]
> sample3 <- Soupx_list[['zipctl_1']]
> sample4 <- Soupx_list[['zipctl_2']]
> ## doubletFinder ####
> # shNELL1_1
> seurat_obj1 <- CreateSeuratObject(counts = sample1,project = 'shNELL1_1')
> seurat_obj1[["percent.mt"]] <- PercentageFeatureSet(seurat_obj1, pattern = "^mt-")
> VlnPlot(seurat_obj1, features = "nFeature_RNA", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
警告信息:
1: The `slot` argument of `FetchData()` is deprecated as of SeuratObject
5.0.0.
ℹ Please use the `layer` argument instead.
ℹ The deprecated feature was likely used in the Seurat package.
Please report the issue at
<https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
2: `PackageCheck()` was deprecated in SeuratObject 5.0.0.
ℹ Please use `rlang::check_installed()` instead.
ℹ The deprecated feature was likely used in the Seurat package.
Please report the issue at
<https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
> VlnPlot(seurat_obj1, features = "nCount_RNA", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
> VlnPlot(seurat_obj1, features = "percent.mt", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
> seurat_obj1.qc <- subset(seurat_obj1, subset = nFeature_RNA > 500 & nFeature_RNA < 6000 & nCount_RNA > 1000 & nCount_RNA < quantile(seurat_obj1$nCount_RNA,0.97) & percent.mt < 5)
> VlnPlot(seurat_obj1.qc, features = "nFeature_RNA", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
> VlnPlot(seurat_obj1.qc, features = "nCount_RNA", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
> VlnPlot(seurat_obj1.qc, features = "percent.mt", pt.size = 0, group.by = 'orig.ident') + NoLegend()
警告: Default search for "data" layer in "RNA" assay yielded no results; utilizing "counts" layer instead.
> dim(seurat_obj1.qc)
[1] 24879 7830
> seurat_obj1 <- SCTransform(seurat_obj1.qc, assay = "RNA", vars.to.regress = "percent.mt",vst.flavor = "v1")
Running SCTransform on assay: RNA
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 19169 by 7830
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
Found 112 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 19169 genes
Computing corrected count matrix for 19169 genes
Calculating gene attributes
Wall clock passed: Time difference of 59.51584 secs
Determine variable features
Regressing out percent.mt
|==============================================================| 100%
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
警告信息:
1: The `slot` argument of `GetAssayData()` is deprecated as of
SeuratObject 5.0.0.
ℹ Please use the `layer` argument instead.
ℹ The deprecated feature was likely used in the Seurat package.
Please report the issue at
<https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
2: The `slot` argument of `SetAssayData()` is deprecated as of
SeuratObject 5.0.0.
ℹ Please use the `layer` argument instead.
ℹ The deprecated feature was likely used in the Seurat package.
Please report the issue at
<https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
> seurat_obj1 <- ScaleData(seurat_obj1, features = VariableFeatures(seurat_obj1))
Centering and scaling data matrix
|==============================================================| 100%
