How to interpret the HTML report generated by cellCellReport function
23 September 2019
Package
scTensor 1.0.13
Contents
- 1 Introduction
- 2 Interpretation of “1. About scTensor Algorithm”
- 3 Interpretation of “2. Global statistics and plots”
- 4 Interpretation of “3. Ligand-Cell Patterns”
- 5 Interpretation of “4. Receptor-Cell Patterns”
- 6 Interpretation of “5. LR-pair Patterns”
- 7 Interpretation of “6. CCI-wise Hypergraph”
- 8 Interpretation of “7. Gene-wise Hypergraph”
- 9 Interpretation of “8. (Ligand-Cell, Receptor-Cell, LR-pair) Patterns”
- Session information
1 Introduction
Here, we explain the way to interpret of HTML report generated by cellCellReport.
If cellCellDecomp is properly finished, we can perform cellCellReport
function to output the HTML report.
The result can be confirmed by typing example(cellCellReport).
The report will be generated in the temporary directory (it costs 5 to 10 minutes).
Figure1 : HTML report of cellCellReport
2 Interpretation of “1. About scTensor Algorithm”
The 1st item describes the overview of scTensor and other CCI-related packages.
Figure2: 1. About scTensor Algorithm
3 Interpretation of “2. Global statistics and plots”
The 2nd item describes all the R objects saved in reanalysis.RData,
which contains the result of scTensor.
This file is saved in the output directory (out.dir) specified in cellCellReport,
and the user also can re-analyze the result of scTensor.
Figure3: 2. Global statistics and plots
Using plotly package, cellCellReport generates some interactive plots.
For example, in item 2.1, the number of cells in each cell type can be confirmed when the cursor moved on the box.
Figure4: 2.1 Number of cells in each celltype
In item 2.2, the number of expressed genes in each cell type (Non-zero genes) can be confirmed when the cursor moved on the box.
In item 2.3, two-dimensional plot user specified can be confirmed.
In item 2.4, distribution of core tensor values and the value of each (Ligand-Cell-type, Receptor-Cell-type, LR-pair) pattern can be confirmed.
The red bars mean that these values are selected by the threshold (thr parameters) in cellCellReport.
Note that the thr can be specified from 0 to 100, the large thr value will generate too many HTML files (cf. 8. (Ligand-Cell, Receptor-Cell, LR-pair) Patterns) and takes a long time.
Figure7 : 2.4 Distribution of core tensor values
The 3-order CCI-tensor consisting of Cell_L \(\times\) Cell_R \(\times\) LR-pair (LR) are decomposed by nnTensor, in which the tensor is iteratively matricised to mode-1 (Ligand-Cell direction), mode-2 (Receptor-Cell direction), and mode-3 direction (LR-pair direction).
In each direction, NMF is performed and the strength of each directional patterns are summarized in the bar plots.
For example, in item 2.5, distribution of mode-1 matricised tensor can be confirmed.
Figure8: 2.5 Distribution of mode-1 matricised tensor (Ligand-Cell Direction) (1/2)
Figure9: 2.5 Distribution of mode-1 matricised tensor (Ligand-Cell Direction) (2/2)
Likewise, in item 2.6, distribution of mode-2 matricised tensor can be confirmed,
Figure10 : 2.6 Distribution of mode-2 matricised tensor (Receptor-Cell Direction) (1/2)
Figure11 : 2.6 Distribution of mode-2 matricised tensor (Receptor-Cell Direction) (2/2)
and in item 2.7, distribution of mode-3 matricised tensor can be confirmed,
Figure12 : 2.7 Distribution of mode-3 matricised tensor (LR-pair Direction) (1/2)
Figure13 : 2.7 Distribution of mode-3 matricised tensor (LR-pair Direction) (2/2)
4 Interpretation of “3. Ligand-Cell Patterns”
In the 3rd item, using heatmap of plotly, user can interactively confirm the detail of Ligand-Cell Patterns extracted by nnTensor.
Figure14 : 3. Ligand-Cell Patterns
5 Interpretation of “4. Receptor-Cell Patterns”
Likewise, in the 4th item, the user can interactively confirm the detail of Receptor-Cell Patterns.
Figure15 : 4. Receptor-Cell Patterns
6 Interpretation of “5. LR-pair Patterns”
In the 5th item, the user can interactively confirm the detail of LR-pair Patterns.
Figure16 : 5. LR-pair Patterns
Since the LR-pairs patterns are messed up because of the length of vectors, in this plot (and also in other plotly plots), we recommend the user to try the zoom-in view of plotly. In this view, the user can confirm which LR-pattern have what kind of LR-pairs.
