standardise_ctd: matrices not getting converted to sparse

[bschilder]

1. Bug description

Only the specificity quantiles matrices are getting converted to sparse format, making CTDs larger than they need to be.

Expected behaviour

Make all matrices in CTD sparse.

2. Reproducible example

Code

ctd2 <- EWCE::standardise_ctd(ctd)
ctd <- ewceData::ctd()
EWCE:::is_sparse_matrix(ctd2[[1]]$mean_exp) # FALSE
EWCE:::is_sparse_matrix(ctd2[[1]]$specificity) # FALSE
EWCE:::is_sparse_matrix(ctd2[[1]]$specificity_quantiles) # TRUE

3. Session info

(Add output of the R function utils::sessionInfo() below. This helps us assess version/OS conflicts which could be causing bugs.)

R Under development (unstable) (2022-02-25 r81808)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so

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       

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

other attached packages:
 [1] ewceData_1.5.0      ExperimentHub_2.5.0 AnnotationHub_3.5.0 BiocFileCache_2.5.0 dbplyr_2.2.1       
 [6] BiocGenerics_0.43.0 sp_1.5-0            SeuratObject_4.1.0  Seurat_4.1.1        echoconda_0.99.6   
[11] scKirby_0.1.0       EWCE_1.5.3          RNOmni_1.0.0        dplyr_1.0.9        

