Hello! I am trying to run a Seurat pipeline that previously worked on an older version of R in the latest version and I am running into a weird error. I have asked this question over on Seurat's github but I wanted to ask here in case it is a session/docker issue and not with Seurat specifically.
My code worked just fine with:
R version 4.3.0, Platform: x86_64-pc-linux-gnu (64-bit) Running
under: Ubuntu 20.04.5 LTS Matrix products: default BLAS:
/usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK:
/usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0 Seurat v5.1.0
Current session info (where the error is occurring) is at the bottom of the post.
I run the following to integrate my layers after SCTransform and PCA:
All_Samples <- IntegrateLayers( object = All_Samples, method = CCAIntegration, normalization.method = "SCT", verbose = F)
I am met with the error (and a warning) after a certain number of minutes after starting the integration:
Warning in irlba(A = mat3, nv = num.cc) : did not converge--results might be invalid!; try increasing work or maxit
Error in if (sign(x[1]) == -1) { : missing value where TRUE/FALSE needed
For the maxit, I am able to increase it on the previous step PCA (I get that warning there too) but it is not a condition for the IntegrateLayers function.
Running which(is.na(rownames(All_Samples)) shows no NA values
I also notice IntegrateLayers() has a much smaller running time prediction (by like 9 hours) in the newer version/docker. Perhaps the SCTransform is broken?
I performed my pipeline with a small data set and the error does not occur (this is all that I run on a merged seurat object)
InstallData("ifnb")
ifnb <- LoadData("ifnb")
ifnb[["RNA"]] <- split(ifnb[["RNA"]], f = ifnb$stim)
ifnb[["percent.mt"]] <- PercentageFeatureSet(ifnb,pattern="^MT-")
ifnb[["percent.er"]] <- PercentageFeatureSet(ifnb,pattern="^RP[SL][[:digit:]]")
ifnb <- subset(ifnb, subset=nFeature_RNA > 400 & nFeature_RNA < 5000)
ifnb <- subset(ifnb, subset=percent.mt < 30 &percent.er <15)
dim(ifnb)
ifnb[["RNA"]] <- JoinLayers(ifnb[["RNA"]])
countMatrix <- GetAssayData(object = ifnb, slot = "counts")
ifnb.gmcf <- gene.vs.molecule.cell.filter(countMatrix,min.cell.size=200)
ifnb <- subset(ifnb, cells = colnames(ifnb.gmcf))
ifnb[["RNA"]] <- split(ifnb[["RNA"]], f = ifnb$orig.ident)
ifnb <- SCTransform(ifnb, vst.flavor = "v2")
ifnb <- RunPCA(object = ifnb, features = VariableFeatures(object = ifnb), maxit=1000000)
ifnb <- IntegrateLayers( object = ifnb, method = CCAIntegration, normalization.method = "SCT", verbose = F, k.weight=30)
This makes me think it's an issue with my docker or session, any help is appreciated!