> seurat_obj1 <- RunPCA(seurat_obj1, features = VariableFeatures(seurat_obj1))
PC_ 1
Positive: Tmsb4x, Fau, Tyrobp, Fcer1g, Btg1, Cd52, Actb, Ccl6, Ctss, Lgals3
Lyz2, Alox5ap, Rpl37a, Rac2, Rpl17, Rpl23, Cybb, Ftl1-ps1, Spi1, Gpx1
Selplg, Rpsa, Rps16, Stk17b, Rpl13a, Rpl39, Mpeg1, Rpl32, Rplp1, Tmsb10
Negative: Sparc, Nfib, Sptbn1, Cavin2, Fermt2, Epas1, Col4a1, Tmem100, Slc43a3, Igfbp7
Pakap, Crip2, Cldn5, Adgrf5, Ramp2, Cdh5, Tspan7, Bmpr2, Calcrl, Aqp1
Egfl7, Eng, Tcf4, Ptprb, Dlc1, Id3, Ly6c1, Ly6a, Clic5, Timp3
PC_ 2
Positive: Col1a2, Gsn, Serping1, Mgp, Sod3, Ogn, Col3a1, Pcolce2, Inmt, Rbp1
Rarres2, Loxl1, Bgn, Col1a1, Prelp, Gpc3, Pdgfra, Igfbp6, Mfap4, Cp
Adh1, Crispld2, Col6a2, Col6a1, Fhl1, Cdh11, C1s1, Itga8, Pcolce, Fxyd1
Negative: Calcrl, Cldn5, Tspan7, Cdh5, Adgrf5, Epas1, Egfl7, Tmem100, Hpgd, Ptprb
Ehd4, Pecam1, Ctla2a, Podxl, Icam2, Cd36, Ramp2, Clec1a, Ly6c1, Clic5
Slco2a1, Ly6a, Flt1, Gpihbp1, Eng, Ace, Aqp1, Cd93, S1pr1, Adgrl4
PC_ 3
Positive: Sec14l3, Tmem212, Dynlrb2, Fam183b, Ccdc153, Foxj1, 1700016K19Rik, Cbr2, Cyp2f2, 1110017D15Rik
Ccdc113, Cldn3, 1700007K13Rik, Riiad1, Rsph1, 3300002A11Rik, Cfap126, Cyp2s1, Arhgdig, Tekt1
Tspan1, Hydin, Pifo, Fhad1, Dnah6, Ak7, 1700024G13Rik, Cdhr3, Sntn, Wfdc2
Negative: Mgp, Col1a2, Serping1, Pcolce2, Ogn, Bgn, Inmt, Sod3, Col3a1, Col1a1
Rbp1, Sparcl1, Loxl1, Fmo2, Prelp, Adh1, Pdgfra, Ltbp4, C1s1, Mfap4
Gpc3, Mfap5, Col6a1, Olfml3, Col6a2, Cfh, Crispld2, Cdh11, Fxyd1, Nbl1
PC_ 4
Positive: Ccl6, Msrb1, Tnfaip2, Cebpb, Gda, Alox5ap, Tyrobp, Ncf2, Spi1, Slpi
Ftl1-ps1, Lyz2, Fth1, Wfdc21, Il1b, S100a8, S100a9, Cxcl2, Csf3r, Clec4d
Fos, Cxcr2, S100a11, Il1r2, Pirb, Grik3, Lilr4b, Grina, Cd9, Retnlg
Negative: Ms4a4b, Rpl13a, Rpl12, Ptprcap, Trbc2, Rps24, Rps15a, Gm2682, Rps21, Rpl32
Skap1, Rps7, Rpsa, Cd3d, Rps14, Rps8, Rpl34, Gimap6, Rps27, Rps16
Rps29, Cd3e, Lat, Trac, Rps20, Rpl17, Rplp1, Cd3g, Ets1, Tmsb10
PC_ 5
Positive: Chil3, Atp6v0d2, Ctss, Ear2, Mrc1, Plet1, Lpl, Abcg1, Krt79, Ear1
Cd9, Ctsz, Ctsd, Lyz2, Mpeg1, Trf, Lgals3, Cybb, Plin2, F10
S100a1, Krt19, Fabp1, AU020206, Cd68, Ctsk, Psap, Ftl1-ps1, Cstb, Marco
Negative: S100a9, S100a8, Il1b, Srgn, Cxcr2, Il1r2, Csf3r, Hdc, Mxd1, Retnlg
Hp, Pglyrp1, Clec4d, Mir142hg, Lmnb1, Cd52, Malat1, Sorl1, Gm16894, Mmp9
Ifitm1, Il1f9, H2-Q10, G0s2, Slfn1, Dennd4a, Ripor2, Sell, Ifitm2, Acod1
> print(DimPlot(seurat_obj1, reduction = "pca"))
> seurat_obj1 <- FindNeighbors(seurat_obj1, dims = 1:20)
Computing nearest neighbor graph
Computing SNN
> seurat_obj1 <- FindClusters(seurat_obj1,
+ resolution = 0.6,
+ method = 'igraph',
+ verbose = T)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 7830
Number of edges: 252001
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9329
Number of communities: 23
Elapsed time: 0 seconds
警告信息:
The `method` argument of `FindClusters()` is deprecated as of Seurat
5.2.0.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning
was generated.
> seurat_obj1 <- RunUMAP(seurat_obj1, dims = 1:20)
17:16:19 UMAP embedding parameters a = 0.9922 b = 1.112
17:16:19 Read 7830 rows and found 20 numeric columns
17:16:19 Using Annoy for neighbor search, n_neighbors = 30