loaded via a namespace (and not attached):
  [1] pbapply_1.5-0                 lattice_0.20-45               vctrs_0.4.1                  
  [4] expm_0.999-6                  fastICA_1.2-3                 usethis_2.1.6                
  [7] mgcv_1.8-40                   blob_1.2.3                    survival_3.3-1               
 [10] spatstat.data_2.2-0           later_1.3.0                   nloptr_2.0.3                 
 [13] DBI_1.1.3                     R.utils_2.12.0                SingleCellExperiment_1.19.0  
 [16] rappdirs_0.3.3                uwot_0.1.11                   zlibbioc_1.43.0              
 [19] rgeos_0.5-9                   htmlwidgets_1.5.4             mvtnorm_1.1-3                
 [22] GlobalOptions_0.1.2           future_1.26.1                 leiden_0.4.2                 
 [25] parallel_4.2.0                irlba_2.3.5                   Rcpp_1.0.9                   
 [28] readr_2.1.2                   KernSmooth_2.23-20            promises_1.2.0.1             
 [31] gdata_2.18.0.1                DDRTree_0.1.5                 DelayedArray_0.23.0          
 [34] limma_3.53.4                  pkgload_1.3.0                 clusterGeneration_1.3.7      
 [37] fs_1.5.2                      googleAuthR_2.0.0             fastmatch_1.1-3              
 [40] mnormt_2.1.0                  basilisk_1.9.2                digest_0.6.29                
 [43] png_0.1-7                     qlcMatrix_0.9.7               sctransform_0.3.3            
 [46] cowplot_1.1.1                 here_1.0.1                    pkgconfig_2.0.3              
 [49] docopt_0.7.1                  spatstat.random_2.2-0         iterators_1.0.14             
 [52] minqa_1.2.4                   reticulate_1.25               SummarizedExperiment_1.27.1  
 [55] circlize_0.4.15               GetoptLong_1.0.5              xfun_0.31                    
 [58] zoo_1.8-10                    tidyselect_1.1.2              reshape2_1.4.4               
 [61] purrr_0.3.4                   ica_1.0-3                     gprofiler2_0.2.1             
 [64] viridisLite_0.4.0             rtracklayer_1.57.0            pkgbuild_1.3.1               
 [67] rlang_1.0.4                   glue_1.6.2                    RColorBrewer_1.1-3           
 [70] orthogene_1.3.1               pals_1.7                      registry_0.5-1               
 [73] matrixStats_0.62.0            MatrixGenerics_1.9.1          stringr_1.4.0                
 [76] ggsignif_0.6.3                labeling_0.4.2                httpuv_1.6.5                 
 [79] class_7.3-20                  webshot_0.5.3                 jsonlite_1.8.0               
 [82] XVector_0.37.0                sceasy_0.0.6                  bit_4.0.4                    
 [85] mime_0.12                     gridExtra_2.3                 gplots_3.1.3                 
 [88] Rsamtools_2.13.3              Exact_3.1                     stringi_1.7.8                
 [91] processx_3.7.0                spatstat.sparse_2.1-1         scattermore_0.8              
 [94] yulab.utils_0.0.5             quadprog_1.5-8                bitops_1.0-7                 
 [97] cli_3.3.0                     rhdf5filters_1.9.0            maps_3.4.0                   
[100] RSQLite_2.2.15                tidyr_1.2.0                   heatmaply_1.3.0              
[103] pheatmap_1.0.12               homologene_1.4.68.19.3.27     data.table_1.14.2            
[106] HGNChelper_0.8.1              rstudioapi_0.13               TSP_1.2-1                    
[109] GenomicAlignments_1.33.0      nlme_3.1-158                  phangorn_2.9.0               
[112] VariantAnnotation_1.43.2      listenv_0.8.0                 miniUI_0.1.1.1               
[115] gridGraphics_0.5-1            leidenbase_0.1.11             R.oo_1.25.0                  
[118] urlchecker_1.0.1              sessioninfo_1.2.2             readxl_1.4.0                 
[121] lifecycle_1.0.1               munsell_0.5.0                 cellranger_1.1.0             
[124] R.methodsS3_1.8.2             mapproj_1.2.8                 caTools_1.18.2               
[127] codetools_0.2-18              coda_0.19-4                   Biobase_2.57.1               
[130] GenomeInfoDb_1.33.3           lmtest_0.9-40                 ontologyIndex_2.7            
[133] xtable_1.8-4                  ROCR_1.0-11                   BiocManager_1.30.18          
[136] scatterplot3d_0.3-41          abind_1.4-5                   farver_2.1.1                 
[139] parallelly_1.32.1             RANN_2.6.1                    aplot_0.1.6                  
[142] sparsesvd_0.2                 ggtree_3.5.1                  GenomicRanges_1.49.0         
[145] BiocIO_1.7.1                  GEOquery_2.65.2               RcppAnnoy_0.0.19             
[148] goftest_1.2-3                 patchwork_1.1.1               tibble_3.1.7                 
[151] ggdendro_0.1.23               profvis_0.3.7                 dichromat_2.0-0.1            
[154] cluster_2.1.3                 future.apply_1.9.0            dendextend_1.16.0            
[157] GeneOverlap_1.33.0            Matrix_1.4-1                  tidytree_0.3.9               
[160] ellipsis_0.3.2                prettyunits_1.1.1             ggridges_0.5.3               
[163] igraph_1.3.4                  remotes_2.4.2                 downloadR_0.99.3             
[166] slam_0.1-50                   gargle_1.2.0                  basilisk.utils_1.9.1         
[169] phytools_1.0-3                spatstat.utils_2.3-1          htmltools_0.5.3              
[172] piggyback_0.1.4               yaml_2.3.5                    GenomicFeatures_1.49.5       
[175] utf8_1.2.2                    plotly_4.10.0                 interactiveDisplayBase_1.35.0
[178] XML_3.99-0.10                 e1071_1.7-11                  ggpubr_0.4.0                 
[181] fitdistrplus_1.1-8            BiocParallel_1.31.10          bit64_4.0.5                  
[184] rootSolve_1.8.2.3             foreach_1.5.2                 Biostrings_2.65.1            
[187] spatstat.core_2.4-4           combinat_0.0-8                progressr_0.10.1             
[190] MAGMA.Celltyping_2.0.4        devtools_2.4.4                evaluate_0.15                
[193] memoise_2.0.1                 VGAM_1.1-7                    tzdb_0.3.0                   
[196] callr_3.7.1                   lmom_2.9                      ps_1.7.1                     
[199] curl_4.3.2                    fansi_1.0.3                   tensor_1.5                   
[202] cachem_1.0.6                  deldir_1.0-6                  babelgene_22.3               
[205] dir.expiry_1.5.0              ggplot2_3.3.6                 rjson_0.2.21                 
[208] rstatix_0.7.0                 ggrepel_0.9.1                 clue_0.3-61                  
[211] rprojroot_2.0.3               tools_4.2.0                   magrittr_2.0.3               
[214] RCurl_1.98-1.7                proxy_0.4-27                  car_3.1-0                    
[217] ape_5.6-2                     ggplotify_0.1.0               xml2_1.3.3                   
[220] httr_1.4.3                    assertthat_0.2.1              rmarkdown_2.14               
[223] boot_1.3-28                   globals_0.15.1                R6_2.5.1                     
[226] Rhdf5lib_1.19.2               progress_1.2.2                KEGGREST_1.37.3              
[229] treeio_1.21.0                 gtools_3.9.3                  shape_1.4.6                  
[232] corrplot_0.92                 BiocVersion_3.16.0            HDF5Array_1.25.1             
[235] rhdf5_2.41.1                  splines_4.2.0                 carData_3.0-5                
[238] ggfun_0.0.6                   colorspace_2.0-3              generics_0.1.3               
[241] stats4_4.2.0                  pillar_1.8.0                  anndata_0.7.5.3              
[244] HSMMSingleCell_1.17.0         GenomeInfoDbData_1.2.8        plyr_1.8.7                   
[247] gtable_0.3.0                  monocle_2.25.1                restfulr_0.0.15              
[250] knitr_1.39                    ComplexHeatmap_2.13.0         biomaRt_2.53.2               
[253] IRanges_2.31.0                fastmap_1.1.0                 seriation_1.3.6              
[256] doParallel_1.0.17             AnnotationDbi_1.59.1          broom_1.0.0                  
[259] BSgenome_1.65.2               scales_1.2.0                  filelock_1.0.2               
[262] backports_1.4.1               plotrix_3.8-2                 S4Vectors_0.35.1             
[265] lme4_1.1-30                   gld_2.6.5                     hms_1.1.1                    
[268] Rtsne_0.16                    shiny_1.7.2                   MungeSumstats_1.5.5          
[271] polyclip_1.10-0               grid_4.2.0                    numDeriv_2016.8-1.1          
[274] DescTools_0.99.45             lazyeval_0.2.2                crayon_1.5.1                 
[277] MASS_7.3-58                   viridis_0.6.2                 rpart_4.1.16                 
[280] compiler_4.2.0                spatstat.geom_2.4-0   

[bschilder]

Also, make standardise_ctd more generalizable to all matrices stored in CTD, not just those I've hard-coded into the function.