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Hi Everyone
I was trying to convert my Single Cell Experiment Object into Seurat, but for some reason, it is failing. Can I get some help here? I looked up the source code of Tsparse of Package Matrix and it's breaking somewhere there. But I don't know the workaround!!
Below is the code and session info:
seurat <- as.Seurat(sce_clean,counts = "counts", data = "logcounts",assay = 'RNA',project = 'SingleCellExperiment')
Warning: Non-unique cell names (colnames) present in the input matrix, making unique
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
Error in intI(j, n = d[2], dn[[2]], give.dn = FALSE) :
invalid character indexing
> sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.2 LTS
Matrix products: default
BLAS/LAPACK: /home/pichkari/miniconda3/envs/R/lib/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_IN.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_IN.UTF-8 LC_COLLATE=en_IN.UTF-8
[5] LC_MONETARY=en_IN.UTF-8 LC_MESSAGES=en_IN.UTF-8
[7] LC_PAPER=en_IN.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_IN.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils
[7] datasets methods base
other attached packages:
[1] SeuratObject_4.0.0 Seurat_4.0.1
[3] scDblFinder_1.4.0 scran_1.18.7
[5] BiocSingular_1.6.0 harmony_1.0
[7] Rcpp_1.0.6 scater_1.18.6
[9] ggplot2_3.3.3 DropletUtils_1.10.3
[11] scales_1.1.1 SingleCellExperiment_1.12.0
[13] SummarizedExperiment_1.20.0 Biobase_2.50.0
[15] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
[17] IRanges_2.24.1 MatrixGenerics_1.2.1
[19] matrixStats_0.58.0 S4Vectors_0.28.1
[21] BiocGenerics_0.36.1
loaded via a namespace (and not attached):
[1] utf8_1.2.1 reticulate_1.19
[3] R.utils_2.10.1 tidyselect_1.1.1
[5] htmlwidgets_1.5.3 grid_4.0.3
[7] BiocParallel_1.24.1 Rtsne_0.15
[9] devtools_2.4.0 munsell_0.5.0
[11] codetools_0.2-18 ica_1.0-2
[13] statmod_1.4.35 xgboost_1.4.1.1
[15] future_1.21.0 miniUI_0.1.1.1
[17] withr_2.4.2 colorspace_2.0-0
[19] rstudioapi_0.13 ROCR_1.0-11
[21] tensor_1.5 listenv_0.8.0
[23] labeling_0.4.2 GenomeInfoDbData_1.2.4
[25] polyclip_1.10-0 farver_2.1.0
[27] rhdf5_2.34.0 rprojroot_2.0.2
[29] parallelly_1.24.0 vctrs_0.3.8
[31] generics_0.1.0 R6_2.5.0
[33] ggbeeswarm_0.6.0 rsvd_1.0.5
[35] locfit_1.5-9.4 bitops_1.0-7
[37] rhdf5filters_1.2.0 spatstat.utils_2.1-0
[39] cachem_1.0.4 DelayedArray_0.16.3
[41] assertthat_0.2.1 promises_1.2.0.1
[43] beeswarm_0.3.1 gtable_0.3.0
[45] beachmat_2.6.4 globals_0.14.0
[47] processx_3.5.1 goftest_1.2-2
[49] rlang_0.4.10 splines_4.0.3
[51] lazyeval_0.2.2 spatstat.geom_2.1-0
[53] BiocManager_1.30.12 yaml_2.2.1
[55] reshape2_1.4.4 abind_1.4-5
[57] httpuv_1.6.0 usethis_2.0.1
[59] tools_4.0.3 ellipsis_0.3.2
[61] spatstat.core_2.1-2 RColorBrewer_1.1-2
[63] sessioninfo_1.1.1 ggridges_0.5.3
[65] plyr_1.8.6 sparseMatrixStats_1.2.1
[67] zlibbioc_1.36.0 purrr_0.3.4
[69] RCurl_1.98-1.3 ps_1.6.0
[71] prettyunits_1.1.1 rpart_4.1-15
[73] deldir_0.2-10 pbapply_1.4-3
[75] viridis_0.6.0 cowplot_1.1.1
[77] zoo_1.8-9 ggrepel_0.9.1
[79] cluster_2.1.2 fs_1.5.0
[81] magrittr_2.0.1 data.table_1.14.0
[83] RSpectra_0.16-0 scattermore_0.7
[85] lmtest_0.9-38 RANN_2.6.1
[87] fitdistrplus_1.1-3 pkgload_1.2.1
[89] patchwork_1.1.1 mime_0.10
[91] xtable_1.8-4 gridExtra_2.3
[93] testthat_3.0.2 compiler_4.0.3
[95] tibble_3.1.1 KernSmooth_2.23-18
[97] crayon_1.4.1 R.oo_1.24.0
[99] htmltools_0.5.1.1 mgcv_1.8-35
[101] later_1.2.0 tidyr_1.1.3
[103] DBI_1.1.1 MASS_7.3-53.1
[105] Matrix_1.3-2 cli_2.5.0
[107] R.methodsS3_1.8.1 igraph_1.2.6
[109] pkgconfig_2.0.3 plotly_4.9.3
[111] scuttle_1.0.4 spatstat.sparse_2.0-0
[113] vipor_0.4.5 dqrng_0.2.1
[115] XVector_0.30.0 stringr_1.4.0
[117] callr_3.7.0 digest_0.6.27
[119] sctransform_0.3.2 RcppAnnoy_0.0.18
[121] spatstat.data_2.1-0 leiden_0.3.7
[123] uwot_0.1.10 edgeR_3.32.1
[125] DelayedMatrixStats_1.12.3 shiny_1.6.0
[127] lifecycle_1.0.0 nlme_3.1-152
[129] jsonlite_1.7.2 Rhdf5lib_1.12.1
[131] BiocNeighbors_1.8.2 desc_1.3.0
[133] viridisLite_0.4.0 limma_3.46.0
[135] fansi_0.4.2 pillar_1.6.0
[137] lattice_0.20-41 fastmap_1.1.0
[139] httr_1.4.2 pkgbuild_1.2.0
[141] survival_3.2-11 remotes_2.3.0
[143] glue_1.4.2 png_0.1-7
[145] bluster_1.0.0 stringi_1.5.3
[147] HDF5Array_1.18.1 memoise_2.0.0
[149] dplyr_1.0.5 irlba_2.3.3
[151] future.apply_1.7.0
Even if this might not be the correct platform to ask about this I just though I could share that I got exactly the same error and ensuring my cellnames were unique solved the problem for me.
I hope that helps
Well !! The seurat function worked after all. Couldn't figure out what was the issue
I tried this code though. I am inquisitive why you used
make.uniquefunction and what will happen if I don't use that?In my case
colnames(sce)wasNULLand that was causing the error you were getting. Seurat expects colnames to be present so I added them, copying from the metadata under thesce$Barcodes. However, again in my case, these Barcodes were not unique so it was still yielding an error. I did modify the colnames in a sensible way for my experiment, and here I just suggestedmake.unique()as an easy workaround ( this adds a .1 to the string if the Barcode is duplicated). I will post that issue here, where a similar problem was discussed in the Seurat github page, they might be able to solve it or give better advise.