17:16:19 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:16:19 Writing NN index file to temp file C:\Users\lenovo\AppData\Local\Temp\RtmpSkA7CH\file24083a2c25ec
17:16:19 Searching Annoy index using 1 thread, search_k = 3000
17:16:21 Annoy recall = 100%
17:16:22 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
17:16:23 Initializing from normalized Laplacian + noise (using RSpectra)
17:16:23 Commencing optimization for 500 epochs, with 325920 positive edges
17:16:23 Using rng type: pcg
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
17:16:37 Optimization finished
> DimPlot(seurat_obj1, reduction = "umap", group.by = "seurat_clusters")
> sweep.res.list <- paramSweep(seurat_obj1, PCs = 1:20, sct = TRUE)
[1] "Creating artificial doublets for pN = 5%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 19406 by 8242
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 67 estimated thetas smaller than 1e-07 - will be set to 1e-07
Found 1 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 19406 genes
Computing corrected count matrix for 19406 genes
Calculating gene attributes
Wall clock passed: Time difference of 40.72067 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: S100a9, S100a8, Il1b, Retnlg, Tyrobp, Srgn, Cxcl2, Ifitm1, Il1r2, Cxcr2
Hdc, Csf3r, Ccl6, Hp, S100a6, S100a11, Wfdc17, Lyz2, G0s2, Mxd1
Clec4d, Pglyrp1, Slpi, Stfa2l1, Fth1, Junb, Lmnb1, Msrb1, Wfdc21, Grina
Negative: Mgp, Epas1, Ly6a, Sparc, Ramp2, Ptprb, Tmem100, Calcrl, Ly6c1, Inmt
Ctla2a, Igfbp7, Cldn5, Cavin2, Hpgd, Gsn, Tspan7, Aqp1, Tm4sf1, Cdh5
Adgrf5, Sptbn1, Bgn, Cd74, Gpihbp1, Ehd4, Egfl7, Cd36, Pltp, Jun
PC_ 2
Positive: S100a9, S100a8, Il1b, Epas1, Mgp, Ly6a, Sparc, Retnlg, Calcrl, Ramp2
Ptprb, Tmem100, Ly6c1, Inmt, Igfbp7, Cldn5, Malat1, Ifitm2, Cavin2, Ctla2a
Gsn, Hpgd, Ifitm1, Tspan7, Aqp1, Adgrf5, Il1r2, Cdh5, Ifitm3, Cxcr2
Negative: Cd74, Ccl5, H2-Aa, H2-Ab1, H2-Eb1, Rps8, Lyz2, Rps24, Rplp1, Rpl13a
Rps27, Tpt1, Rps29, Gzma, Rpl39, Rps16, Rpl37a, Rpl34, Rps21, Rpl23
Rps20, Rpl17, Rpl12, Fau, Rpl41, Rpl32, Rps12, Rps15a, Rplp2, Rps14
PC_ 3
Positive: Lyz2, Mgp, Chil3, Ccl6, Inmt, Gsn, Fth1, Ftl1-ps1, Ctss, Ear2
Bgn, Ctsd, Col1a2, Lgals3, Serping1, Fmo2, Apoe, Pcolce2, Npnt, Ogn
Cd9, Dcn, Ear1, Sod3, Abcg1, Ltbp4, Col3a1, Gpx3, Limch1, Atp6v0d2
Negative: Cd74, Ccl5, H2-Ab1, H2-Aa, S100a9, H2-Eb1, S100a8, Gzma, Epas1, Il1b
Ly6a, Malat1, Cd52, Ctla2a, Ramp2, Ptprb, Calcrl, Tmem100, Srgn, Ly6c1
Rps27, Cldn5, Hpgd, Tspan7, Rps24, Retnlg, Rpl13a, Nkg7, Igkc, Cavin2
PC_ 4
Positive: Mgp, Inmt, Gsn, Bgn, Col1a2, Ccl5, Serping1, Fmo2, Pcolce2, Npnt
Ogn, Sparc, Igfbp7, Dcn, S100a9, Ltbp4, Sod3, Cst3, Gpx3, Igfbp6
Limch1, Col3a1, S100a8, S100a6, Sparcl1, Eln, Apoe, Gzma, Col1a1, Il1b
Negative: Lyz2, Chil3, Ccl6, Epas1, Ftl1-ps1, Ctss, Ear2, Ctsd, Ly6a, Calcrl
Ramp2, Ptprb, Tmem100, Cd9, Ly6c1, Lgals3, Cldn5, Ctla2a, Hpgd, Abcg1
Tspan7, Ear1, Cd36, Cmss1, Lpl, Adgrf5, Atp6v0d2, Cdh5, Cybb, Cxcl2
PC_ 5
Positive: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Gm2682, Ly6c2, Trbc2, Lgals1
Klra4, Prf1, Gzmb, Ptprcap, Hcst, Trbc1, Klrk1, Gimap6, Skap1, Klre1
Il2rb, Rac2, Mir142hg, Tmsb10, Vps37b, Txk, Ets1, Cd3d, Klrd1, Gm2245
