Error: matrix multiplication: incompatible matrix dimensions: 100x96490 and 96489x22
Opened this issue · 3 comments
When I am running RunHarmony, it produces this error message. What could this mean? I have ran the same code before and never encountered this error. Would appreciate any help, thanks!
ERROR MESSAGE:
Error: matrix multiplication: incompatible matrix dimensions: 100x96490 and 96489x22
In addition: Warning messages:
1: In theta * (1 - exp(-(N_b/(nclust * tau))^2)) :
longer object length is not a multiple of shorter object length
2: In rbind(rep(1, N), phi) :
number of columns of result is not a multiple of vector length (arg 1)
CODE SAMPLE:
pbmc_small <- FilterGenes(object = pbmc_small, min.value = 0.5, min.cells = 100)
pbmc_small <- NormalizeData(pbmc_small)
pbmc_small <- FindVariableFeatures(pbmc_small, selection.method = "vst", nfeatures = 2000)
pbmc_small <- ScaleData(pbmc_small, features = rownames(pbmc_small))
pbmc_small <- RunPCA(pbmc_small, features = VariableFeatures(object = pbmc_small))
pbmc_small@meta.data$library_id <- as.factor(pbmc_small@meta.data$library_id)
pbmc_small <- RunHarmony(pbmc_small, group.by.vars="library_id", verbose = TRUE)
Hi @claireanjou,
sorry for the delayed response. Can you share your sessionInfo() to determine your Seurat and harmony versions?
Have the same problem.
SessionInfo:
R version 4.3.3 (2024-02-29)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Manjaro Linux
Matrix products: default
BLAS/LAPACK: /home/s/anaconda3/envs/deg/lib/libopenblasp-r0.3.27.so; LAPACK version 3.12.0
locale:
[1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
[5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
[7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Lisbon
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] reticulate_1.38.0 scuttle_1.12.0
[3] scRNAseq_2.16.0 SingleCellExperiment_1.24.0
[5] ggplot2_3.5.1 celldex_1.12.0
[7] SingleR_2.4.0 SummarizedExperiment_1.32.0
[9] Biobase_2.62.0 GenomicRanges_1.54.1
[11] GenomeInfoDb_1.38.1 IRanges_2.36.0
[13] S4Vectors_0.40.2 BiocGenerics_0.48.1
[15] MatrixGenerics_1.14.0 matrixStats_1.3.0
[17] IRdisplay_1.1 patchwork_1.2.0
[19] dplyr_1.1.4 Seurat_5.1.0
[21] SeuratObject_5.0.2 sp_2.1-4
[23] spatstat.utils_3.0-5
loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.22 splines_4.3.3
[3] later_1.3.2 BiocIO_1.12.0
[5] pbdZMQ_0.3-11 filelock_1.0.3
[7] bitops_1.0-7 tibble_3.2.1
[9] polyclip_1.10-6 XML_3.99-0.16.1
[11] fastDummies_1.7.4 lifecycle_1.0.4
[13] ensembldb_2.26.0 globals_0.16.3
[15] lattice_0.22-6 MASS_7.3-60
[17] magrittr_2.0.3 plotly_4.10.4
[19] yaml_2.3.10 httpuv_1.6.15
[21] sctransform_0.4.1 spam_2.9-1
[23] spatstat.sparse_3.1-0 cowplot_1.1.3
[25] pbapply_1.7-2 DBI_1.2.2
[27] RColorBrewer_1.1-3 abind_1.4-5
[29] zlibbioc_1.48.0 Rtsne_0.17
[31] purrr_1.0.2 AnnotationFilter_1.26.0
[33] RCurl_1.98-1.14 rappdirs_0.3.3
[35] GenomeInfoDbData_1.2.11 ggrepel_0.9.5
[37] irlba_2.3.5.1 listenv_0.9.1
[39] goftest_1.2-3 RSpectra_0.16-1
[41] spatstat.random_3.2-3 fitdistrplus_1.2-1
[43] parallelly_1.37.1 DelayedMatrixStats_1.24.0
[45] leiden_0.4.3.1 codetools_0.2-20
[47] DelayedArray_0.28.0 xml2_1.3.6
[49] tidyselect_1.2.1 farver_2.1.2
[51] ScaledMatrix_1.10.0 BiocFileCache_2.10.1
[53] base64enc_0.1-3 spatstat.explore_3.2-6
[55] GenomicAlignments_1.38.0 jsonlite_1.8.8
[57] progressr_0.14.0 ggridges_0.5.6
[59] survival_3.6-4 progress_1.2.3
[61] tools_4.3.3 ica_1.0-3
[63] Rcpp_1.0.13 glue_1.7.0
[65] gridExtra_2.3 SparseArray_1.2.2
[67] withr_3.0.1 BiocManager_1.30.23
[69] fastmap_1.1.1 fansi_1.0.6
[71] digest_0.6.35 rsvd_1.0.5
[73] R6_2.5.1 mime_0.12
[75] colorspace_2.1-0 scattermore_1.2
[77] tensor_1.5 biomaRt_2.58.0
[79] spatstat.data_3.1-2 RSQLite_2.3.4
[81] utf8_1.2.4 tidyr_1.3.1
[83] generics_0.1.3 data.table_1.16.0
[85] rtracklayer_1.62.0 prettyunits_1.2.0
[87] httr_1.4.7 htmlwidgets_1.6.4
[89] S4Arrays_1.2.0 uwot_0.1.16
[91] pkgconfig_2.0.3 gtable_0.3.5
[93] blob_1.2.4 lmtest_0.9-40
[95] XVector_0.42.0 htmltools_0.5.8.1
[97] dotCall64_1.1-0 ProtGenerics_1.34.0
[99] scales_1.3.0 png_0.1-8
[101] harmony_1.0 rjson_0.2.21
[103] reshape2_1.4.4 uuid_1.2-0
[105] curl_5.1.0 nlme_3.1-164
[107] repr_1.1.7 zoo_1.8-12
[109] cachem_1.0.8 stringr_1.5.1
[111] BiocVersion_3.18.1 KernSmooth_2.23-22
[113] parallel_4.3.3 miniUI_0.1.1.1
[115] AnnotationDbi_1.64.1 restfulr_0.0.15
[117] pillar_1.9.0 grid_4.3.3
[119] vctrs_0.6.5 RANN_2.6.2
[121] promises_1.3.0 BiocSingular_1.18.0
[123] dbplyr_2.5.0 beachmat_2.18.0
[125] xtable_1.8-4 cluster_2.1.6
[127] evaluate_0.24.0 GenomicFeatures_1.54.1
[129] Rsamtools_2.18.0 cli_3.6.3
[131] compiler_4.3.3 rlang_1.1.4
[133] crayon_1.5.2 future.apply_1.11.2
[135] labeling_0.4.3 plyr_1.8.9
[137] stringi_1.8.4 viridisLite_0.4.2
[139] deldir_2.0-4 BiocParallel_1.36.0
[141] munsell_0.5.1 Biostrings_2.70.1
[143] lazyeval_0.2.2 spatstat.geom_3.2-9
[145] Matrix_1.6-5 ExperimentHub_2.10.0
[147] IRkernel_1.3.2 RcppHNSW_0.6.0
[149] hms_1.1.3 sparseMatrixStats_1.14.0
[151] bit64_4.0.5 future_1.33.2
[153] KEGGREST_1.42.0 shiny_1.8.1.1
[155] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0
[157] ROCR_1.0-11 igraph_2.0.3
[159] memoise_2.0.1 bit_4.0.5
Hi @fistorres,
Can you share the code you are running? Is this on the pbmc_small dataset?