AustinHartman
commented
1 year ago Could you provide a small reproducible example? It is possible that the graph provided to the leiden algorithm unexpectedly contains some non-integer values.
Open denvercal1234GitHub opened 1 year ago
AustinHartman
commented
1 year ago Could you provide a small reproducible example? It is possible that the graph provided to the leiden algorithm unexpectedly contains some non-integer values.
yuhanH
commented
1 year ago Do you meet the similar warning if you use other clustering algorithm, such as algorithm = 3?
denvercal1234GitHub
commented
1 year ago Thanks, @AustinHartman for your response. The code is below. Do you detect any thing wrong in the steps before FindClusters()?
You can download the data (12.5MB) at https://drive.google.com/file/d/1zBO1XAPnlt-SCUwWcgFWnOX4fcEA-Ve1/view?usp=share_link. It was a subset of cells from the 20220215_tonsil_atlas_cite_seurat_obj(from https://zenodo.org/record/6340174#.ZCFjkBXMI0R)
load(".../Massoni_20220215_tonsil_atlas_cite_seurat_obj_CD8Tcells_BCLL15_8_9.RData")
data_ID.list <- SplitObject(Massoni_20220215_tonsil_atlas_cite_seurat_obj_CD8Tcells_BCLL15_8_9, split.by = "data_ID")
### Process RNA data with SCTransform for RNA-based clustering
for (i in 1:length(data_ID.list)) {
DefaultAssay(data_ID.list[[i]]) <- 'RNA'
data_ID.list[[i]] <- NormalizeData(data_ID.list[[i]], assay = 'RNA')
DefaultAssay(data_ID.list[[i]]) <- 'RNA'
data_ID.list[[i]] <- CellCycleScoring(data_ID.list[[i]], s.features = s.genes, g2m.features = g2m.genes, set.ident = F)
DefaultAssay(data_ID.list[[i]]) <- 'RNA'
data_ID.list[[i]]$CC.Difference <- data_ID.list[[i]]$S.Score - data_ID.list[[i]]$G2M.Score
DefaultAssay(data_ID.list[[i]]) <- 'RNA'
data_ID.list[[i]] <- SCTransform(data_ID.list[[i]], verbose = FALSE, method = "glmGamPoi", vst.flavor = "v2", return.only.var.genes = F, assay = "RNA", vars.to.regress = c("pct_mt", "CC.Difference", "pct_ribosomal"), min_cells=4)
}
data_ID.list_MERGED <- merge(data_ID.list[[1]], y = c(data_ID.list[[2]], data_ID.list[[3]],data_ID.list[[4]],data_ID.list[[5]],data_ID.list[[6]],data_ID.list[[7]],data_ID.list[[8]],data_ID.list[[9]],data_ID.list[[10]],data_ID.list[[11]],data_ID.list[[12]],data_ID.list[[13]],data_ID.list[[14]],data_ID.list[[15]],data_ID.list[[16]], data_ID.list[[17]],data_ID.list[[18]],data_ID.list[[19]],data_ID.list[[20]],data_ID.list[[21]],data_ID.list[[22]],data_ID.list[[23]],data_ID.list[[24]]), merge.data = T)
data_ID.list_MERGED_var_features <- SelectIntegrationFeatures(data_ID.list, assay = c("SCT", "SCT", "SCT", "SCT","SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT", "SCT"), nfeatures = 3000, fvf.nfeatures=3000)
DefaultAssay(data_ID.list_MERGED) <- 'SCT'
VariableFeatures(data_ID.list_MERGED) <- data_ID.list_MERGED_var_features
data_ID.list_MERGED <- RunPCA(data_ID.list_MERGED, verbose = FALSE, npcs=50, assay = 'SCT', features = data_ID.list_MERGED_var_features)
data_ID.list_MERGED <- RunHarmony(data_ID.list_MERGED, reduction = "pca", dims = 1:31, group.by.vars = "data_ID", assay.use = "SCT", reduction.save = "harmony_SCT_QNN")
### Process and integrate ADT data for visualization of protein expression
DefaultAssay(data_ID.list_MERGED) <- 'ADT'
# we will use all ADT features for dimensional reduction
# we set a dimensional reduction name to avoid overwriting the
VariableFeatures(data_ID.list_MERGED) <- rownames(data_ID.list_MERGED[["ADT"]])
data_ID.list_MERGED <- NormalizeData(data_ID.list_MERGED, normalization.method = 'CLR', margin = 2) %>%
ScaleData() %>% RunPCA(reduction.name = 'apca') %>%
