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satijalab
Inconsistent Clusters with same R script
Hi, I'm analyzing CosMx spatial data (1000plex panel) with Seurat package version 5.0.1.
Below is the code I used to normalize and integrate two different dataset.
However, when I run the exact same code multiple times, the results are completely inconsistent. For example, DimPlots of UMAP, the number of Clusters, etc.
I've never experienced this with my single cell data or Visium before. I was using Seurat version 4, then. Recently, I have rerun the single cell/visium data with Seurat version 5, and I think I haven't experienced this issue, either.
I'm not sure if this is bug of Seurat version 5, or I did something wrong.
======================================================================= lean <- LoadNanostring('FlatFiles/Lean', fov = 'Lean') ob <- LoadNanostring('FlatFile/Ob', fov = 'Ob')
Add fov number to the metadata
library(tibble)
[email protected] <- rownames_to_column([email protected], var = 'rowname') [email protected]$fov <- [email protected]$rowname [email protected] <- column_to_rownames([email protected], var = 'rowname') [email protected]$fov <- gsub(".*_","", [email protected]$fov)
[email protected]$orig.ident <- 'lean'
[email protected] <- rownames_to_column([email protected], var = 'rowname') [email protected]$fov <- [email protected]$rowname [email protected] <- column_to_rownames([email protected], var = 'rowname') [email protected]$fov <- gsub(".*_","", [email protected]$fov)
[email protected]$orig.ident <- 'ob'
Quality Control: All the detailed metadata in the original metadata_file, such as nCount/nFeature_RNA or negprobes are all lost
filter out Cells with an average negative control count greater than 0.5 && less than 20 detected features
........
Merge
merged <- merge (lean, ob)
Normalization
merged <- SCTransform(merged, assay = "Nanostring", clip.range= c(-10, 10), verbose = FALSE)
PCA reduction
Perform PCA on the normalized data as needed before running Harmony batch correction, npcs=50 by default
Calculate PCs using variable features determined by SCTransform (3000 by default)
total.pcad<- RunPCA(merged, npcs = 50)
PERFORM INTEGRATION
harmonized.total <- RunHarmony(total.pcad, group.by.vars = "orig.ident", reduction = "pca", assay.use = "SCT", reduction.save = "harmony") harmonized.total[["Nanostring"]] <- JoinLayers(harmonized.total[["Nanostring"]]) harmonized.total <- RunUMAP(harmonized.total, reduction = "harmony", assay = "SCT", dims = 1:30) harmonized.total <- FindNeighbors(object = harmonized.total, reduction = "harmony") harmonized.total <- FindClusters(harmonized.total, resolution = 0.3)
sessionInfo() R version 4.3.1 (2023-06-16) Platform: aarch64-apple-darwin20 (64-bit) Running under: macOS Ventura 13.6.3
Matrix products: default BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Asia/Seoul tzcode source: internal
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] tibble_3.2.1 dplyr_1.1.4 patchwork_1.1.3 ggplot2_3.4.4 harmony_1.2.0 Rcpp_1.0.11 Seurat_5.0.1 SeuratObject_5.0.1 [9] sp_2.1-2
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.15.0 jsonlite_1.8.8 magrittr_2.0.3 spatstat.utils_3.0-4
[6] farver_2.1.1 zlibbioc_1.48.0 vctrs_0.6.5 ROCR_1.0-11 DelayedMatrixStats_1.24.0
[11] spatstat.explore_3.2-5 RCurl_1.98-1.13 htmltools_0.5.7 S4Arrays_1.2.0 SparseArray_1.2.2
[16] sctransform_0.4.1 parallelly_1.36.0 KernSmooth_2.23-22 htmlwidgets_1.6.4 ica_1.0-3
[21] plyr_1.8.9 plotly_4.10.3 zoo_1.8-12 igraph_1.6.0 mime_0.12
[26] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.6-4 R6_2.5.1 fastmap_1.1.1
[31] GenomeInfoDbData_1.2.11 MatrixGenerics_1.14.0 fitdistrplus_1.1-11 future_1.33.0 shiny_1.8.0
[36] digest_0.6.33 colorspace_2.1-0 S4Vectors_0.40.2 DESeq2_1.42.0 tensor_1.5
[41] RSpectra_0.16-1 irlba_2.3.5.1 GenomicRanges_1.54.1 labeling_0.4.3 progressr_0.14.0
[46] fansi_1.0.6 spatstat.sparse_3.0-3 httr_1.4.7 polyclip_1.10-6 abind_1.4-5
[51] compiler_4.3.1 withr_2.5.2 BiocParallel_1.36.0 fastDummies_1.7.3 MASS_7.3-60
[56] DelayedArray_0.28.0 tools_4.3.1 lmtest_0.9-40 httpuv_1.6.13 future.apply_1.11.0
[61] goftest_1.2-3 glmGamPoi_1.14.0 glue_1.6.2 nlme_3.1-164 promises_1.2.1
[66] grid_4.3.1 Rtsne_0.17 cluster_2.1.6 reshape2_1.4.4 generics_0.1.3
[71] gtable_0.3.4 spatstat.data_3.0-3 tidyr_1.3.0 data.table_1.14.10 utf8_1.2.4
[76] XVector_0.42.0 BiocGenerics_0.48.1 spatstat.geom_3.2-7 RcppAnnoy_0.0.21 ggrepel_0.9.4
[81] RANN_2.6.1 pillar_1.9.0 stringr_1.5.1 limma_3.58.1 spam_2.10-0
[86] RcppHNSW_0.5.0 later_1.3.2 splines_4.3.1 lattice_0.22-5 survival_3.5-7
[91] deldir_2.0-2 tidyselect_1.2.0 locfit_1.5-9.8 miniUI_0.1.1.1 pbapply_1.7-2
[96] gridExtra_2.3 IRanges_2.36.0 SummarizedExperiment_1.32.0 scattermore_1.2 RhpcBLASctl_0.23-42
[101] stats4_4.3.1 Biobase_2.62.0 statmod_1.5.0 matrixStats_1.2.0 stringi_1.8.3
[106] lazyeval_0.2.2 codetools_0.2-19 cli_3.6.2 uwot_0.1.16 xtable_1.8-4
[111] reticulate_1.34.0 munsell_0.5.0 GenomeInfoDb_1.38.2 globals_0.16.2 spatstat.random_3.2-2
[116] png_0.1-8 parallel_4.3.1 ellipsis_0.3.2 presto_1.0.0 dotCall64_1.1-1
[121] sparseMatrixStats_1.14.0 bitops_1.0-7 listenv_0.9.0 viridisLite_0.4.2 scales_1.3.0
[126] ggridges_0.5.5 leiden_0.4.3.1 purrr_1.0.2 crayon_1.5.2 rlang_1.1.2
[131] cowplot_1.1.2
Hi @hjbp0610, Can you share the objects and the entire code with me over email [email protected] for me to reproduce.