Figure17 : 5. LR-pair Patterns (Zoom in)
7 Interpretation of “6. CCI-wise Hypergraph”
In the 6th item describes, the strength between Ligand-Cell Patterns and Receptor-Cell Patterns (CCI-strength), by the summation of the core tensor with the mode-3 direction, a matrix consisting of the number of Ligand-Cell Patterns \(\times\) the number of Receptor-Cell Patterns.
8 Interpretation of “7. Gene-wise Hypergraph”
In the 7th item, the relationship between LR-pairs, which coexpressed in any LR-pair pattern at least one time. Ligand genes are described as red nodes, receptor genes are described as blue nodes, and corresponding LR-pair patterns are described as the color of edges.
Figure20 : 7. Gene-wise Hypergraph
Under the gene-wise hypergraph, four links are embedded.
In the 1st link, the details of gene-wise hypergraph can be confirmed as a corresponding table in a ligand gene-centric manner.
This page can work as reverse lookup search by “Ctrl + F”;
by typing the gene name of ligand that the user is interested in,
the partner receptors, which are coexpressed in some LR-pair patterns,
also can be found.
In the 2nd link, the user can find all the partner receptors,
even if the partner receptors are not coexpressed in any LR-pair pattern,
and if they are not included in the data matrix.
Likewise, the receptor gene-centric reverse search page is embedded in the 3rd link,
and, in the 4th link, all the partner ligand genes are included.
9 Interpretation of “8. (Ligand-Cell, Receptor-Cell, LR-pair) Patterns”
In the 8th item, the details of (Ligand-Cell, Receptor-Cell, LR-pair) Patterns are ordered by the size of core tensor, and the link of each pattern is embedded.
(Note that the number of the number of links is dependent on the thr parameter of cellCellReport.)
For example, the 1st link describes the details of (1,1,5) Pattern, which means the relationship of 1st pattern of Ligand-Cell patterns, 1st pattern of Receptor-Cell patterns, and 5th pattern of LR-pair patterns.
In this pattern, only one LR-pair is coexpressed (INSL3 and GNG11).
The hyperlinks to many databases and PubMed are also embedded.
The degree of the size of the LR-pair in the LR-pair pattern is quantified as P-value and Q-value.
Under the LR-pair list,
the results of many enrichment analysis is also embedded such as Gene Ontology (BP/MF/CC), Reactome, MeSH…etc.
User can confirm the detail of the result of scTensor, and perform the biological interpretation.
Session information
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [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
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] AnnotationHub_2.16.1
## [2] BiocFileCache_1.8.0
## [3] dbplyr_1.4.2
## [4] Homo.sapiens_1.3.1
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] org.Hs.eg.db_3.8.2
## [7] GO.db_3.8.2
## [8] OrganismDbi_1.26.0
## [9] GenomicFeatures_1.36.4
## [10] AnnotationDbi_1.46.1
## [11] MeSH.Mmu.eg.db_1.12.0
## [12] LRBase.Mmu.eg.db_1.1.0
## [13] MeSH.Hsa.eg.db_1.12.0
## [14] MeSHDbi_1.20.0
## [15] SingleCellExperiment_1.6.0
## [16] SummarizedExperiment_1.14.1
## [17] DelayedArray_0.10.0
## [18] BiocParallel_1.18.1
## [19] matrixStats_0.55.0
## [20] Biobase_2.44.0
## [21] GenomicRanges_1.36.1
## [22] GenomeInfoDb_1.20.0
## [23] IRanges_2.18.2
## [24] S4Vectors_0.22.1
## [25] BiocGenerics_0.30.0
## [26] scTensor_1.0.13
## [27] RSQLite_2.1.2
## [28] LRBase.Hsa.eg.db_1.1.0
## [29] LRBaseDbi_1.2.0
