{"Package":"SCIBER","Type":"Package","Title":"Single-Cell Integrator and Batch Effect Remover","Version":"0.2.2","Authors@R":"c(person(given = \"Dailin\", family = \"Gan\", role = c(\"aut\", \"cre\"), email = \"dgan@nd.edu\", comment = c(ORCID = \"0000-0003-0535-1259\")), person(given = \"Jun\", family = \"Li\", role = \"aut\"))","Description":"Remove batch effects by projecting query batches into the reference batch space.","License":"MIT + file LICENSE","Encoding":"UTF-8","RoxygenNote":"7.2.1","LazyData":"true","URL":"https://github.com/RavenGan/SCIBER","BugReports":"https://github.com/RavenGan/SCIBER/issues","Imports":"dplyr, parallel, stats","Depends":"R (>= 2.10)","Suggests":"knitr, rmarkdown, testthat (>= 3.0.0), cowplot, ggplot2,\nggthemes, Matrix, uwot","Config/testthat/edition":"3","VignetteBuilder":"knitr","NeedsCompilation":"no","Packaged":"2023-05-02 01:26:15 UTC; david","Author":"Dailin Gan [aut, cre] (),\nJun Li [aut]","Maintainer":"Dailin Gan ","Repository":"CRAN","Date/Publication":"2023-05-02 08:10:12 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/SCIBER_0.2.2.tar.gz","jsonAuthors":[{"name":"Dailin Gan","email":"dgan@nd.edu","maintainer":true},{"name":"Jun Li","email":null,"maintainer":false}],"readme":"\n\n\n# SCIBER\n\n\n\n\nSCIBER is a simple method that outputs the batch-effect corrected\nexpression data in the original space/dimension. These expression data\nof individual genes can be directly used for all follow-up analyses.\nSCIBER has four steps; each step has a clear biological meaning, and the\nalgorithms used for them are k-means clustering, t-test, Fisher’s exact\ntest, and linear regression, respectively, all of which are easily\ncomprehensible\n\n## Installation\n\nYou can install the development version of SCIBER with the following\ninstructions:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"RavenGan/SCIBER\")\n```\n\n## Example\n\nThe following example uses the pre-processed Human dendritic cell\ndataset \\[1\\] to perform batch integration.\n\nPlease note that for each data frame in the object `meta`, there should\nbe two columns named `cell_id` and `cell_type`. For instance, let\n`meta_i` be a data frame under `meta`, and there should be two columns\n`meta_i$cell_id` and `meta_i$cell_type`. If the cell type information is\nnot available, any values put in `meta_i$cell_type` should work.\n\n``` r\nlibrary(SCIBER)\nrm(list = ls())\nset.seed(7)\ndata(HumanDC)\nexp <- HumanDC[[\"exp\"]]\nmeta <- HumanDC[[\"metadata\"]]\n\n# Specify the proportion for each query batch to integrate batches.\nomega <- c()\nomega[[1]] <- 0.6\n\nres <- SCIBER(input_batches = exp, ref_index = 1,\nbatches_meta_data = meta, omega = omega, n_core = 1)\n#> [1] \"The available number of cores is 10. SCIBER uses 1 to perform batch effect removal.\"\n```\n\n## Dataset reference\n\n1. Villani, A. C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar,\n K., Fletcher, J., … & Hacohen, N. (2017). Single-cell RNA-seq\n reveals new types of human blood dendritic cells, monocytes, and\n progenitors. Science, 356(6335), eaah4573.\n\n\n\n\n\n\n\n\n\n\n","jobInfo":{"package":"SCIBER","version":"0.2.2","parsingStatus":"success","parserVersion":2,"parsedAt":"2023-05-02T08:29:48+0000"}}

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