Hi,
I'm out of the office for 3 days. May I send you the data next week?
Many thanks, Hee Joo
2024년 1월 20일 (토) 오전 5:36, Saket Choudhary @.***>님이 작성:
Hi @hjbp0610 https://github.com/hjbp0610, Can you share the objects and the entire code with me over email @.*** for me to reproduce.
— Reply to this email directly, view it on GitHub https://github.com/satijalab/seurat/issues/8342#issuecomment-1901077081, or unsubscribe https://github.com/notifications/unsubscribe-auth/ASBWDWAUBG7JTCUVT6AWYNDYPLKMRAVCNFSM6AAAAABCBH5Z42VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMBRGA3TOMBYGE . You are receiving this because you were mentioned.Message ID: @.***>
CosMx_Seurat.zip https://drive.google.com/file/d/1OUVv873YRirfba1LSppS6rT3JP0jyiIe/view?usp=drive_web Hi,
I've attached the script file and the original flatfiles of the dataset.
Thank you very much for your nice package and help.
Best wishes, Hee Joo
On Sat, Jan 20, 2024 at 7:25 AM Hee Joo Kim김희주 @.***> wrote:
Hi,
I'm out of the office for 3 days. May I send you the data next week?
Many thanks, Hee Joo
2024년 1월 20일 (토) 오전 5:36, Saket Choudhary @.***>님이 작성:
Hi @hjbp0610 https://github.com/hjbp0610, Can you share the objects and the entire code with me over email @.*** for me to reproduce.
— Reply to this email directly, view it on GitHub https://github.com/satijalab/seurat/issues/8342#issuecomment-1901077081, or unsubscribe https://github.com/notifications/unsubscribe-auth/ASBWDWAUBG7JTCUVT6AWYNDYPLKMRAVCNFSM6AAAAABCBH5Z42VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMBRGA3TOMBYGE . You are receiving this because you were mentioned.Message ID: @.***>
Hi,
I wanted to emphasize that the dataset I sent you is our own, and not any open public data. So, please be discrete with it and do not share the dataset with other people. If no update or solution is available, please completely discard the dataset.
Thanks,
Hee Joo
On Mon, Jan 22, 2024 at 7:51 AM Hee Joo Kim김희주 @.***> wrote:
CosMx_Seurat.zip https://drive.google.com/file/d/1OUVv873YRirfba1LSppS6rT3JP0jyiIe/view?usp=drive_web Hi,
I've attached the script file and the original flatfiles of the dataset.
Thank you very much for your nice package and help.
Best wishes, Hee Joo
On Sat, Jan 20, 2024 at 7:25 AM Hee Joo Kim김희주 @.***> wrote:
Hi,
I'm out of the office for 3 days. May I send you the data next week?
Many thanks, Hee Joo
2024년 1월 20일 (토) 오전 5:36, Saket Choudhary @.***>님이 작성:
Hi @hjbp0610 https://github.com/hjbp0610, Can you share the objects and the entire code with me over email @.*** for me to reproduce.
— Reply to this email directly, view it on GitHub https://github.com/satijalab/seurat/issues/8342#issuecomment-1901077081, or unsubscribe https://github.com/notifications/unsubscribe-auth/ASBWDWAUBG7JTCUVT6AWYNDYPLKMRAVCNFSM6AAAAABCBH5Z42VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMBRGA3TOMBYGE . You are receiving this because you were mentioned.Message ID: @.***>
Hello.
I have recently encountered the same problem. Re-running my analysis script changed the results. I also tried to re-run it in a different computer, and results were not consistent either.
Hi @hjbp0610 and @jmgs7,
Do you get a warning message saying Quick-TRANSfer stage steps exceeded maximum when you run RunHarmony?
Check issue #25 on their GitHub page. One thing you could do is to increase max.iter.harmony and max.iter.cluster (e.g. to 100).
Hope this helps!
Best, Leon
