scTensor 2.14.0
Here, we introduced the objects saved in reanalysis.RData.
library("scTensor")
load("reanalysis.RData")After performing cellCellReport, some R objects are saved in the reanalysis.RData as follows;
cellCellSettingcellCellSettingUsing the reanalysis.RData, some users may want to perform scTensor with different parameters.
For example, some users want to perform cellCellDecomp with different ranks, perform cellCellReport with omitting some enrichment analysis, provide the results to their collaborators.
To do such tasks, just type like belows.
library("AnnotationHub")
library("LRBaseDbi")
# Create LRBase object
ah <- AnnotationHub()
dbfile <- query(ah, c("LRBaseDb", "Homo sapiens", "v002"))[[1]]
LRBase.Hsa.eg.db <- LRBaseDbi::LRBaseDb(dbfile)
# Register the file pass of user's LRBase
metadata(sce)$lrbase <- dbfile(LRBase.Hsa.eg.db)
# CCI Tensor Decomposition
cellCellDecomp(sce, ranks=c(6,5), assayNames="normcounts")
# HTML Report
cellCellReport(sce, reducedDimNames="TSNE", assayNames="normcounts",
title="Cell-cell interaction within Germline_Male, GSE86146",
author="Koki Tsuyuzaki", html.open=TRUE,
goenrich=TRUE, meshenrich=FALSE, reactomeenrich=FALSE,
doenrich=FALSE, ncgenrich=FALSE, dgnenrich=FALSE)## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] AnnotationHub_3.12.0
## [2] BiocFileCache_2.12.0
## [3] dbplyr_2.5.0
## [4] scTGIF_1.18.0
## [5] Homo.sapiens_1.3.1
## [6] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [7] org.Hs.eg.db_3.19.1
## [8] GO.db_3.19.1
## [9] OrganismDbi_1.46.0
## [10] GenomicFeatures_1.56.0
## [11] GenomicRanges_1.56.0
## [12] GenomeInfoDb_1.40.0
## [13] AnnotationDbi_1.66.0
## [14] IRanges_2.38.0
## [15] S4Vectors_0.42.0
## [16] Biobase_2.64.0
## [17] BiocGenerics_0.50.0
## [18] scTensor_2.14.0
## [19] BiocStyle_2.32.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.4 matrixStats_1.3.0
## [3] bitops_1.0-7 enrichplot_1.24.0
## [5] HDO.db_0.99.1 httr_1.4.7
## [7] webshot_0.5.5 RColorBrewer_1.1-3
## [9] Rgraphviz_2.48.0 tools_4.4.0
## [11] backports_1.4.1 utf8_1.2.4
## [13] R6_2.5.1 lazyeval_0.2.2
## [15] withr_3.0.0 prettyunits_1.2.0
## [17] graphite_1.50.0 gridExtra_2.3
## [19] schex_1.18.0 fdrtool_1.2.17
## [21] cli_3.6.2 TSP_1.2-4
## [23] scatterpie_0.2.2 entropy_1.3.1
## [25] sass_0.4.9 genefilter_1.86.0
## [27] meshr_2.10.0 Rsamtools_2.20.0
## [29] yulab.utils_0.1.4 gson_0.1.0
## [31] txdbmaker_1.0.0 DOSE_3.30.0
## [33] MeSHDbi_1.40.0 AnnotationForge_1.46.0
## [35] nnTensor_1.2.0 plotrix_3.8-4
## [37] maps_3.4.2 RSQLite_2.3.6
## [39] visNetwork_2.1.2 generics_0.1.3
## [41] gridGraphics_0.5-1 GOstats_2.70.0
## [43] BiocIO_1.14.0 dplyr_1.1.4
## [45] dendextend_1.17.1 Matrix_1.7-0
