{"Package":"CDSeq","Type":"Package","Title":"A Complete Deconvolution Method using Sequencing Data","Depends":"R (>= 3.6.0)","biocViews":"","Version":"1.0.8","Date":"2021-01-29","Authors@R":"c(person(\"Kai\", \"Kang\", email = \"kangkai0714@gmail.com\", role = c(\"aut\", \"cre\",\"cph\")),\nperson(\"Caizhi\", \"Huang\", email = \"chuang19@ncsu.edu\", role = \"aut\"),\nperson(\"Qian\",\"Meng\", role=\"ctb\"),\nperson(\"Igor\",\"Shats\", role=\"ctb\"),\nperson(\"Melissa\",\"Li\", role=\"ctb\"),\nperson(\"David\",\"Umbach\",role=\"ctb\"),\nperson(\"Leping\",\"Li\", role=c(\"aut\",\"cph\")),\nperson(\"Yuanyuan\",\"Li\", role=\"ctb\"),\nperson(\"Xiaoling\",\"Li\", role=\"ctb\"))","Maintainer":"Kai Kang ","Description":"Estimate cell-type-specific gene expression profiles and sample-specific cell-type proportions simultaneously using bulk sequencing data. Kang et al. (2019) .","License":"GPL-3","Imports":"Rcpp (>= 1.0.3), MASS, foreach, doParallel, dirmult,\nRcppThread, iterators, parallel, qlcMatrix, gplots, grDevices,\nclue, Biobase, Seurat, ggplot2, magrittr, dplyr, rlang, Matrix,\nmatrixStats, ggpubr","LinkingTo":"Rcpp, RcppArmadillo,","Encoding":"UTF-8","LazyData":"true","RoxygenNote":"7.1.1","Suggests":"knitr, rmarkdown, testthat (>= 3.0.0)","VignetteBuilder":"knitr","URL":"https://github.com/kkang7/CDSeq_R_Package,\nhttps://doi.org/10.1371/journal.pcbi.1007510","BugReports":"https://github.com/kkang7/CDSeq_R_Package/issues","Config/testthat/edition":"3","NeedsCompilation":"yes","Packaged":"2021-02-06 02:22:54 UTC; kaikang","Author":"Kai Kang [aut, cre, cph],\nCaizhi Huang [aut],\nQian Meng [ctb],\nIgor Shats [ctb],\nMelissa Li [ctb],\nDavid Umbach [ctb],\nLeping Li [aut, cph],\nYuanyuan Li [ctb],\nXiaoling Li [ctb]","Repository":"CRAN","Date/Publication":"2021-02-10 16:10:02 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/CDSeq_1.0.8.tar.gz","jsonAuthors":[{"name":"Kai Kang","email":"kangkai0714@gmail.com","maintainer":true},{"name":"Caizhi Huang","email":"chuang19@ncsu.edu","maintainer":false},{"name":"Qian Meng","email":null,"maintainer":false},{"name":"Igor Shats","email":null,"maintainer":false},{"name":"Melissa Li","email":null,"maintainer":false},{"name":"David Umbach","email":null,"maintainer":false},{"name":"Leping Li","email":null,"maintainer":false},{"name":"Yuanyuan Li","email":null,"maintainer":false},{"name":"Xiaoling Li","email":null,"maintainer":false}],"readme":"\n\n\n# CDSeq\n\n\n\n[![Travis build\nstatus](https://travis-ci.com/kkang7/CDSeq_R_Package.svg?branch=master)](https://travis-ci.com/kkang7/CDSeq_R_Package)\n\n\nCDSeq is a complete deconvolution method for dissecting bulk RNA-Seq\ndata. The input of CDSeq is, ideally, bulk RNA-Seq read counts (similar\nto the input format required by DESeq2), and CDSeq will estimate,\nsimultaneously, the cell-type-specific gene expression profiles and the\nsample-specific cell-type proportions, no reference of pure cell line\nGEPs or scRNAseq reference is needed for running CDSeq.\n\nFor example, if you have a bulk RNA-Seq data, a G by M matrix **A**,\nwhich is a G by M matrix. G denotes the number of genes and M is the\nsample size, then CDSeq will output **B** (a G by T matrix) and **C** (a\nT by M matrix), where T is the number of cell types, **B** is the\nestimate of cell-type-specific GEPs and **C** is the estimate of\nsample-specific cell-type proportions.