# How to reanalyze the results of scTensor

Koki Tsuyuzaki1, Manabu Ishii1 and Itoshi Nikaido1*

1Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Reseach

*k.t.the-answer@hotmail.co.jp

#### 6 July 2020

#### Package

scTensor 1.4.3

# Contents

# 1 Summary of the output objects of scTensor

Here, we introduced the objects saved in reanalysis.RData.

    suppressPackageStartupMessages(library("scTensor"))
    load("reanalysis.RData")

After performing `cellCellReport`, some R objects are saved in the reanalysis.RData as follows;

- sce : SingleCellExperiment object
  - metadata(sce)$lrbase : The file pass to the database file of LRBase
  - metadata(sce)$color : The color vector specified by `cellCellSetting`
  - metadata(sce)$label : The label vector specified by `cellCellSetting`
  - metadata(sce)$algorithm : The algorithm for performing scTensor
  - metadata(sce)$sctensor : The results of scTensor
    - metadata(sce)$sctensor$ligand : The factor matrix (Ligand)
    - metadata(sce)$sctensor$receptor : The factor matrix (Receptor)
    - metadata(sce)$sctensor$lrpair : The core tensor
  - metadata(sce)$datasize : The data size of CCI tensor
  - metadata(sce)$ranks : The number of lower dimension in each direction of CCI tensor
  - metadata(sce)$recerror : Reconstruction Error of NTD
  - metadata(sce)$relchange : Relative Change of NTD
- input : The gene expression matrix <# Genes * # Cells>
- twoD : The result of 2D dimensional reduction (e.g. t-SNE)
- LR : The Ligand-Receptor corresponding table extracted from LRBase.XXX.eg.db
- celltypes : The celltype label and color scheme
- index : The core tensor values
- corevalue : The core tensor values (normalized)
- selected : The selected corevalue position with thr threshold “thr”
- ClusterL : The result of analysis in each L vector
- ClusterR : The result of analysis in each R vector
- out.vecLR : The result of analysis in LR pairs
- g : The igraph object to visualize ligand-receptor gene network

# 2 Execution of scTensor with the different options

Using the reanalysis.RData, some user may want to perform scTensor with different parameters.

For example, the user can perform `cellCellDecomp` with different ranks, and also can peform `cellCellReport` with omiting some enrichment analysis step as follows.

    suppressPackageStartupMessages(library("LRBase.Hsa.eg.db"))
    # 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)

# Session information

    ## R version 4.0.0 (2020-04-24)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] AnnotationHub_2.20.0
    ##  [2] BiocFileCache_1.12.0
    ##  [3] dbplyr_1.4.4
    ##  [4] Homo.sapiens_1.3.1
    ##  [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
    ##  [6] org.Hs.eg.db_3.11.4
    ##  [7] GO.db_3.11.4
    ##  [8] OrganismDbi_1.30.0
    ##  [9] GenomicFeatures_1.40.0
    ## [10] AnnotationDbi_1.50.1
    ## [11] MeSH.Mmu.eg.db_1.13.0
    ## [12] LRBase.Mmu.eg.db_1.2.0
    ## [13] MeSH.Hsa.eg.db_1.13.0
    ## [14] MeSHDbi_1.24.0
    ## [15] SingleCellExperiment_1.10.1
    ## [16] SummarizedExperiment_1.18.1
    ## [17] DelayedArray_0.14.0
    ## [18] matrixStats_0.56.0
    ## [19] Biobase_2.48.0
    ## [20] GenomicRanges_1.40.0
    ## [21] GenomeInfoDb_1.24.2
    ## [22] IRanges_2.22.2
    ## [23] S4Vectors_0.26.1
    ## [24] BiocGenerics_0.34.0
    ## [25] scTensor_1.4.3
    ## [26] RSQLite_2.2.0
    ## [27] LRBase.Hsa.eg.db_1.2.0
    ## [28] LRBaseDbi_1.6.0
    ## [29] BiocStyle_2.16.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] rsvd_1.0.3                    Hmisc_4.4-0
    ##   [3] ica_1.0-2                     Rsamtools_2.4.0
    ##   [5] foreach_1.5.0                 lmtest_0.9-37
    ##   [7] crayon_1.3.4                  MASS_7.3-51.6
    ##   [9] nlme_3.1-148                  backports_1.1.8
    ##  [11] GOSemSim_2.14.0               rlang_0.4.6
    ##  [13] XVector_0.28.0                ROCR_1.0-11
    ##  [15] irlba_2.3.3                   nnTensor_1.0.5
    ##  [17] GOstats_2.54.0                BiocParallel_1.22.0
    ##  [19] tagcloud_0.6                  bit64_0.9-7
    ##  [21] glue_1.4.1                    sctransform_0.2.1
    ##  [23] dotCall64_1.0-0               DOSE_3.14.0
    ##  [25] tidyselect_1.1.0              fitdistrplus_1.1-1
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    ##  [29] zoo_1.8-8                     GenomicAlignments_1.24.0
    ##  [31] xtable_1.8-4                  magrittr_1.5
    ##  [33] evaluate_0.14                 ggplot2_3.3.2
    ##  [35] zlibbioc_1.34.0               rstudioapi_0.11
    ##  [37] rpart_4.1-15                  fastmatch_1.1-0
    ##  [39] ensembldb_2.12.1              maps_3.3.0
    ##  [41] fields_10.3                   shiny_1.5.0
    ##  [43] xfun_0.15                     askpass_1.1
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    ##  [59] bitops_1.0-6                  ggforce_0.3.2
    ##  [61] RBGL_1.64.0                   plyr_1.8.6
    ##  [63] GSEABase_1.50.1               AnnotationFilter_1.12.0
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    ##  [93] AnnotationForge_1.30.1        later_1.1.0.1
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