Negative: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Apoe, Mgl2, Il1b, Mt1, C1qa
Igkc, Crip1, C1qb, Ifitm3, H2-DMa, C1qc, Plbd1, Psap, Egr1, Ccl17
Fth1, Ifi30, Fos, H2-DMb1, Mgp, Gpx1, Aif1, Gm2a, Ifitm2, Zfp36
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
[1] "Creating artificial doublets for pN = 10%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 19628 by 8700
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 83 estimated thetas smaller than 1e-07 - will be set to 1e-07
Second step: Get residuals using fitted parameters for 19628 genes
Computing corrected count matrix for 19628 genes
Calculating gene attributes
Wall clock passed: Time difference of 42.42966 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: Mgp, Epas1, Ly6a, Sparc, Cd74, Inmt, Ramp2, Ptprb, Tmem100, Ly6c1
Igfbp7, Ctla2a, Calcrl, Gsn, Cldn5, Cavin2, Hpgd, H2-Ab1, H2-Aa, Tspan7
Bgn, Aqp1, H2-Eb1, Sptbn1, Tm4sf1, Cdh5, Adgrf5, Rps8, Gpihbp1, Ehd4
Negative: S100a9, S100a8, Il1b, Retnlg, Tyrobp, Srgn, Cxcl2, Ifitm1, Il1r2, Cxcr2
Hdc, Csf3r, S100a11, Hp, S100a6, Ccl6, Wfdc17, G0s2, Mxd1, Clec4d
Lyz2, Pglyrp1, Stfa2l1, Ifitm2, Slpi, Junb, Fth1, Lmnb1, Msrb1, Wfdc21
PC_ 2
Positive: Cd74, Ccl5, Lyz2, H2-Aa, H2-Ab1, H2-Eb1, Rps24, Rps8, Rps27, Rplp1
Rpl13a, Tpt1, Rps29, Fau, Gzma, Rps16, Rpl39, Rpl37a, Rpl34, Rpl17
Rpl23, Rpl41, Rpl12, Rps20, Rps21, Rpl32, Rps12, Rplp2, Rps15a, Rps28
Negative: Mgp, Epas1, S100a9, Ly6a, Sparc, S100a8, Inmt, Calcrl, Ramp2, Ptprb
Tmem100, Igfbp7, Ly6c1, Gsn, Il1b, Cldn5, Cavin2, Ctla2a, Hpgd, Malat1
Tspan7, Aqp1, Sptbn1, Adgrf5, Cdh5, Bgn, Ifitm2, Tm4sf1, Ifitm3, Jun
PC_ 3
Positive: Ccl5, Cd74, S100a9, S100a8, Il1b, Gzma, H2-Ab1, H2-Aa, H2-Eb1, Malat1
Cd52, Srgn, Rps27, Cst3, Retnlg, Rps24, Rpl13a, Tpt1, Nkg7, Rps29
Igkc, Rpl12, Ly6a, Ifitm1, S100a6, Mir142hg, Fau, Ms4a4b, Rpl34, Rps21
Negative: Lyz2, Chil3, Ccl6, Ftl1-ps1, Fth1, Ctss, Ear2, Ctsd, Lgals3, Cd9
Ear1, Abcg1, Atp6v0d2, Cybb, Lpl, Psap, Cmss1, Grik3, Mpeg1, Mrc1
Cxcl2, Krt79, Gm42418, Ctsz, Plet1, Trf, Marco, Plin2, Fabp5, Fabp1
PC_ 4
Positive: Epas1, Ly6a, Calcrl, Ramp2, Ptprb, Tmem100, Ly6c1, Ctla2a, Cldn5, Lyz2
Hpgd, Tspan7, Cd74, Adgrf5, Cdh5, Cd36, Cavin2, Aqp1, Chil3, Ehd4
Gpihbp1, Ace, Tm4sf1, Egfl7, Clic5, Pecam1, Bmpr2, Cd93, Sema3c, Icam2
Negative: Mgp, Inmt, Gsn, Bgn, Col1a2, Serping1, Fmo2, Pcolce2, Dcn, Npnt
Ogn, Igfbp7, Ltbp4, Sod3, Igfbp6, Col3a1, Sparc, Gpx3, Limch1, Apoe
Sparcl1, Col1a1, Eln, Prelp, Timp3, Mfap4, Dpt, S100a6, Macf1, Rbp1
PC_ 5
Positive: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Gm2682, Ly6c2, Trbc2, Lgals1
Klra4, Gzmb, Prf1, Ptprcap, Hcst, Trbc1, Klrk1, Gimap6, Skap1, Klre1
Tmsb10, Rpl13a, Il2rb, Rac2, Mir142hg, Vps37b, Txk, Cd3d, Ets1, Klrd1
Negative: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Apoe, Mgl2, Il1b, C1qa, Mt1
Crip1, C1qb, Psap, Ifitm3, C1qc, Fth1, Igkc, H2-DMa, Egr1, Plbd1
Ccl17, Mgp, Ifi30, Fos, H2-DMb1, Gpx1, Ifitm2, Aif1, Gm2a, Zfp36
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
[1] "Creating artificial doublets for pN = 15%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 19846 by 9212
