mhkowalski
commented
11 months ago Hi,
Thanks for reporting this. I'm not able to reduce this on my end, could you please share a reproducible example?
Closed ShixiangWang closed 11 months ago
mhkowalski
commented
11 months ago Hi,
Thanks for reporting this. I'm not able to reduce this on my end, could you please share a reproducible example?
ShixiangWang
commented
11 months ago @mhkowalski Thanks for your response. It's strange that I also cannot reproduce it on another Mac. I'm sorry for bothering you.
> library(Seurat)
Loading required package: SeuratObject
Loading required package: sp
The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
‘SeuratObject’ was built with package ‘Matrix’ 1.6.2 but the current version is 1.6.3; it is recomended that you reinstall ‘SeuratObject’ as the ABI for
‘Matrix’ may have changed
Attaching package: ‘SeuratObject’
The following object is masked from ‘package:base’:
intersect
Warning messages:
1: package ‘Seurat’ was built under R version 4.2.3
2: package ‘SeuratObject’ was built under R version 4.2.3
> # Load the PBMC dataset
> pbmc.data <- Read10X(data.dir = "~/Downloads/filtered_gene_bc_matrices/hg19/")
> # Initialize the Seurat object with the raw (non-normalized data).
> pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
> pbmc
An object of class Seurat
13714 features across 2700 samples within 1 assay
Active assay: RNA (13714 features, 0 variable features)
1 layer present: counts
> pbmc <- SCTransform(object = pbmc)
Running SCTransform on assay: RNA
Running SCTransform on layer: counts
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
Variance stabilizing transformation of count matrix of size 12572 by 2700
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 2700 cells
Found 70 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 12572 genes
Computing corrected count matrix for 12572 genes
Calculating gene attributes
Wall clock passed: Time difference of 7.689123 secs
Determine variable features
Centering data matrix
|=======================================================================================================================================================================| 100%
Set default assay to SCT
> pbmc <- RunPCA(object = pbmc)
PC_ 1
Positive: MALAT1, RPS27A, LTB, RPS6, RPS27, CCL5, RPL13A, RPS3A, RPL3, RPS3
IL32, RPS12, RPL13, RPL21, NKG7, RPS18, RPL9, RPL23A, RPSA, CD3D
RPS15A, PTPRCAP, RPLP2, RPL30, CD3E, B2M, LDHB, IL7R, RPL27A, RPL34
Negative: FTL, LYZ, FTH1, CST3, S100A9, TYROBP, S100A8, AIF1, LST1, FCN1
LGALS1, FCER1G, LGALS2, SAT1, S100A4, CTSS, COTL1, TYMP, S100A6, IFITM3
CFD, HLA-DRA, PSAP, S100A11, GPX1, SERPINA1, OAZ1, GSTP1, CD68, NPC2
PC_ 2
Positive: NKG7, CCL5, GZMB, GNLY, GZMA, CST7, PRF1, FGFBP2, CTSW, GZMH
CCL4, B2M, SPON2, FCGR3A, CLIC3, CD247, HLA-C, GZMM, HOPX, KLRD1
ACTB, XCL2, AKR1C3, IGFBP7, TTC38, HLA-A, APMAP, S1PR5, SRGN, PRSS23
Negative: HLA-DRA, CD74, CD79A, HLA-DPB1, HLA-DQA1, HLA-DQB1, TCL1A, CD79B, HLA-DRB1, MS4A1
HLA-DPA1, RPL13, RPL13A, LINC00926, LTB, RPL18A, RPL32, HLA-DRB5, RPS18, RPS27
VPREB3, RPS2, CD37, HLA-DQA2, RPS6, RPL11, RPS12, RPS23, RPL10, HLA-DMA
PC_ 3
Positive: CD74, HLA-DRA, CD79A, HLA-DPB1, HLA-DQA1, HLA-DRB1, CD79B, HLA-DPA1, HLA-DQB1, TCL1A
MS4A1, NKG7, HLA-DRB5, GZMB, LINC00926, GNLY, HLA-DQA2, VPREB3, FGFBP2, PRF1
HLA-DMA, CST7, FCER2, CD37, GZMA, BANK1, HLA-DMB, GZMH, HVCN1, CCL5
Negative: S100A8, S100A9, LYZ, FTL, JUNB, RPS12, LDHB, IL7R, CD3D, RPS14
RPS3, RPS6, CD3E, RPL32, RPL13, NOSIP, IL32, S100A4, TPT1, S100A6
VIM, RPL10, RPL3, FOS, RPLP1, RPL11, LEF1, FCN1, RGCC, RPS18
PC_ 4
Positive: S100A8, S100A9, LYZ, LGALS2, CD14, GPX1, GSTP1, NKG7, MS4A6A, FCN1
CCL3, S100A12, FOLR3, CEBPD, GNLY, GRN, CSF3R, GZMB, RBP7, GAPDH
BLVRB, CCL5, IL8, VCAN, ID1, CST7, ALDH2, FGFBP2, NCF1, GZMA
Negative: FCGR3A, LST1, FCER1G, AIF1, IFITM3, MS4A7, IFITM2, FTH1, COTL1, RHOC
RP11-290F20.3, TIMP1, SAT1, HES4, CDKN1C, SERPINA1, CEBPB, CKB, RPS19, LRRC25
LILRA3, HMOX1, SIGLEC10, HCK, SPI1, PILRA, STXBP2, ACTB, WARS, BID
PC_ 5
Positive: CCL5, GPX1, PPBP, PF4, SDPR, SPARC, GNG11, HIST1H2AC, CD9, TPM4