session info:
R version 4.4.0 (2024-04-24) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.4 LTS
Matrix products: default BLAS:
/usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0locale: [1] C
time zone: America/Chicago tzcode source: system (glibc)
attached base packages: [1] stats graphics grDevices utils
datasets methods baseother attached packages: [1] ifnb.SeuratData_3.1.0 knitr_1.48
sctransform_0.4.1 glmGamPoi_1.14.3 pagoda2_1.0.12
igraph_2.0.3 Matrix_1.7-0 [8] pdfCluster_1.0-4
patchwork_1.2.0 ggplot2_3.5.1 dplyr_1.1.4
SeuratDisk_0.0.0.9021 Seurat_5.1.0 SeuratObject_5.0.2 [15] sp_2.1-4 SeuratData_0.2.2.9001loaded via a namespace (and not attached): [1] RcppAnnoy_0.0.22
splines_4.4.0 later_1.3.2 urltools_1.7.3 tibble_3.2.1 R.oo_1.26.0 [7] triebeard_0.4.1 polyclip_1.10-7
fastDummies_1.7.3 lifecycle_1.0.4 globals_0.16.3 lattice_0.22-6 [13] hdf5r_1.3.11
MASS_7.3-61 magrittr_2.0.3 plotly_4.10.4 httpuv_1.6.15 spam_2.10-0 [19] spatstat.sparse_3.1-0 reticulate_1.38.0 cowplot_1.1.3 pbapply_1.7-2 RColorBrewer_1.1-3 abind_1.4-5
[25] zlibbioc_1.50.0 Rtsne_0.17
GenomicRanges_1.56.1 purrr_1.0.2 R.utils_2.12.3 BiocGenerics_0.50.0 [31] rappdirs_0.3.3
GenomeInfoDbData_1.2.12 IRanges_2.38.1
S4Vectors_0.42.1 ggrepel_0.9.5 RMTstat_0.3.1 [37] irlba_2.3.5.1 listenv_0.9.1
spatstat.utils_3.0-5 goftest_1.2-3
RSpectra_0.16-2 spatstat.random_3.3-1 [43] brew_1.0-10 fitdistrplus_1.2-1
parallelly_1.37.1 DelayedMatrixStats_1.26.0 leiden_0.4.3.1 codetools_0.2-20 [49] DelayedArray_0.30.1
tidyselect_1.2.1 UCSC.utils_1.0.0
matrixStats_1.3.0 stats4_4.4.0
spatstat.explore_3.3-1 [55] jsonlite_1.8.8 Rook_1.2 progressr_0.14.0 ggridges_0.5.6 survival_3.7-0 tools_4.4.0 [61] ica_1.0-3
Rcpp_1.0.13 glue_1.7.0 gridExtra_2.3 SparseArray_1.4.8 xfun_0.46 [67] mgcv_1.9-1 MatrixGenerics_1.16.0
GenomeInfoDb_1.40.1 withr_3.0.0 fastmap_1.2.0 fansi_1.0.6 [73] digest_0.6.36 R6_2.5.1 mime_0.12 colorspace_2.1-0
scattermore_1.2 N2R_1.0.3 [79] sccore_1.0.5 tensor_1.5
spatstat.data_3.1-2 R.methodsS3_1.8.2 utf8_1.2.4
tidyr_1.3.1 [85] generics_0.1.3
data.table_1.15.4 httr_1.4.7
htmlwidgets_1.6.4 S4Arrays_1.4.1 uwot_0.2.2
[91] pkgconfig_2.0.3 gtable_0.3.5
lmtest_0.9-40 XVector_0.44.0
htmltools_0.5.8.1 dotCall64_1.1-1 [97] scales_1.3.0 Biobase_2.64.0 dendsort_0.3.4 png_0.1-8 spatstat.univar_3.0-0 geometry_0.5.0 [103] rstudioapi_0.16.0 reshape2_1.4.4
rjson_0.2.21 nlme_3.1-165 magic_1.6-1
zoo_1.8-12 [109] stringr_1.5.1
KernSmooth_2.23-24 drat_0.2.4 parallel_4.4.0 miniUI_0.1.1.1 pillar_1.9.0 [115] grid_4.4.0 vctrs_0.6.5 RANN_2.6.1
promises_1.3.0 xtable_1.8-4 cluster_2.1.6 [121] cli_3.6.3 compiler_4.4.0
rlang_1.1.4 crayon_1.5.3
future.apply_1.11.2 plyr_1.8.9 [127] stringi_1.8.4 viridisLite_0.4.2 deldir_2.0-4
munsell_0.5.1 lazyeval_0.2.2
spatstat.geom_3.3-2 [133] RcppHNSW_0.6.0
sparseMatrixStats_1.16.0 bit64_4.0.5 future_1.33.2 shiny_1.8.1.1 SummarizedExperiment_1.34.0 [139] ROCR_1.0-11 bit_4.0.5
Any help or insight is appreciated thank you so much!