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 77 estimated thetas smaller than 1e-07 - will be set to 1e-07
Second step: Get residuals using fitted parameters for 19846 genes
Computing corrected count matrix for 19846 genes
Calculating gene attributes
Wall clock passed: Time difference of 43.96074 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: S100a9, S100a8, Il1b, Retnlg, Tyrobp, Srgn, Ifitm1, Cxcl2, Il1r2, Cxcr2
Hdc, Csf3r, Hp, S100a6, S100a11, Wfdc17, G0s2, Ccl6, Mxd1, Clec4d
Pglyrp1, Ifitm2, Stfa2l1, Junb, Lmnb1, Slpi, Lyz2, Grina, Msrb1, Wfdc21
Negative: Mgp, Epas1, Ly6a, Sparc, Cd74, Inmt, Ramp2, Igfbp7, Ptprb, Tmem100
Ly6c1, Calcrl, Ctla2a, Gsn, Cldn5, Cavin2, Hpgd, H2-Ab1, H2-Aa, Bgn
Tspan7, Aqp1, H2-Eb1, Tm4sf1, Sptbn1, Cdh5, Adgrf5, Col1a2, Rps8, Gpihbp1
PC_ 2
Positive: Mgp, Epas1, Sparc, S100a9, Ly6a, S100a8, Inmt, Gsn, Igfbp7, Ramp2
Calcrl, Ptprb, Tmem100, Ly6c1, Il1b, Cldn5, Cavin2, Ctla2a, Hpgd, Malat1
Bgn, Tspan7, Aqp1, Sptbn1, Adgrf5, Cdh5, Ifitm3, Ifitm2, Tm4sf1, Col1a2
Negative: Cd74, Ccl5, Lyz2, H2-Aa, H2-Ab1, H2-Eb1, Rps24, Rps8, Rps27, Rplp1
Rpl13a, Gzma, Rps29, Tpt1, Fau, Rpl39, Rps16, Rpl37a, Rpl17, Rpl34
Rpl23, Rpl12, Rpl41, Rpl32, Rps20, Chil3, Rps21, Tmsb4x, Rps28, Rps12
PC_ 3
Positive: Cd74, Ccl5, S100a9, S100a8, H2-Ab1, H2-Aa, H2-Eb1, Gzma, Il1b, Malat1
Cd52, Rps27, Srgn, Ly6a, Epas1, Cst3, Retnlg, Rps24, Rpl13a, Ctla2a
Nkg7, Igkc, Tmem100, Ramp2, Ptprb, Calcrl, Ly6c1, Rps29, Tpt1, Rpl12
Negative: Lyz2, Chil3, Ccl6, Ftl1-ps1, Fth1, Ctss, Ear2, Mgp, Ctsd, Lgals3
Cd9, Inmt, Ear1, Abcg1, Gsn, Atp6v0d2, Cybb, Lpl, Grik3, Cxcl2
Psap, Cmss1, Mpeg1, Mrc1, Krt79, Gm42418, Ctsz, Plet1, Marco, Cd63
PC_ 4
Positive: Mgp, Inmt, Gsn, Bgn, Col1a2, Serping1, Fmo2, Pcolce2, Dcn, Npnt
Ogn, Igfbp7, Ltbp4, Col3a1, Sod3, Igfbp6, Limch1, Sparc, Gpx3, Apoe
Cst3, Col1a1, Sparcl1, Eln, Timp3, Prelp, S100a6, Mfap4, Ccl5, Dpt
Negative: Lyz2, Epas1, Ly6a, Chil3, Calcrl, Ptprb, Ramp2, Tmem100, Ly6c1, Ctla2a
Cldn5, Ccl6, Hpgd, Tspan7, Adgrf5, Cdh5, Cd36, Cavin2, Aqp1, Ehd4
Gpihbp1, Ace, Tm4sf1, Egfl7, Clic5, Bmpr2, Cd93, Pecam1, Ctss, Sema3c
PC_ 5
Positive: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Gm2682, Ly6c2, Trbc2, Lgals1
Klra4, Ptprcap, Prf1, Gzmb, Hcst, Trbc1, Klrk1, Gimap6, Skap1, Klre1
Tmsb10, Rpl13a, Rac2, Il2rb, Mir142hg, Vps37b, Txk, Cd3d, Ets1, Klrd1
Negative: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Apoe, Mgl2, Mt1, Il1b, C1qa
Crip1, Psap, Ifitm3, C1qb, H2-DMa, C1qc, Fth1, Plbd1, Ccl17, Igkc
Egr1, Ifi30, Fos, H2-DMb1, Mgp, Gpx1, Lyz2, Gm2a, Aif1, Zfp36
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
[1] "Creating artificial doublets for pN = 20%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 20096 by 9788
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 96 estimated thetas smaller than 1e-07 - will be set to 1e-07
Found 2 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 20096 genes
Computing corrected count matrix for 20096 genes
Calculating gene attributes
Wall clock passed: Time difference of 48.67748 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: Cd74, Mgp, H2-Ab1, H2-Aa, Epas1, Sparc, Ly6a, Inmt, H2-Eb1, Rps8
Ccl5, Gsn, Igfbp7, Rps24, Cst3, Tpt1, Ramp2, Rplp1, Ptprb, Rpl13a
