bmilab / vizapa Goto Github PK

Hello,
I am trying to use vizAPA for a research project. I have Sierra generated data and tried to import it to create a PACdataset.
Everything was okay until I try to find markers. I thought the problem is about my dataset then I tried with the example data (pbmc2_sierra_APA.rda) you provided and had the exact same error.
I run these codes:

`iPACds=getAPAindexPACds(scPACds)

markers=getAPAmarkers(iPACds, group= "celltype", everyPair = TRUE , min.pct = 0 , logFC = 0)`

and have this error:

Warning: Data is of class matrix. Coercing to dgCMatrix. Warning: No layers found matching search pattern provided Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function 'rowSums': subscript out of bounds In addition: Warning messages: 1: Layer 'data' is empty 2: Layer 'data' is empty

Also, it is possible to plot UMAP using vizUMAP for more than 10 clusters?

Thank you in advance.

`> sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

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

time zone: Europe/Istanbul
tzcode source: system (glibc)

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

other attached packages:
[1] purrr_1.0.2 Seurat_5.0.1
[3] SeuratObject_5.0.1 sp_2.1-2
[5] devtools_2.4.5 usethis_2.2.2
[7] dplyr_1.1.4 BSgenome.Hsapiens.NCBI.GRCh38_1.3.1000
[9] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.68.0
[11] rtracklayer_1.60.1 Biostrings_2.68.1
[13] XVector_0.40.0 GenomicRanges_1.52.1
[15] GenomeInfoDb_1.36.4 IRanges_2.34.1
[17] S4Vectors_0.38.2 BiocGenerics_0.46.0
[19] movAPA_0.2.0 vizAPA_0.1.0

loaded via a namespace (and not attached):
[1] spatstat.sparse_3.0-3 fs_1.6.3 ProtGenerics_1.32.0
[4] matrixStats_1.1.0 bitops_1.0-7 httr_1.4.7
[7] RColorBrewer_1.1-3 sctransform_0.4.1 profvis_0.3.8
[10] tools_4.3.2 backports_1.4.1 utf8_1.2.4
[13] R6_2.5.1 uwot_0.1.16 lazyeval_0.2.2
[16] Gviz_1.44.2 urlchecker_1.0.1 withr_2.5.2
[19] prettyunits_1.2.0 GGally_2.2.0 gridExtra_2.3
[22] progressr_0.14.0 cli_3.6.1 Biobase_2.60.0
[25] spatstat.explore_3.2-5 fastDummies_1.7.3 labeling_0.4.3
[28] ggbio_1.48.0 spatstat.data_3.0-3 genefilter_1.82.1
[31] pbapply_1.7-2 ggridges_0.5.4 Rsamtools_2.16.0
[34] foreign_0.8-86 dichromat_2.0-0.1 parallelly_1.36.0
[37] sessioninfo_1.2.2 rstudioapi_0.15.0 RSQLite_2.3.3
[40] generics_0.1.3 BiocIO_1.10.0 spatstat.random_3.2-2
[43] ica_1.0-3 car_3.1-2 Matrix_1.6-3
[46] interp_1.1-5 fansi_1.0.5 abind_1.4-5
[49] lifecycle_1.0.4 yaml_2.3.7 carData_3.0-5
[52] SummarizedExperiment_1.30.2 BiocFileCache_2.8.0 Rtsne_0.16
[55] grid_4.3.2 blob_1.2.4 promises_1.2.1
[58] crayon_1.5.2 miniUI_0.1.1.1 lattice_0.22-5
[61] cowplot_1.1.1 GenomicFeatures_1.52.2 annotate_1.78.0
[64] KEGGREST_1.40.1 pillar_1.9.0 knitr_1.45
[67] rjson_0.2.21 future.apply_1.11.0 codetools_0.2-19
[70] leiden_0.4.3.1 glue_1.6.2 data.table_1.14.8
[73] remotes_2.4.2.1 vctrs_0.6.4 png_0.1-8
[76] spam_2.10-0 gtable_0.3.4 cachem_1.0.8
[79] xfun_0.41 S4Arrays_1.0.6 mime_0.12
[82] survival_3.5-7 SingleCellExperiment_1.22.0 iterators_1.0.14
[85] ellipsis_0.3.2 fitdistrplus_1.1-11 ROCR_1.0-11
[88] nlme_3.1-164 bit64_4.0.5 progress_1.2.2
[91] filelock_1.0.2 RcppAnnoy_0.0.21 irlba_2.3.5.1
[94] KernSmooth_2.23-22 rpart_4.1.21 colorspace_2.1-0
[97] DBI_1.1.3 Hmisc_5.1-1 nnet_7.3-19
[100] tidyselect_1.2.0 processx_3.8.2 bit_4.0.5
[103] compiler_4.3.2 curl_5.1.0 graph_1.78.0
[106] htmlTable_2.4.2 xml2_1.3.5 plotly_4.10.3
[109] DelayedArray_0.26.7 checkmate_2.3.0 scales_1.3.0
[112] lmtest_0.9-40 RBGL_1.76.0 callr_3.7.3
[115] rappdirs_0.3.3 goftest_1.2-3 stringr_1.5.1
[118] digest_0.6.33 spatstat.utils_3.0-4 rmarkdown_2.25
[121] htmltools_0.5.7 pkgconfig_2.0.3 jpeg_0.1-10
[124] base64enc_0.1-3 MatrixGenerics_1.12.3 highr_0.10
[127] dbplyr_2.4.0 fastmap_1.1.1 ensembldb_2.24.1
[130] rlang_1.1.2 htmlwidgets_1.6.3 shiny_1.8.0
[133] farver_2.1.1 zoo_1.8-12 jsonlite_1.8.7
[136] BiocParallel_1.34.2 VariantAnnotation_1.46.0 RCurl_1.98-1.13
[139] magrittr_2.0.3 Formula_1.2-5 GenomeInfoDbData_1.2.10
[142] dotCall64_1.1-1 patchwork_1.1.3 munsell_0.5.0
[145] Rcpp_1.0.11 reticulate_1.34.0 stringi_1.8.2
[148] zlibbioc_1.46.0 MASS_7.3-60 plyr_1.8.9
[151] pkgbuild_1.4.2 ggstats_0.5.1 parallel_4.3.2
[154] listenv_0.9.0 ggrepel_0.9.4 deldir_2.0-2
[157] splines_4.3.2 tensor_1.5 hms_1.1.3
[160] ps_1.7.5 ggpubr_0.6.0 igraph_1.5.1
[163] spatstat.geom_3.2-7 ggsignif_0.6.4 RcppHNSW_0.5.0
[166] reshape2_1.4.4 biomaRt_2.56.1 pkgload_1.3.3
[169] XML_3.99-0.15 Sierra_0.99.27 evaluate_0.23
[172] latticeExtra_0.6-30 biovizBase_1.48.0 BiocManager_1.30.22
[175] foreach_1.5.2 httpuv_1.6.12 polyclip_1.10-6
[178] RANN_2.6.1 tidyr_1.3.0 scattermore_1.2
[181] future_1.33.0 ggplot2_3.4.4 broom_1.0.5
[184] xtable_1.8-4 restfulr_0.0.15 AnnotationFilter_1.24.0
[187] RSpectra_0.16-1 rstatix_0.7.2 later_1.3.1
[190] viridisLite_0.4.2 OrganismDbi_1.42.0 tibble_3.2.1
[193] memoise_2.0.1 AnnotationDbi_1.62.2 GenomicAlignments_1.36.0
[196] cluster_2.1.5 globals_0.16.2 `