CLU, NRGN, TUBB1, GP9, TAGLN2, RGS18, RUFY1, MPP1, ACTB, TUBA4A
CA2, NCOA4, GRAP2, CTSA, PTCRA, TREML1, NGFRAP1, PGRMC1, FERMT3, RGS10
Negative: GNLY, GZMB, FGFBP2, FCGR3A, PRF1, NKG7, TYROBP, FCER1G, MALAT1, LST1
SPON2, IFITM3, AIF1, RPS6, FTL, RPL10, RPS19, RPS3A, CCL4, RPL13
RPL32, IFITM2, IGFBP7, CTSW, RPL21, CLIC3, RPL19, CD247, RPL11, KLRD1
> pbmc <- FindNeighbors(object = pbmc, dims = 1:30)
Computing nearest neighbor graph
Computing SNN
> pbmc <- FindClusters(object = pbmc)
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 2700
Number of edges: 109691
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8384
Number of communities: 13
Elapsed time: 0 seconds
> pbmc <- RunUMAP(object = pbmc, dims = 1:30)
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
10:15:50 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
10:15:51 Read 2700 rows and found 30 numeric columns
10:15:51 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by ‘BiocGenerics’
10:15:51 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:51 Writing NN index file to temp file /var/folders/gm/lw6z28md2594gcnws_38_9f40000gn/T//Rtmp9WkNHQ/file286d5085d604
10:15:51 Searching Annoy index using 1 thread, search_k = 3000
10:15:51 Annoy recall = 100%
10:15:51 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
10:15:52 Initializing from normalized Laplacian + noise (using RSpectra)
10:15:52 Commencing optimization for 500 epochs, with 112294 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
10:15:55 Optimization finished
> DimPlot(object = pbmc, reduction = "umap")
> sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 14.1.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Seurat_5.0.0 SeuratObject_5.0.0 sp_1.6-1
loaded via a namespace (and not attached):
[1] Rtsne_0.16 colorspace_2.1-0 deldir_1.0-9 ellipsis_0.3.2 ggridges_0.5.4 XVector_0.38.0
[7] GenomicRanges_1.50.2 RcppHNSW_0.5.0 rstudioapi_0.14 spatstat.data_3.0-1 farver_2.1.1 leiden_0.4.3
[13] listenv_0.9.0 ggrepel_0.9.3 RSpectra_0.16-1 fansi_1.0.4 sparseMatrixStats_1.10.0 codetools_0.2-19
[19] splines_4.2.2 R.methodsS3_1.8.2 polyclip_1.10-4 spam_2.10-0 jsonlite_1.8.4 ica_1.0-3
[25] cluster_2.1.4 png_0.1-8 R.oo_1.25.0 uwot_0.1.14 shiny_1.7.4 sctransform_0.4.1
[31] spatstat.sparse_3.0-1 compiler_4.2.2 httr_1.4.6 Matrix_1.6-3 fastmap_1.1.1 lazyeval_0.2.2
[37] cli_3.6.1 later_1.3.1 htmltools_0.5.5 tools_4.2.2 igraph_1.4.3 dotCall64_1.1-0
[43] GenomeInfoDbData_1.2.9 gtable_0.3.3 glue_1.6.2 RANN_2.6.1 reshape2_1.4.4 dplyr_1.1.2
[49] Rcpp_1.0.10 Biobase_2.58.0 scattermore_1.2 vctrs_0.6.2 spatstat.explore_3.2-1 nlme_3.1-162
[55] progressr_0.13.0 DelayedMatrixStats_1.20.0 lmtest_0.9-40 spatstat.random_3.1-5 stringr_1.5.0 globals_0.16.2
[61] mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.3 irlba_2.3.5.1 goftest_1.2-3 future_1.32.0
[67] zlibbioc_1.44.0 MASS_7.3-60 zoo_1.8-12 scales_1.2.1 MatrixGenerics_1.10.0 promises_1.2.0.1
[73] spatstat.utils_3.0-3 SummarizedExperiment_1.28.0 parallel_4.2.2 RColorBrewer_1.1-3 reticulate_1.28 pbapply_1.7-0
[79] gridExtra_2.3 ggplot2_3.4.2 stringi_1.7.12 S4Vectors_0.36.2 fastDummies_1.7.3 BiocGenerics_0.44.0
[85] GenomeInfoDb_1.34.9 bitops_1.0-7 rlang_1.1.1 pkgconfig_2.0.3 matrixStats_0.63.0 glmGamPoi_1.10.2
[91] lattice_0.21-8 ROCR_1.0-11 purrr_1.0.1 tensor_1.5 labeling_0.4.2 patchwork_1.1.2
[97] htmlwidgets_1.6.2 cowplot_1.1.1 tidyselect_1.2.0 parallelly_1.36.0 RcppAnnoy_0.0.20 plyr_1.8.8
[103] magrittr_2.0.3 R6_2.5.1 IRanges_2.32.0 generics_0.1.3 DelayedArray_0.24.0 withr_2.5.0
[109] pillar_1.9.0 fitdistrplus_1.1-11 RCurl_1.98-1.12 survival_3.5-5 abind_1.4-5 tibble_3.2.1
[115] future.apply_1.11.0 KernSmooth_2.23-21 utf8_1.2.3 spatstat.geom_3.2-1 plotly_4.10.1 grid_4.2.2
[121] data.table_1.14.8 digest_0.6.31 xtable_1.8-4 tidyr_1.3.0 httpuv_1.6.11 R.utils_2.12.2
[127] stats4_4.2.2 munsell_0.5.0 viridisLite_0.4.2
I got the following error when I learning the Seurat with doc from https://satijalab.org/seurat/articles/essential_commands
The warning generated from the commands:
Here
useNames = TRUErepeats twice. And as a beginner, I don't know why I have to set useNames.