Tmem100, Ctla2a, Ly6c1, Rps21, Rps29, Calcrl, Rps20, Bgn, Rpl12, Rps27
Negative: S100a9, S100a8, Il1b, Retnlg, Tyrobp, Srgn, Ifitm1, Cxcl2, Il1r2, Cxcr2
Hdc, Csf3r, S100a6, Hp, S100a11, G0s2, Wfdc17, Clec4d, Mxd1, Ifitm2
Ccl6, Stfa2l1, Pglyrp1, Junb, Lmnb1, Slpi, Grina, Gm16894, Msrb1, Wfdc21
PC_ 2
Positive: Mgp, Inmt, Sparc, Gsn, Epas1, Ly6a, Igfbp7, Ramp2, Bgn, Ptprb
Calcrl, Tmem100, Ly6c1, Col1a2, Cldn5, Cavin2, Ctla2a, Fmo2, Hpgd, S100a9
Malat1, Tspan7, Aqp1, Sptbn1, Jun, Timp3, S100a8, Ifitm3, Serping1, Adgrf5
Negative: Cd74, Ccl5, Lyz2, H2-Aa, H2-Ab1, H2-Eb1, Rps27, Rps24, Gzma, Rps8
Rpl13a, Fau, Rplp1, Rps29, Tmsb4x, Tpt1, Chil3, Rpl37a, Rpl39, Rps16
Rpl41, Rpl17, Rpl34, Rpl23, Rpl32, Cd52, Rpl12, Rps11, Rps20, Rplp2
PC_ 3
Positive: Lyz2, Chil3, Ccl6, Ftl1-ps1, Ctss, Ear2, Fth1, Ctsd, Lgals3, Cd9
Abcg1, Ear1, Cmss1, Cybb, Atp6v0d2, Lpl, Cxcl2, Grik3, Mpeg1, Psap
Mrc1, Gm42418, Krt79, Ctsz, Plet1, Trf, Marco, Fabp5, Plin2, Fabp1
Negative: Cd74, Ccl5, S100a9, H2-Ab1, H2-Aa, S100a8, H2-Eb1, Cst3, Il1b, Gzma
Cd52, Malat1, Rps27, Srgn, Mgp, Rps24, Rpl13a, Retnlg, Tpt1, S100a6
Igkc, Rps29, Rpl12, Rps21, Rpl34, Rpl17, Rps16, Nkg7, Fau, Rps15a
PC_ 4
Positive: Mgp, Inmt, Gsn, Bgn, Col1a2, Serping1, Dcn, Pcolce2, Fmo2, Ogn
Npnt, Apoe, Col3a1, Igfbp6, Sod3, Ltbp4, Gpx3, Limch1, Col1a1, Eln
Sparcl1, Igfbp7, Fth1, Prelp, Sparc, Mfap4, Dpt, Timp3, Cst3, Rbp1
Negative: Epas1, Ly6a, Calcrl, Ptprb, Ramp2, Tmem100, Ly6c1, Ctla2a, Cldn5, Hpgd
Tspan7, Cavin2, Adgrf5, Cdh5, Aqp1, Gpihbp1, Cd36, Cd74, Tm4sf1, Ehd4
Ace, Egfl7, Clic5, Pecam1, Icam2, Eng, Sema3c, Bmpr2, Podxl, Slco2a1
PC_ 5
Positive: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Apoe, Mgl2, Lyz2, Mt1, C1qa
Fth1, Psap, C1qb, Ifitm3, C1qc, Il1b, Crip1, Ccl17, H2-DMa, Egr1
Plbd1, Fos, Ftl1-ps1, Ifi30, Ctss, Gpx1, H2-DMb1, Cxcl2, Igkc, Mgp
Negative: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Gm2682, Ly6c2, Trbc2, Lgals1
Gzmb, Prf1, Ptprcap, Klra4, Hcst, Trbc1, Rpl13a, Klrk1, Tmsb10, Skap1
Gimap6, Mir142hg, Klre1, Rac2, Il2rb, Vps37b, Cd52, Cd3d, Hmgb2, Txk
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
[1] "Creating artificial doublets for pN = 25%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 20322 by 10440
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 88 estimated thetas smaller than 1e-07 - will be set to 1e-07
Found 2 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 20322 genes
Computing corrected count matrix for 20322 genes
Calculating gene attributes
Wall clock passed: Time difference of 47.64712 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: Cd74, Mgp, H2-Ab1, H2-Aa, H2-Eb1, Rps8, Sparc, Inmt, Cst3, Ccl5
Epas1, Ly6a, Rps24, Tpt1, Rplp1, Rpl13a, Rps29, Rps21, Gsn, Rps20
Igfbp7, Rps27, Rpl12, Rps12, Rpl34, Rpl39, Ctla2a, Ramp2, Rps14, Rpl41
Negative: S100a9, S100a8, Il1b, Retnlg, Srgn, Tyrobp, Ifitm1, Cxcl2, Il1r2, Cxcr2
Csf3r, Hdc, Hp, S100a6, S100a11, G0s2, Wfdc17, Mxd1, Clec4d, Ifitm2
Stfa2l1, Pglyrp1, Ccl6, Lmnb1, Junb, Grina, Slpi, Msrb1, Gm16894, Wfdc21
PC_ 2
Positive: Mgp, Inmt, Gsn, Sparc, Epas1, Igfbp7, Ly6a, Bgn, Ramp2, Tmem100
Col1a2, Ptprb, Ly6c1, Calcrl, Fmo2, Cavin2, Cldn5, Ctla2a, Timp3, Hpgd
