cellCellSimulate functionHere, we explain the way to generate CCI simulation data. scTensor
has a function cellCellSimulate to generate the simulation
data.
The simplest way to generate such data is
cellCellSimulate with default parameters.
## Getting the values of params...## Setting random seed...## Generating simulation data...## Done!This function internally generate the parameter sets by
newCCSParams, and the values of the parameter can be
changed, and specified as the input of cellCellSimulate by
users as follows.
## Formal class 'CCSParams' [package "scTensor"] with 5 slots
## ..@ nGene : num 1000
## ..@ nCell : num [1:3] 50 50 50
## ..@ cciInfo:List of 4
## .. ..$ nPair: num 500
## .. ..$ CCI1 :List of 4
## .. .. ..$ LPattern: num [1:3] 1 0 0
## .. .. ..$ RPattern: num [1:3] 0 1 0
## .. .. ..$ nGene : num 50
## .. .. ..$ fc : chr "E10"
## .. ..$ CCI2 :List of 4
## .. .. ..$ LPattern: num [1:3] 0 1 0
## .. .. ..$ RPattern: num [1:3] 0 0 1
## .. .. ..$ nGene : num 50
## .. .. ..$ fc : chr "E10"
## .. ..$ CCI3 :List of 4
## .. .. ..$ LPattern: num [1:3] 0 0 1
## .. .. ..$ RPattern: num [1:3] 1 0 0
## .. .. ..$ nGene : num 50
## .. .. ..$ fc : chr "E10"
## ..@ lambda : num 1
## ..@ seed : num 1234# Setting different parameters
# No. of genes : 1000
setParam(params, "nGene") <- 1000
# 3 cell types, 20 cells in each cell type
setParam(params, "nCell") <- c(20, 20, 20)
# Setting for Ligand-Receptor pair list
setParam(params, "cciInfo") <- list(
nPair=500, # Total number of L-R pairs
# 1st CCI
CCI1=list(
LPattern=c(1,0,0), # Only 1st cell type has this pattern
RPattern=c(0,1,0), # Only 2nd cell type has this pattern
nGene=50, # 50 pairs are generated as CCI1
fc="E10"), # Degree of differential expression (Fold Change)
# 2nd CCI
CCI2=list(
LPattern=c(0,1,0),
RPattern=c(0,0,1),
nGene=30,
fc="E100")
)
# Degree of Dropout
setParam(params, "lambda") <- 10
# Random number seed
setParam(params, "seed") <- 123
# Simulation data
sim <- cellCellSimulate(params)## Getting the values of params...## Setting random seed...## Generating simulation data...## Done!The output object sim has some attributes as follows.
Firstly, sim$input contains a synthetic gene expression matrix. The size can be changed by nGene and nCell parameters described above.
## [1] 1000 60## Cell1 Cell2 Cell3
## Gene1 9105 2 0
## Gene2 4 37 850Next, sim$LR contains a ligand-receptor (L-R) pair list. The size can be changed by nPair parameter of cciInfo, and the differentially expressed (DE) L-R pairs are saved in the upper side of this matrix. Here, two DE L-R patterns are specified as cciInfo, and each number of pairs is 50 and 30, respectively.
## [1] 500 2## GENEID_L GENEID_R
## 1 Gene1 Gene81
## 2 Gene2 Gene82
## 3 Gene3 Gene83
## 4 Gene4 Gene84
## 5 Gene5 Gene85
## 6 Gene6 Gene86
## 7 Gene7 Gene87
## 8 Gene8 Gene88
## 9 Gene9 Gene89
## 10 Gene10 Gene90## GENEID_L GENEID_R
## 46 Gene46 Gene126
## 47 Gene47 Gene127
## 48 Gene48 Gene128
## 49 Gene49 Gene129
## 50 Gene50 Gene130
## 51 Gene51 Gene131
## 52 Gene52 Gene132
## 53 Gene53 Gene133
## 54 Gene54 Gene134
## 55 Gene55 Gene135## GENEID_L GENEID_R
## 491 Gene571 Gene991
## 492 Gene572 Gene992
## 493 Gene573 Gene993
## 494 Gene574 Gene994
## 495 Gene575 Gene995
## 496 Gene576 Gene996
## 497 Gene577 Gene997
## 498 Gene578 Gene998
## 499 Gene579 Gene999
## 500 Gene580 Gene1000Finally, sim$celltypes contains a cell type vector. Since nCell is specified as “c(20, 20, 20)” described above, three cell types are generated.