RunHarmony(reduction = "apca",dims = 1:20, group.by.vars = "data_ID", assay.use = "ADT", reduction.save = "harmony_ADT_QNN")
### UMAP and clustering based on transcriptome
DefaultAssay(data_ID.list_MERGED) <- 'SCT'
data_ID.list_MERGED <- RunUMAP(data_ID.list_MERGED, dims = 1:31, reduction = "harmony_SCT_QNN", return.model=T) %>% FindNeighbors(reduction = "harmony_SCT_QNN", dims = 1:31)
DefaultAssay(data_ID.list_MERGED) <- 'SCT'
for(i in seq(0,2,0.5)){
data_ID.list_MERGED <- Seurat::FindClusters(data_ID.list_MERGED, algorithm = 4, resolution = i, verbose = T)
}
#Not ran in this example: data_ID.list_MERGED <- PrepSCTFindMarkers(data_ID.list_MERGED, assay = "SCT")This is where it threw a Warning Warning: NAs introduced by coercion to integer rangeWarning in paste(condition$message, collapse = "\n")
> sessionInfo()
R version 4.2.3 (2023-03-15)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.2.1
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] grid stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggVennDiagram_1.2.2 harmony_0.1.1 Rcpp_1.0.10 scales_1.2.1 flexclust_1.4-1
[6] modeltools_0.2-23 lattice_0.20-45 patchwork_1.1.2.9000 pbmc3k.SeuratData_3.1.4 bmcite.SeuratData_0.3.0
[11] SeuratData_0.2.2 SeuratDisk_0.0.0.9020 scater_1.24.0 scuttle_1.6.3 SingleCellExperiment_1.20.0
[16] SummarizedExperiment_1.28.0 Biobase_2.58.0 GenomicRanges_1.50.2 GenomeInfoDb_1.34.9 IRanges_2.32.0
[21] S4Vectors_0.36.1 BiocGenerics_0.44.0 MatrixGenerics_1.10.0 matrixStats_0.63.0 dplyr_1.1.1
[26] ggplot2_3.4.1 Signac_1.9.0 HCATonsilData_0.0.0.9000 SeuratObject_4.1.3 Seurat_4.3.0
loaded via a namespace (and not attached):
[1] utf8_1.2.3 spatstat.explore_3.1-0 reticulate_1.28 tidyselect_1.2.0
[5] RSQLite_2.3.0 AnnotationDbi_1.60.0 htmlwidgets_1.6.2 BiocParallel_1.30.4
[9] Rtsne_0.16 munsell_0.5.0 ScaledMatrix_1.4.1 codetools_0.2-19
[13] ica_1.0-3 DT_0.27 future_1.32.0 miniUI_0.1.1.1
[17] withr_2.5.0 spatstat.random_3.1-4 colorspace_2.1-0 progressr_0.13.0
[21] filelock_1.0.2 knitr_1.42 rstudioapi_0.14 ROCR_1.0-11
[25] tensor_1.5 listenv_0.9.0 labeling_0.4.2 GenomeInfoDbData_1.2.9
[29] polyclip_1.10-4 farver_2.1.1 bit64_4.0.5 rhdf5_2.42.0
[33] rprojroot_2.0.3 parallelly_1.35.0 vctrs_0.6.1 generics_0.1.3
[37] xfun_0.38 BiocFileCache_2.6.1 R6_2.5.1 ggbeeswarm_0.7.1
[41] rsvd_1.0.5 RVenn_1.1.0 hdf5r_1.3.8 bitops_1.0-7
[45] rhdf5filters_1.10.0 spatstat.utils_3.0-2 cachem_1.0.7 DelayedArray_0.24.0
[49] promises_1.2.0.1 beeswarm_0.4.0 gtable_0.3.3 beachmat_2.12.0
[53] globals_0.16.2 goftest_1.2-3 rlang_1.1.0 RcppRoll_0.3.0
[57] splines_4.2.3 lazyeval_0.2.2 spatstat.geom_3.1-0 BiocManager_1.30.20
[61] yaml_2.3.7 reshape2_1.4.4 abind_1.4-5 httpuv_1.6.9
[65] tools_4.2.3 ellipsis_0.3.2 jquerylib_0.1.4 RColorBrewer_1.1-3
[69] ggridges_0.5.4 plyr_1.8.8 sparseMatrixStats_1.8.0 zlibbioc_1.44.0
[73] purrr_1.0.1 RCurl_1.98-1.10 deldir_1.0-6 viridis_0.6.2
[77] pbapply_1.7-0 cowplot_1.1.1 zoo_1.8-11 ggrepel_0.9.3
[81] cluster_2.1.4 here_1.0.1 magrittr_2.0.3 glmGamPoi_1.8.0
[85] data.table_1.14.8 scattermore_0.8 openxlsx_4.2.5.2 lmtest_0.9-40
[89] RANN_2.6.1 fitdistrplus_1.1-8 evaluate_0.20 mime_0.12
[93] xtable_1.8-4 gridExtra_2.3 compiler_4.2.3 tibble_3.2.1
[97] KernSmooth_2.23-20 crayon_1.5.2 htmltools_0.5.5 later_1.3.0
[101] tidyr_1.3.0 DBI_1.1.3 ExperimentHub_2.6.0 dbplyr_2.3.2
[105] MASS_7.3-58.3 rappdirs_0.3.3 Matrix_1.5-3 cli_3.6.1
[109] parallel_4.2.3 igraph_1.4.1 pkgconfig_2.0.3 sp_1.6-0
[113] plotly_4.10.1.9000 spatstat.sparse_3.0-1 bslib_0.4.2 vipor_0.4.5
