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 4.4.1 (2024-06-14)
## 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_1.20.0
## [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_1.59.0
## [8] AnnotationDbi_1.69.0
## [9] SingleCellExperiment_1.28.0
## [10] SummarizedExperiment_1.36.0
## [11] Biobase_2.67.0
## [12] GenomicRanges_1.59.0
## [13] GenomeInfoDb_1.43.0
## [14] IRanges_2.41.0
## [15] S4Vectors_0.44.0
## [16] MatrixGenerics_1.19.0
## [17] matrixStats_1.4.1
## [18] scTensor_2.17.0
## [19] RSQLite_2.3.7
## [20] LRBaseDbi_2.17.0
## [21] AnnotationHub_3.15.0
## [22] BiocFileCache_2.15.0
## [23] dbplyr_2.5.0
## [24] BiocGenerics_0.53.0
## [25] BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.5 bitops_1.0-9 enrichplot_1.27.1
## [4] httr_1.4.7 webshot_0.5.5 RColorBrewer_1.1-3
## [7] Rgraphviz_2.50.0 tools_4.4.1 backports_1.5.0
## [10] utf8_1.2.4 R6_2.5.1 lazyeval_0.2.2
## [13] withr_3.0.2 prettyunits_1.2.0 graphite_1.53.0
## [16] gridExtra_2.3 schex_1.20.0 fdrtool_1.2.18
## [19] cli_3.6.3 TSP_1.2-4 entropy_1.3.1
## [22] sass_0.4.9 genefilter_1.89.0 meshr_2.13.0
## [25] Rsamtools_2.22.0 yulab.utils_0.1.7 txdbmaker_1.2.0
## [28] gson_0.1.0 DOSE_4.1.0 R.utils_2.12.3
## [31] MeSHDbi_1.43.0 AnnotationForge_1.49.0 nnTensor_1.3.0
## [34] plotrix_3.8-4 maps_3.4.2 visNetwork_2.1.2
## [37] generics_0.1.3 gridGraphics_0.5-1 GOstats_2.73.0
## [40] BiocIO_1.17.0 dplyr_1.1.4 dendextend_1.18.1
## [43] Matrix_1.7-1 fansi_1.0.6 abind_1.4-8
## [46] R.methodsS3_1.8.2 lifecycle_1.0.4 yaml_2.3.10
## [49] qvalue_2.38.0 SparseArray_1.6.0 grid_4.4.1
## [52] blob_1.2.4 misc3d_0.9-1 crayon_1.5.3
## [55] ggtangle_0.0.4 lattice_0.22-6 msigdbr_7.5.1
## [58] cowplot_1.1.3 annotate_1.85.0 KEGGREST_1.47.0
## [61] sys_3.4.3 maketools_1.3.1 pillar_1.9.0
## [64] knitr_1.48 fgsea_1.33.0 tcltk_4.4.1
## [67] rjson_0.2.23 codetools_0.2-20 fastmatch_1.1-4
## [70] glue_1.8.0 outliers_0.15 ggfun_0.1.7
## [73] data.table_1.16.2 vctrs_0.6.5 png_0.1-8
## [76] treeio_1.30.0 spam_2.11-0 rTensor_1.4.8
## [79] gtable_0.3.6 assertthat_0.2.1 cachem_1.1.0
## [82] xfun_0.48 S4Arrays_1.6.0 mime_0.12
## [85] tidygraph_1.3.1 survival_3.7-0 seriation_1.5.6
## [88] iterators_1.0.14 fields_16.3 nlme_3.1-166
## [91] Category_2.73.0 ggtree_3.15.0 bit64_4.5.2
## [94] progress_1.2.3 filelock_1.0.3 bslib_0.8.0
## [97] colorspace_2.1-1 DBI_1.2.3 tidyselect_1.2.1
## [100] bit_4.5.0 compiler_4.4.1 curl_5.2.3
## [103] httr2_1.0.5 graph_1.85.0 xml2_1.3.6
## [106] DelayedArray_0.33.1 plotly_4.10.4 rtracklayer_1.66.0
## [109] checkmate_2.3.2 scales_1.3.0 hexbin_1.28.4
## [112] RBGL_1.82.0 plot3D_1.4.1 rappdirs_0.3.3
## [115] stringr_1.5.1 digest_0.6.37 rmarkdown_2.28
## [118] ca_0.71.1 XVector_0.46.0 htmltools_0.5.8.1
## [121] pkgconfig_2.0.3 highr_0.11 fastmap_1.2.0
## [124] rlang_1.1.4 htmlwidgets_1.6.4 UCSC.utils_1.2.0
## [127] farver_2.1.2 jquerylib_0.1.4 jsonlite_1.8.9
## [130] BiocParallel_1.41.0 GOSemSim_2.33.0 R.oo_1.26.0
## [133] RCurl_1.98-1.16 magrittr_2.0.3 GenomeInfoDbData_1.2.13
## [136] ggplotify_0.1.2 dotCall64_1.2 patchwork_1.3.0
## [139] munsell_0.5.1 Rcpp_1.0.13 babelgene_22.9
## [142] ape_5.8 viridis_0.6.5 stringi_1.8.4
## [145] tagcloud_0.6 ggraph_2.2.1 zlibbioc_1.52.0
## [148] MASS_7.3-61 plyr_1.8.9 parallel_4.4.1
## [151] ggrepel_0.9.6 Biostrings_2.75.0 graphlayouts_1.2.0
## [154] splines_4.4.1 hms_1.1.3 igraph_2.1.1
## [157] buildtools_1.0.0 biomaRt_2.63.0 reshape2_1.4.4
## [160] BiocVersion_3.21.1 XML_3.99-0.17 evaluate_1.0.1
## [163] BiocManager_1.30.25 foreach_1.5.2 tweenr_2.0.3
## [166] tidyr_1.3.1 purrr_1.0.2 polyclip_1.10-7
## [169] heatmaply_1.5.0 ggplot2_3.5.1 ReactomePA_1.50.0
## [172] ggforce_0.4.2 xtable_1.8-4 restfulr_0.0.15
## [175] reactome.db_1.89.0 tidytree_0.4.6 viridisLite_0.4.2
## [178] tibble_3.2.1 aplot_0.2.3 ccTensor_1.0.2
## [181] GenomicAlignments_1.43.0 memoise_2.0.1 registry_0.5-1
## [184] cluster_2.1.6 concaveman_1.1.0 GSEABase_1.69.0