t() incompatibility with sparseMatrix

[mgrout81]

When I run create_profile_matrix with a mtx object that needs to be transposed (internal to the function), I get the following error:

[1] "Transposing Matrix"
Error in t.default(mtx) : argument is not a matrix

Inside of create_profile_matrix, mtx is a sparseMatrix:

SpatialDecon/R/create_profile_matrix.R

Line 119 in 94fe087

mtx <- Matrix::Matrix(as.matrix(mtx), sparse = TRUE)

If necessary, mtx is transposed:

SpatialDecon/R/create_profile_matrix.R

Line 142 in 94fe087

mtx<- t(mtx)

t(mtx) does not work, but Matrix::t(mtx) does, when I copy the source code into my R file and edit.

> sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
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 3.10.0

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

time zone: Etc/UTC
tzcode source: system (glibc)

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

other attached packages:
 [1] SpatialDecon_1.12.3 Seurat_5.0.3        SeuratObject_5.0.1  sp_2.1-3            lubridate_1.9.3    
 [6] forcats_1.0.0       stringr_1.5.1       dplyr_1.1.4         purrr_1.0.2         readr_2.1.5        
[11] tidyr_1.3.1         tibble_3.2.1        ggplot2_3.5.0       tidyverse_2.0.0    

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22        splines_4.3.3           later_1.3.2             bitops_1.0-7           
  [5] filelock_1.0.3          cellranger_1.1.0        R.oo_1.26.0             polyclip_1.10-6        
  [9] XML_3.99-0.16.1         fastDummies_1.7.3       lifecycle_1.0.4         globals_0.16.3         
 [13] lattice_0.22-5          MASS_7.3-60.0.1         magrittr_2.0.3          GeomxTools_3.4.0       
 [17] plotly_4.10.4           httpuv_1.6.15           sctransform_0.4.1       spam_2.10-0            
 [21] spatstat.sparse_3.0-3   reticulate_1.36.1       minqa_1.2.6             cowplot_1.1.3          
 [25] pbapply_1.7-2           DBI_1.2.2               RColorBrewer_1.1-3      abind_1.4-5            
 [29] zlibbioc_1.46.0         EnvStats_2.8.1          Rtsne_0.17              R.cache_0.16.0         
 [33] R.utils_2.12.0          BiocGenerics_0.46.0     RCurl_1.98-1.14         rappdirs_0.3.3         
 [37] GenomeInfoDbData_1.2.10 IRanges_2.34.1          S4Vectors_0.38.2        ggrepel_0.9.5          
 [41] irlba_2.3.5.1           listenv_0.9.1           spatstat.utils_3.0-4    pheatmap_1.0.12        
 [45] goftest_1.2-3           RSpectra_0.16-1         spatstat.random_3.2-3   fitdistrplus_1.1-11    
 [49] parallelly_1.37.1       leiden_0.3.9            codetools_0.2-19        xml2_1.3.6             
 [53] tidyselect_1.2.1        lme4_1.1-35.3           viridis_0.6.2           matrixStats_1.3.0      
 [57] stats4_4.3.3            BiocFileCache_2.10.2    spatstat.explore_3.2-1  jsonlite_1.8.8         
 [61] progressr_0.14.0        ggridges_0.5.6          survival_3.5-8          systemfonts_1.0.6      
 [65] tools_4.3.3             progress_1.2.3          ica_1.0-3               Rcpp_1.0.10            
 [69] glue_1.7.0              gridExtra_2.3           ggthemes_5.1.0          GenomeInfoDb_1.36.4    
 [73] numDeriv_2016.8-1.1     withr_3.0.0             fastmap_1.1.1           NanoStringNCTools_1.8.0
 [77] GGally_2.2.1            boot_1.3-29             repmis_0.5              fansi_1.0.6            
 [81] digest_0.6.29           timechange_0.3.0        R6_2.5.1                mime_0.12              
 [85] colorspace_2.1-0        scattermore_1.2         tensor_1.5              spatstat.data_3.0-4    
 [89] biomaRt_2.58.2          RSQLite_2.3.6           R.methodsS3_1.8.2       utf8_1.2.4             
 [93] generics_0.1.3          data.table_1.14.2       prettyunits_1.2.0       httr_1.4.7             
 [97] htmlwidgets_1.6.4       ggstats_0.6.0           uwot_0.2.2              pkgconfig_2.0.3        
[101] gtable_0.3.5            blob_1.2.4              lmtest_0.9-40           XVector_0.40.0         
[105] htmltools_0.5.8.1       dotCall64_1.1-1         scales_1.3.0            Biobase_2.60.0         
[109] png_0.1-8               logNormReg_0.5-0        rstudioapi_0.16.0       rjson_0.2.21           
[113] uuid_1.2-0              tzdb_0.4.0              reshape2_1.4.4          nloptr_2.0.3           
[117] nlme_3.1-164            curl_5.2.1              cachem_1.0.8            zoo_1.8-12             
[121] KernSmooth_2.23-22      vipor_0.4.7             parallel_4.3.3          miniUI_0.1.1.1         
[125] AnnotationDbi_1.62.2    pillar_1.9.0            grid_4.3.3              vctrs_0.6.5            
[129] RANN_2.6.1              promises_1.3.0          dbplyr_2.5.0            xtable_1.8-4           
[133] cluster_2.1.6           beeswarm_0.4.0          cli_3.6.2               compiler_4.3.3         
[137] rlang_1.1.1             crayon_1.5.2            future.apply_1.11.2     ggbeeswarm_0.7.2       
[141] plyr_1.8.7              ggiraph_0.8.9           stringi_1.8.3           viridisLite_0.4.2      
[145] deldir_2.0-4            lmerTest_3.1-3          munsell_0.5.1           Biostrings_2.68.1      
[149] lazyeval_0.2.2          spatstat.geom_2.4-0     Matrix_1.6-5            RcppHNSW_0.6.0         
[153] hms_1.1.3               patchwork_1.2.0         bit64_4.0.5             future_1.33.2          
[157] KEGGREST_1.40.1         shiny_1.8.1.1           ROCR_1.0-11             igraph_2.0.3           
[161] memoise_2.0.1           bit_4.0.5               readxl_1.4.3

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