Jun, Serping1, Sptbn1, Ltbp4, Tspan7, Dcn, Ifitm3, Aqp1, Pcolce2, Npnt
Negative: Cd74, H2-Aa, H2-Ab1, Ccl5, H2-Eb1, Lyz2, Gzma, Fau, Tmsb4x, Rps27
Rps24, Rps8, Rpl13a, Rplp1, Rps29, Cst3, Tpt1, Cd52, Chil3, Rpl17
Rpl37a, Rpl39, Rpl41, Rps16, Rpl34, Rps11, Rpl23, Rpl32, Ccl6, Rpl12
PC_ 3
Positive: Lyz2, Chil3, Ccl6, Ftl1-ps1, Ear2, Ctss, Ctsd, Fth1, Lgals3, Cd9
Abcg1, Ear1, Cxcl2, Cybb, Cmss1, Atp6v0d2, Lpl, Grik3, Mpeg1, Mrc1
Gm42418, Krt79, Psap, Ctsz, Plet1, Slpi, Marco, Trf, Plin2, Fabp1
Negative: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, S100a9, Ccl5, S100a8, Il1b, Mgp
Gzma, Cd52, Malat1, Rps27, Srgn, Inmt, Rps24, Tpt1, S100a6, Retnlg
Rpl13a, Igkc, Gsn, Rps29, Rps21, Rpl34, Rpl17, Rpl12, Fau, Rps16
PC_ 4
Positive: Epas1, Ly6a, Ptprb, Ramp2, Tmem100, Calcrl, Ly6c1, Ctla2a, Cldn5, Hpgd
Tspan7, Cavin2, Adgrf5, Cdh5, Aqp1, Cd74, Tm4sf1, Cd36, Gpihbp1, Ehd4
Ace, Egfl7, Clic5, Pecam1, Icam2, Bmpr2, Eng, Sema3c, Cd93, Slco2a1
Negative: Mgp, Inmt, Gsn, Bgn, Col1a2, Dcn, Serping1, Fmo2, Pcolce2, Apoe
Ogn, Npnt, Col3a1, Sod3, Igfbp6, Ltbp4, Gpx3, Limch1, Fth1, Col1a1
Sparcl1, Eln, Igfbp7, Prelp, Mfap4, Timp3, Dpt, S100a6, Lyz2, Rbp1
PC_ 5
Positive: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Lyz2, Apoe, Fth1, Mt1, Ccl6
Ftl1-ps1, Psap, Mgl2, Chil3, Ctss, Mgp, C1qa, Cxcl2, Egr1, Ccl17
Fos, C1qc, C1qb, Ifitm3, Ifi30, Gpx1, Crip1, Plbd1, Il1b, Inmt
Negative: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Ly6c2, Gm2682, Trbc2, Ptprcap
Rpl13a, Lgals1, Gzmb, Prf1, Klra4, Cd52, Hcst, Trbc1, Rps27, Gimap6
Tmsb10, Mir142hg, Rac2, Skap1, Klrk1, Hmgb2, Ptpn18, Klre1, Id2, Vps37b
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
[1] "Creating artificial doublets for pN = 30%"
[1] "Creating Seurat object..."
[1] "Running SCTransform..."
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 20542 by 11186
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
There are 87 estimated thetas smaller than 1e-07 - will be set to 1e-07
Found 1 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 20542 genes
Computing corrected count matrix for 20542 genes
Calculating gene attributes
Wall clock passed: Time difference of 51.14941 secs
Determine variable features
Centering data matrix
|==============================================================| 100%
Place corrected count matrix in counts slot
Set default assay to SCT
[1] "Running PCA..."
PC_ 1
Positive: S100a9, S100a8, Il1b, Retnlg, Srgn, Ifitm1, Tyrobp, Il1r2, Cxcl2, Cxcr2
Csf3r, Hdc, Hp, S100a6, S100a11, G0s2, Wfdc17, Mxd1, Clec4d, Ifitm2
Stfa2l1, Pglyrp1, Lmnb1, Junb, Grina, Slpi, Ccl6, Msrb1, Gm16894, Wfdc21
Negative: Mgp, Cd74, H2-Ab1, H2-Aa, H2-Eb1, Inmt, Sparc, Epas1, Ly6a, Cst3
Rps8, Gsn, Igfbp7, Rplp1, Rps24, Tpt1, Rpl13a, Rps29, Ramp2, Ccl5
Ctla2a, Rps21, Bgn, Ptprb, Ly6c1, Rps20, Tmem100, Calcrl, Rps12, Rpl12
PC_ 2
Positive: Mgp, Inmt, Gsn, Sparc, Igfbp7, Bgn, Epas1, Col1a2, Ly6a, Fmo2
Dcn, Serping1, Ramp2, Ly6c1, Ptprb, Tmem100, Timp3, Calcrl, Ltbp4, Pcolce2
S100a9, Npnt, Ogn, Cavin2, Limch1, S100a8, Gpx3, Cldn5, Sparcl1, Jun
Negative: Cd74, Lyz2, H2-Ab1, H2-Aa, H2-Eb1, Ccl5, Chil3, Ccl6, Tmsb4x, Fau
