stage2: incorrect number of dimensions

[yanhaidong1]

Hey,

When I used the testing data provided in the github to run the scID, but it returns the following error.
Would you help figure it out?

Best wishes,
Haidong

scID_output <- scid_multiclass(target_gem = target_gem, reference_gem = reference_gem, reference_clusters = reference_clusters, logFC = 0.6, only_pos = FALSE, estimate_weights_from_target = FALSE)
Stage 1: extract signatures genes from reference clusters
Performing log-normalization
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
|======================================================================| 100%
Warning: No DE genes identified
Warning: The following tests were not performed:
Warning: When testing 0 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 1 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 2 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 3 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 4 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 5 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 6 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 7 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 8 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 9 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 10 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 11 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 12 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 13 versus all:
missing value where TRUE/FALSE needed
Warning: When testing 14 versus all:
missing value where TRUE/FALSE needed
Stage 2: Estimate weights of signature genes from reference
Error in ref_gem_norm[signature_genes, , drop = FALSE] :
incorrect number of dimensions

[kboufea]

Hi Haidong,

To me it looks like you don't have MAST installed which is needed for extracting the gene signatures. If that's the case, could you please install it and try again?
https://www.bioconductor.org/packages/release/bioc/html/MAST.html
Please let me know if you have further issues.

Best,
Katerina

[yanhaidong1]

Hey Katerina,

Thanks for the replying.
I still met this issue even I installed the MAST.

library(MAST)
library(scID)

I reinstalled the MAST but still it did not work.

Best,
Haidong

[kboufea]

Hi Haidong,

I was not able to replicate your issue. Could you please share with me your sessionInfo()?
I am using scID_2.2, Seurat_4.2.1 and MAST_1.16.0. What versions are you using?

Thanks,
Katerina

[yanhaidong1]

Hey Katerina,

Here is the session information. Please have a look.

Best,
Haidong

sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /apps/eb/OpenBLAS/0.3.12-GCC-10.2.0/lib/libopenblas_zenp-r0.3.12.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base

other attached packages:
[1] sp_1.5-0 SeuratObject_4.1.0
[3] Seurat_4.1.1 MAST_1.22.0
[5] SingleCellExperiment_1.10.1 SummarizedExperiment_1.26.1
[7] Biobase_2.56.0 GenomicRanges_1.48.0
[9] GenomeInfoDb_1.32.2 IRanges_2.30.0
[11] S4Vectors_0.34.0 BiocGenerics_0.42.0
[13] MatrixGenerics_1.8.1 matrixStats_0.62.0
[15] scID_2.2

loaded via a namespace (and not attached):
[1] Rtsne_0.16 colorspace_2.0-3 deldir_1.0-6
[4] ellipsis_0.3.2 ggridges_0.5.3 XVector_0.36.0
[7] spatstat.data_2.2-0 leiden_0.4.2 listenv_0.8.0
[10] ggrepel_0.9.1 fansi_1.0.3 codetools_0.2-18
[13] splines_4.2.1 polyclip_1.10-0 jsonlite_1.8.0
[16] ica_1.0-3 cluster_2.1.3 png_0.1-7
[19] rgeos_0.5-9 uwot_0.1.11 shiny_1.7.2
[22] sctransform_0.3.3 spatstat.sparse_2.1-1 compiler_4.2.1
[25] httr_1.4.3 assertthat_0.2.1 Matrix_1.4-1
[28] fastmap_1.1.0 lazyeval_0.2.2 cli_3.3.0
[31] later_1.3.0 htmltools_0.5.3 tools_4.2.1
[34] igraph_1.3.4 gtable_0.3.0 glue_1.6.2
[37] GenomeInfoDbData_1.2.8 RANN_2.6.1 reshape2_1.4.4
[40] dplyr_1.0.9 Rcpp_1.0.9 scattermore_0.8
[43] vctrs_0.4.1 nlme_3.1-158 progressr_0.10.1
[46] lmtest_0.9-40 spatstat.random_2.2-0 stringr_1.4.0
[49] globals_0.15.1 mime_0.12 miniUI_0.1.1.1
[52] lifecycle_1.0.1 irlba_2.3.5 goftest_1.2-3
[55] future_1.26.1 zlibbioc_1.42.0 MASS_7.3-58
[58] zoo_1.8-11 scales_1.2.0 spatstat.core_2.4-4
[61] promises_1.2.0.1 spatstat.utils_2.3-1 parallel_4.2.1
[64] RColorBrewer_1.1-3 reticulate_1.25 pbapply_1.5-0
[67] gridExtra_2.3 ggplot2_3.3.6 rpart_4.1.16
[70] stringi_1.7.8 rlang_1.0.6 pkgconfig_2.0.3
[73] bitops_1.0-7 lattice_0.20-45 tensor_1.5
[76] ROCR_1.0-11 purrr_0.3.4 patchwork_1.1.1
[79] htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.2
[82] parallelly_1.32.0 RcppAnnoy_0.0.19 plyr_1.8.7
[85] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[88] DelayedArray_0.22.0 DBI_1.1.3 mgcv_1.8-40
[91] pillar_1.8.0 fitdistrplus_1.1-8 survival_3.3-1
[94] abind_1.4-5 RCurl_1.98-1.7 tibble_3.1.8
[97] future.apply_1.9.0 KernSmooth_2.23-20 utf8_1.2.2
[100] spatstat.geom_2.4-0 plotly_4.10.0 grid_4.2.1
[103] data.table_1.14.2 digest_0.6.29 xtable_1.8-4
[106] tidyr_1.2.0 httpuv_1.6.5 munsell_0.5.0
[109] viridisLite_0.4.0