## [1] 60## Celltype1 Celltype1 Celltype1 Celltype1 Celltype1 Celltype1
## "Cell1" "Cell2" "Cell3" "Cell4" "Cell5" "Cell6"##
## Celltype1 Celltype2 Celltype3
## 20 20 20## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## 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
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scTGIF_1.21.0
## [2] Homo.sapiens_1.3.1
## [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [4] org.Hs.eg.db_3.20.0
## [5] GO.db_3.20.0
## [6] OrganismDbi_1.49.0
## [7] GenomicFeatures_1.59.1
## [8] AnnotationDbi_1.69.0
## [9] SingleCellExperiment_1.29.1
## [10] SummarizedExperiment_1.37.0
## [11] Biobase_2.67.0
## [12] GenomicRanges_1.59.1
## [13] GenomeInfoDb_1.43.4
## [14] IRanges_2.41.2
## [15] S4Vectors_0.45.2
## [16] MatrixGenerics_1.19.1
## [17] matrixStats_1.5.0
## [18] scTensor_2.17.0
## [19] RSQLite_2.3.9
## [20] LRBaseDbi_2.17.0
## [21] AnnotationHub_3.15.0
## [22] BiocFileCache_2.15.1
## [23] dbplyr_2.5.0
## [24] BiocGenerics_0.53.5
## [25] generics_0.1.3
## [26] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.5 bitops_1.0-9 enrichplot_1.27.4
## [4] httr_1.4.7 webshot_0.5.5 RColorBrewer_1.1-3
## [7] Rgraphviz_2.51.0 tools_4.4.2 backports_1.5.0
## [10] R6_2.5.1 lazyeval_0.2.2 withr_3.0.2
## [13] prettyunits_1.2.0 graphite_1.53.0 gridExtra_2.3
## [16] schex_1.21.0 fdrtool_1.2.18 cli_3.6.3
## [19] TSP_1.2-4 entropy_1.3.1 sass_0.4.9
## [22] genefilter_1.89.0 meshr_2.13.0 Rsamtools_2.23.1
## [25] yulab.utils_0.1.9 txdbmaker_1.3.1 gson_0.1.0
## [28] DOSE_4.1.0 R.utils_2.12.3 MeSHDbi_1.43.0
## [31] AnnotationForge_1.49.0 nnTensor_1.3.0 plotrix_3.8-4
## [34] maps_3.4.2.1 visNetwork_2.1.2 gridGraphics_0.5-1
## [37] GOstats_2.73.0 BiocIO_1.17.1 dplyr_1.1.4
## [40] dendextend_1.19.0 Matrix_1.7-2 abind_1.4-8
## [43] R.methodsS3_1.8.2 lifecycle_1.0.4 yaml_2.3.10
## [46] qvalue_2.39.0 SparseArray_1.7.4 grid_4.4.2
## [49] blob_1.2.4 misc3d_0.9-1 crayon_1.5.3
## [52] ggtangle_0.0.6 lattice_0.22-6 msigdbr_7.5.1
## [55] cowplot_1.1.3 annotate_1.85.0 KEGGREST_1.47.0
## [58] sys_3.4.3 maketools_1.3.1 pillar_1.10.1
## [61] knitr_1.49 fgsea_1.33.2 tcltk_4.4.2
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## [73] treeio_1.31.0 spam_2.11-1 rTensor_1.4.8
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## [85] iterators_1.0.14 fields_16.3 nlme_3.1-166
## [88] Category_2.73.0 ggtree_3.15.0 bit64_4.6.0-1
## [91] progress_1.2.3 filelock_1.0.3 bslib_0.8.0
## [94] colorspace_2.1-1 DBI_1.2.3 tidyselect_1.2.1
## [97] bit_4.5.0.1 compiler_4.4.2 curl_6.2.0
## [100] httr2_1.1.0 graph_1.85.1 xml2_1.3.6
## [103] DelayedArray_0.33.4 plotly_4.10.4 rtracklayer_1.67.0
## [106] checkmate_2.3.2 scales_1.3.0 hexbin_1.28.5
## [109] RBGL_1.83.0 plot3D_1.4.1 rappdirs_0.3.3
## [112] stringr_1.5.1 digest_0.6.37 rmarkdown_2.29
## [115] ca_0.71.1 XVector_0.47.2 htmltools_0.5.8.1
## [118] pkgconfig_2.0.3 fastmap_1.2.0 rlang_1.1.5
## [121] htmlwidgets_1.6.4 UCSC.utils_1.3.1 farver_2.1.2
## [124] jquerylib_0.1.4 jsonlite_1.8.9 BiocParallel_1.41.0
## [127] GOSemSim_2.33.0 R.oo_1.27.0 RCurl_1.98-1.16
## [130] magrittr_2.0.3 GenomeInfoDbData_1.2.13 ggplotify_0.1.2
## [133] dotCall64_1.2 patchwork_1.3.0 munsell_0.5.1
## [136] Rcpp_1.0.14 babelgene_22.9 ape_5.8-1
## [139] viridis_0.6.5 stringi_1.8.4 tagcloud_0.6
## [142] ggraph_2.2.1 MASS_7.3-64 plyr_1.8.9
## [145] parallel_4.4.2 ggrepel_0.9.6 Biostrings_2.75.3
## [148] graphlayouts_1.2.2 splines_4.4.2 hms_1.1.3
## [151] igraph_2.1.4 buildtools_1.0.0 biomaRt_2.63.0
## [154] reshape2_1.4.4 BiocVersion_3.21.1 XML_3.99-0.18
## [157] evaluate_1.0.3 BiocManager_1.30.25 foreach_1.5.2
## [160] tweenr_2.0.3 tidyr_1.3.1 purrr_1.0.2
## [163] polyclip_1.10-7 heatmaply_1.5.0 ggplot2_3.5.1
## [166] ReactomePA_1.51.0 ggforce_0.4.2 xtable_1.8-4
## [169] restfulr_0.0.15 reactome.db_1.89.0 tidytree_0.4.6
## [172] viridisLite_0.4.2 tibble_3.2.1 aplot_0.2.4
## [175] ccTensor_1.0.2 memoise_2.0.1 registry_0.5-1
## [178] GenomicAlignments_1.43.0 cluster_2.1.8 concaveman_1.1.0
## [181] GSEABase_1.69.0