## [47] fansi_1.0.6 abind_1.4-5
## [49] lifecycle_1.0.4 yaml_2.3.8
## [51] SummarizedExperiment_1.34.0 qvalue_2.36.0
## [53] SparseArray_1.4.0 grid_4.4.0
## [55] blob_1.2.4 misc3d_0.9-1
## [57] crayon_1.5.2 lattice_0.22-6
## [59] msigdbr_7.5.1 cowplot_1.1.3
## [61] annotate_1.82.0 KEGGREST_1.44.0
## [63] pillar_1.9.0 knitr_1.46
## [65] fgsea_1.30.0 tcltk_4.4.0
## [67] rjson_0.2.21 codetools_0.2-20
## [69] fastmatch_1.1-4 glue_1.7.0
## [71] outliers_0.15 ggfun_0.1.4
## [73] data.table_1.15.4 vctrs_0.6.5
## [75] png_0.1-8 treeio_1.28.0
## [77] spam_2.10-0 rTensor_1.4.8
## [79] gtable_0.3.5 assertthat_0.2.1
## [81] cachem_1.0.8 xfun_0.43
## [83] mime_0.12 S4Arrays_1.4.0
## [85] tidygraph_1.3.1 survival_3.6-4
## [87] SingleCellExperiment_1.26.0 seriation_1.5.5
## [89] iterators_1.0.14 fields_15.2
## [91] nlme_3.1-164 Category_2.70.0
## [93] ggtree_3.12.0 bit64_4.0.5
## [95] progress_1.2.3 filelock_1.0.3
## [97] bslib_0.7.0 colorspace_2.1-0
## [99] DBI_1.2.2 tidyselect_1.2.1
## [101] bit_4.0.5 compiler_4.4.0
## [103] curl_5.2.1 httr2_1.0.1
## [105] graph_1.82.0 xml2_1.3.6
## [107] DelayedArray_0.30.0 plotly_4.10.4
## [109] bookdown_0.39 shadowtext_0.1.3
## [111] rtracklayer_1.64.0 checkmate_2.3.1
## [113] scales_1.3.0 hexbin_1.28.3
## [115] RBGL_1.80.0 plot3D_1.4.1
## [117] rappdirs_0.3.3 stringr_1.5.1
## [119] digest_0.6.35 rmarkdown_2.26
## [121] ca_0.71.1 XVector_0.44.0
## [123] htmltools_0.5.8.1 pkgconfig_2.0.3
## [125] MatrixGenerics_1.16.0 fastmap_1.1.1
## [127] rlang_1.1.3 htmlwidgets_1.6.4
## [129] UCSC.utils_1.0.0 farver_2.1.1
## [131] jquerylib_0.1.4 jsonlite_1.8.8
## [133] BiocParallel_1.38.0 GOSemSim_2.30.0
## [135] RCurl_1.98-1.14 magrittr_2.0.3
## [137] GenomeInfoDbData_1.2.12 ggplotify_0.1.2
## [139] dotCall64_1.1-1 patchwork_1.2.0
## [141] munsell_0.5.1 Rcpp_1.0.12
## [143] babelgene_22.9 ape_5.8
## [145] viridis_0.6.5 stringi_1.8.3
## [147] tagcloud_0.6 ggraph_2.2.1
## [149] zlibbioc_1.50.0 MASS_7.3-60.2
## [151] plyr_1.8.9 parallel_4.4.0
## [153] ggrepel_0.9.5 Biostrings_2.72.0
## [155] graphlayouts_1.1.1 splines_4.4.0
## [157] hms_1.1.3 igraph_2.0.3
## [159] biomaRt_2.60.0 reshape2_1.4.4
## [161] BiocVersion_3.19.1 XML_3.99-0.16.1
## [163] evaluate_0.23 BiocManager_1.30.22
## [165] foreach_1.5.2 tweenr_2.0.3
## [167] tidyr_1.3.1 purrr_1.0.2
## [169] polyclip_1.10-6 heatmaply_1.5.0
## [171] ggplot2_3.5.1 ReactomePA_1.48.0
## [173] ggforce_0.4.2 xtable_1.8-4
## [175] restfulr_0.0.15 reactome.db_1.88.0
## [177] tidytree_0.4.6 viridisLite_0.4.2
## [179] tibble_3.2.1 aplot_0.2.2
## [181] ccTensor_1.0.2 memoise_2.0.1
## [183] registry_0.5-1 GenomicAlignments_1.40.0
## [185] cluster_2.1.6 concaveman_1.1.0
## [187] GSEABase_1.66.0