\n\nImportantly, you can ask CDSeq to estimate the number of cell types,\ni.e. T, by providing a vector of possible integer values for T. For\nexample, if the user input for T is a vector, i.e. \$T=\\{2,3,4,5,6\\}\$,\nthen CDSeq will estimate the most likely number for T.\n\n## Installation\n\nYou can install the released version of CDSeq from\n[CRAN](https://CRAN.R-project.org) with:\n\n``` r\ninstall.packages(\"CDSeq\")\n```\n\nAnd the development version from [GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"kkang7/CDSeq_R_Package\")\n```\n\nbuild the vignette with\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"kkang7/CDSeq_R_Package\", build_vignettes = TRUE)\n```\n\n## Known issue about MacOS installation\n\nIt is possible for Mac users to run into some errors when install from\nsource due to problems of Rcpp compiler tools. Follow the instruction\nhere may help:\n\n\n## Example\n\nLoad package\n\n``` r\nlibrary(CDSeq)\n```\n\n### When the number of cell types is a scalar\n\n``` r\n## basic example code\nresult1<-CDSeq(bulk_data = mixtureGEP, \n cell_type_number = 6, \n mcmc_iterations = 5, # increase the mcmc_iterations to 700 or above\n gene_length = as.vector(gene_length), \n reference_gep = refGEP, # gene expression profile of pure cell lines\n cpu_number = 1)\n```\n\n### When the number of cell types is a vector\n\nThe *cell\\_type\\_number* can also be a vector which contains different\ninteger values. CDSeq will perform estimation for each integer in the\nvector and estimate the number of cell types in the mixtures. For\nexample, one can set *cell\\_type\\_number = 2:10* as follows, and CDSeq\nwill estimate the most likely number of cell types from 2 to 10.\n\n``` r\nresult2<-CDSeq(bulk_data = mixtureGEP, \n cell_type_number = 2:10, \n mcmc_iterations = 5, \n dilution_factor = 1, \n block_number = 1, \n gene_length = as.vector(gene_length), \n reference_gep = refGEP, # gene expression profile of pure cell lines\n cpu_number = 1, # use multiple cores to save time. Set the cpu_number = length(cell_type_number) if there is enough cores.\n print_progress_msg_to_file = 0)\n```\n\n### Use single cell to annotate CDSeq-estimated cell types\n\n``` r\ncdseq.result <- CDSeq::CDSeq(bulk_data = pbmc_mix,\n cell_type_number = seq(3,12,3),\n beta = 0.5,\n alpha = 5,\n mcmc_iterations = 700,\n cpu_number = 4,\n dilution_factor = 10)\n\ncdseq.result.celltypeassign <- cellTypeAssignSCRNA(cdseq_gep = cdseq.result$estGEP, # CDSeq-estimated cell-type-specific GEPs\n cdseq_prop = cdseq.result$estProp, # CDSeq-estimated cell type proportions\n sc_gep = sc_gep, # PBMC single cell data\n sc_annotation = sc_annotation,# PBMC single data annotations\n sc_pt_size = 3,\n cdseq_pt_size = 6,\n seurat_nfeatures = 100,\n seurat_npcs = 50,\n seurat_dims=1:5,\n plot_umap = 1,\n plot_tsne = 0)\n```\n\nCheck vignette for more details and examples:\n*browseVignettes(“CDSeq”)*.\n\n## Contact\n\nemail: ","jobInfo":{"package":"CDSeq","version":"1.0.8","parsingStatus":"success","parserVersion":1,"parsedAt":"2021-02-10T17:29:56+0000"}}

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