## [30] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1 rtracklayer_1.44.4
## [3] AnnotationForge_1.26.0 tidyr_1.0.0
## [5] ggplot2_3.2.1 acepack_1.4.1
## [7] bit64_0.9-7 knitr_1.25
## [9] data.table_1.12.2 rpart_4.1-15
## [11] RCurl_1.95-4.12 AnnotationFilter_1.8.0
## [13] cowplot_1.0.0 europepmc_0.3
## [15] bit_1.1-14 enrichplot_1.4.0
## [17] webshot_0.5.1 xml2_1.2.2
## [19] httpuv_1.5.2 assertthat_0.2.1
## [21] viridis_0.5.1 xfun_0.9
## [23] hms_0.5.1 evaluate_0.14
## [25] promises_1.0.1 TSP_1.1-7
## [27] progress_1.2.2 caTools_1.17.1.2
## [29] dendextend_1.12.0 Rgraphviz_2.28.0
## [31] igraph_1.2.4.1 DBI_1.0.0
## [33] htmlwidgets_1.3 MeSH.db_1.12.0
## [35] purrr_0.3.2 dplyr_0.8.3
## [37] backports_1.1.4 bookdown_0.13
## [39] annotate_1.62.0 biomaRt_2.40.4
## [41] vctrs_0.2.0 ensembldb_2.8.0
## [43] abind_1.4-5 ggforce_0.3.1
## [45] Gviz_1.28.3 triebeard_0.3.0
## [47] BSgenome_1.52.0 checkmate_1.9.4
## [49] GenomicAlignments_1.20.1 gclus_1.3.2
## [51] fdrtool_1.2.15 prettyunits_1.0.2
## [53] cluster_2.1.0 DOSE_3.10.2
## [55] dotCall64_1.0-0 lazyeval_0.2.2
## [57] crayon_1.3.4 genefilter_1.66.0
## [59] pkgconfig_2.0.3 tweenr_1.0.1
## [61] ProtGenerics_1.16.0 seriation_1.2-8
## [63] nnet_7.3-12 rlang_0.4.0
## [65] lifecycle_0.1.0 meshr_1.20.0
## [67] registry_0.5-1 MeSH.PCR.db_1.12.0
## [69] rTensor_1.4 GOstats_2.50.0
## [71] dichromat_2.0-0 polyclip_1.10-0
## [73] graph_1.62.0 Matrix_1.2-17
## [75] urltools_1.7.3 base64enc_0.1-3
## [77] ggridges_0.5.1 viridisLite_0.3.0
## [79] MeSH.AOR.db_1.12.0 bitops_1.0-6
## [81] visNetwork_2.0.8 KernSmooth_2.23-15
## [83] spam_2.3-0 MeSH.Bsu.168.eg.db_1.12.0
## [85] Biostrings_2.52.0 blob_1.2.0
## [87] stringr_1.4.0 qvalue_2.16.0
## [89] nnTensor_1.0.1 gridGraphics_0.4-1
## [91] reactome.db_1.68.0 scales_1.0.0
## [93] graphite_1.30.0 memoise_1.1.0
## [95] GSEABase_1.46.0 magrittr_1.5
## [97] plyr_1.8.4 gplots_3.0.1.1
## [99] gdata_2.18.0 zlibbioc_1.30.0
## [101] compiler_3.6.1 RColorBrewer_1.1-2
## [103] plotrix_3.7-6 Rsamtools_2.0.1
## [105] XVector_0.24.0 Category_2.50.0
## [107] MeSH.Aca.eg.db_1.12.0 htmlTable_1.13.2
## [109] Formula_1.2-3 MASS_7.3-51.4
## [111] tidyselect_0.2.5 stringi_1.4.3
## [113] highr_0.8 MeSH.Syn.eg.db_1.12.0
## [115] yaml_2.2.0 GOSemSim_2.10.0
## [117] latticeExtra_0.6-28 ggrepel_0.8.1
## [119] grid_3.6.1 VariantAnnotation_1.30.1
## [121] fastmatch_1.1-0 tools_3.6.1
## [123] rstudioapi_0.10 foreach_1.4.7
## [125] foreign_0.8-72 tagcloud_0.6
## [127] outliers_0.14 gridExtra_2.3
## [129] farver_1.1.0 ggraph_2.0.0
## [131] digest_0.6.21 rvcheck_0.1.3
## [133] BiocManager_1.30.4 shiny_1.3.2
## [135] Rcpp_1.0.2 later_0.8.0
## [137] httr_1.4.1 cummeRbund_2.26.0
## [139] biovizBase_1.32.0 colorspace_1.4-1
## [141] XML_3.98-1.20 splines_3.6.1
## [143] fields_9.8-6 RBGL_1.60.0
## [145] graphlayouts_0.5.0 ggplotify_0.0.4
## [147] plotly_4.9.0 xtable_1.8-4
## [149] jsonlite_1.6 heatmaply_0.16.0
## [151] tidygraph_1.1.2 UpSetR_1.4.0
## [153] zeallot_0.1.0 R6_2.4.0
## [155] Hmisc_4.2-0 pillar_1.4.2
## [157] htmltools_0.3.6 mime_0.7
## [159] glue_1.3.1 interactiveDisplayBase_1.22.0
## [161] codetools_0.2-16 maps_3.3.0
## [163] fgsea_1.10.1 lattice_0.20-38
## [165] tibble_2.1.3 curl_4.1
## [167] gtools_3.8.1 ReactomePA_1.28.0
## [169] misc3d_0.8-4 survival_2.44-1.1
## [171] rmarkdown_1.15 munsell_0.5.0
## [173] DO.db_2.9 GenomeInfoDbData_1.2.1
## [175] plot3D_1.1.1 iterators_1.0.12
## [177] reshape2_1.4.3 gtable_0.3.0