Gzma, Rps8, Rps27, Rps24, Rplp1, Rpl13a, Rps29, Cst3, Tpt1, Ctss
Rpl41, Cd52, Rpl37a, Rps11, Ftl1-ps1, Rpl39, Tyrobp, Rpl17, Rpl23, Rpl32
PC_ 3
Positive: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Mgp, Ccl5, S100a9, S100a8, Il1b
Cd52, Inmt, Gzma, Rps27, Gsn, Rps24, Tpt1, Igkc, Rps29, Rpl13a
Srgn, S100a6, Rps21, Fau, Malat1, Rpl17, Rpl34, Rps16, Rpl12, Rps8
Negative: Lyz2, Chil3, Ccl6, Ftl1-ps1, Ear2, Ctsd, Ctss, Fth1, Lgals3, Cd9
Cmss1, Abcg1, Ear1, Atp6v0d2, Lpl, Cxcl2, Cybb, Gm42418, Grik3, Mrc1
Mpeg1, Krt79, Slpi, Ctsz, Marco, Fabp5, Fabp1, Psap, Ctsk, Tnfaip2
PC_ 4
Positive: Mgp, Inmt, Gsn, Bgn, Lyz2, Col1a2, Dcn, Fth1, Serping1, Apoe
Chil3, Pcolce2, Ogn, Fmo2, Npnt, Col3a1, Igfbp6, Ccl6, Sod3, Ltbp4
Gpx3, Limch1, Col1a1, Eln, Ftl1-ps1, Sparcl1, Prelp, Mfap4, Dpt, C3
Negative: Epas1, Ly6a, Ramp2, Calcrl, Ptprb, Tmem100, Ly6c1, Ctla2a, Cldn5, Hpgd
Tspan7, Cavin2, Aqp1, Adgrf5, Cdh5, Tm4sf1, Ehd4, Gpihbp1, Cd36, Cd74
Ace, Egfl7, Clic5, Pecam1, Bmpr2, Icam2, Eng, Sema3c, Pltp, Slco2a1
PC_ 5
Positive: Ccl5, Gzma, Nkg7, Malat1, AW112010, Ms4a4b, Ly6c2, Gm2682, Trbc2, Rpl13a
Cd52, Ptprcap, Lgals1, Rps27, Gzmb, Klra4, Prf1, Hcst, Tmsb10, Trbc1
Mir142hg, Rac2, Gimap6, Id2, Ptpn18, Skap1, Rps24, Hmgb2, Rpl12, Cd3d
Negative: Cd74, H2-Ab1, H2-Aa, H2-Eb1, Cst3, Lyz2, Chil3, Ccl6, Fth1, Ftl1-ps1
Apoe, Ctss, Mt1, Psap, Cxcl2, Mgl2, C1qa, Ear2, Egr1, Fos
Ifitm3, Gpx1, C1qc, C1qb, Ccl17, Lgals3, Cd9, Grik3, Ctsz, Alox5ap
[1] "Calculating PC distance matrix..."
[1] "Defining neighborhoods..."
[1] "Computing pANN across all pK..."
[1] "pK = 0.005..."
[1] "pK = 0.01..."
[1] "pK = 0.02..."
[1] "pK = 0.03..."
[1] "pK = 0.04..."
[1] "pK = 0.05..."
[1] "pK = 0.06..."
[1] "pK = 0.07..."
[1] "pK = 0.08..."
[1] "pK = 0.09..."
[1] "pK = 0.1..."
[1] "pK = 0.11..."
[1] "pK = 0.12..."
[1] "pK = 0.13..."
[1] "pK = 0.14..."
[1] "pK = 0.15..."
[1] "pK = 0.16..."
[1] "pK = 0.17..."
[1] "pK = 0.18..."
[1] "pK = 0.19..."
[1] "pK = 0.2..."
[1] "pK = 0.21..."
[1] "pK = 0.22..."
[1] "pK = 0.23..."
[1] "pK = 0.24..."
[1] "pK = 0.25..."
[1] "pK = 0.26..."
[1] "pK = 0.27..."
[1] "pK = 0.28..."
[1] "pK = 0.29..."
[1] "pK = 0.3..."
> sweep.stats <- summarizeSweep(sweep.res.list, GT = FALSE)
> bcmvn <- find.pK(sweep.stats)# 查找最佳 pK 值
NULL
> best_pK <- bcmvn$pK[which.max(bcmvn$BCmetric)] # 提取最佳 pK
> best_pN <- sweep.stats$pN[which.max(sweep.stats$BCreal)]# 提取最佳 pN
> best_pK <- as.numeric(as.character(best_pK))
> best_pN <- as.numeric(as.character(best_pN))
> best_pK
[1] 0.01
> best_pN
[1] 0.1
> homotypic.prop <- modelHomotypic(seurat_obj1$seurat_clusters)# 估计的同源双细胞
> nExp_poi <- round(0.075 *nrow(seurat_obj1@meta.data)) # 计算总的双细胞数量（10X文档推荐双细胞形成率为 7.5%）
> nExp_poi.adj <- round(nExp_poi*(1-homotypic.prop)) # 计算异源双细胞数量
> seurat_obj1 <- doubletFinder(seurat_obj1,
+ PCs = 1:20,
+ pN = best_pN,
+ pK = best_pK,
+ nExp = nExp_poi.adj,
+ reuse.pANN = FALSE,
+ sct = TRUE)
错误于xtfrm.data.frame(x): 无法 xtfrm 数据帧
> seurat_obj1 <- doubletFinder(seurat_obj1,
+ PCs = 1:20,
+ pN = best_pN,
+ pK = best_pK,
+ nExp = nExp_poi.adj,
+ reuse.pANN = FALSE,
+ sct = TRUE)
错误于xtfrm.data.frame(x): 无法 xtfrm 数据帧
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