[117] XVector_0.38.0 stringr_1.5.0 digest_0.6.31 sctransform_0.3.5.9002
[121] RcppAnnoy_0.0.20 spatstat.data_3.0-1 Biostrings_2.66.0 rmarkdown_2.21
[125] leiden_0.4.3 fastmatch_1.1-3 uwot_0.1.14 DelayedMatrixStats_1.18.2
[129] curl_5.0.0 shiny_1.7.4 Rsamtools_2.12.0 lifecycle_1.0.3.9000
[133] nlme_3.1-162 jsonlite_1.8.4 Rhdf5lib_1.20.0 BiocNeighbors_1.14.0
[137] viridisLite_0.4.1 fansi_1.0.4 pillar_1.9.0 KEGGREST_1.38.0
[141] fastmap_1.1.1 httr_1.4.5 survival_3.5-5 interactiveDisplayBase_1.36.0
[145] glue_1.6.2 zip_2.2.2 png_0.1-8 BiocVersion_3.16.0
[149] bit_4.0.5 sass_0.4.5 class_7.3-21 stringi_1.7.12
[153] HDF5Array_1.26.0 blob_1.2.4 BiocSingular_1.12.0 AnnotationHub_3.6.0
[157] memoise_2.0.1 irlba_2.3.5.1 future.apply_1.10.0 And @yuhanH, if I instead did algorithm = 3, no warning or error occured. When do you usually prefer the 3 = SLM algorithm for transcriptome over the Leiden in some cases? Thank you for your help.
for(i in seq(0,2,0.5)){
data_ID.list_MERGED <- Seurat::FindClusters(data_ID.list_MERGED, algorithm = 3, resolution = i, verbose = T)
}Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1286
Number of edges: 118834
Running smart local moving algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1286
Number of edges: 118834
Running smart local moving algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6947
Number of communities: 4
Elapsed time: 1 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1286
Number of edges: 118834
Running smart local moving algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5699
Number of communities: 6
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1286
Number of edges: 118834
Running smart local moving algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4674
Number of communities: 9
Elapsed time: 0 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 1286
Number of edges: 118834
Running smart local moving algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.4027
Number of communities: 10
Elapsed time: 0 seconds
Harmony714
commented
1 year ago I also have this problem. when I run seuobj <- FindClusters(object = seuobj ,algorithm=1,resolution =0.4），its ok.
when I run seuobj <- FindClusters(object = seuobj ,algorithm=4,resolution =0.4）, I have same problem
eonurk
commented
12 months ago Seurat v4 documentation says:
method Method for running leiden (defaults to matrix which is fast for small datasets). Enable method = "igraph" to avoid casting large data to a dense matrix
algorithm Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python.
So try method = "igraph".
I think documentation should be clearer with this though, method should be after algorithm.
RijndertAriese
commented
11 months ago Hi, I have the same problem. A lot of warnings when I use the leiden algorithm (also with method = "igraph"). Did someone find a fix for this? In the end I do get clusters that are looking well defined in different resolutions, so I'm also wondering how influential these warnings are.
carlacohen
commented
2 weeks ago Same for me as RijndertAriese
JordanWean
commented
1 week ago Same problem as well. Only using Leidgenalg. Problem occurs whether I use igraph or not.
SteGruener
commented
1 week ago Same issue for me (and my colleagues) as well. I don't think that it affects the output, but would be good if the developers could confirm that.
Hi there,
Thanks for the package.
When I was running
FindClusters(algorithm=4), I encountered this Warning. Should I be worried about it? If so, would you mind helping me diagnose this warning?Thank you again for your help.