{ "_id": "6acf6b39e2d863f662951de7", "Type": "Package", "Package": "BatchQC", "Title": "Batch Effects Quality Control Software", "Version": "2.8.3", "Date": "2026-08-28", "Authors@R": "c(\nperson(\"Jessica\", \"Anderson\", , \"anderson.jessica@rutgers.edu\",\nrole = c(\"aut\"), comment = c(ORCID = \"0000-0002-0542-9872\")),\nperson(\"W. Evan\", \"Johnson\", , \"wj183@njms.rutgers.edu\",\nrole = c(\"aut\", \"fnd\"), comment = c(ORCID = \"0000-0002-6247-6595\")),\nperson(\"Yaoan\", \"Leng\", , \"leng@bu.edu\", role = c(\"ctb\", \"cre\"),\ncomment = c(ORCID = \"0009-0002-3957-5250\")),\nperson(\"Solaiappan\", \"Manimaran\", , \"manimaran_1975@hotmail.com\", role = \"aut\"),\nperson(\"Heather\", \"Selby\", , \"selbyh@bu.edu\", role = \"ctb\"),\nperson(\"Claire\", \"Ruberman\", , \"claireruberman@gmail.com\", role = \"ctb\"),\nperson(\"Kwame\", \"Okrah\", , \"kwame.okrah@gmail.com\", role = \"ctb\"),\nperson(\"Hector Corrada\", \"Bravo\", , \"hcorrada@gmail.com\", role = \"ctb\"),\nperson(\"Michael\", \"Silverstein\", , \"msilver4@bu.edu\", role = \"ctb\"),\nperson(\"Regan\", \"Conrad\", , \"rdconrad@bu.edu\", role = \"ctb\"),\nperson(\"Zhaorong\", \"Li\", , \"zhaorong@bu.edu\", role = \"ctb\"),\nperson(\"Evan\", \"Holmes\", , \"evanjh19@gmail.com\", role = \"ctb\"),\nperson(\"Solomon\", \"Joseph\", , \"sj1136@njms.rutgers.edu\", role = \"ctb\"),\nperson(\"Howard\", \"Fan\", , \"hf268@njms.rutgers.edu\", role = \"ctb\"),\nperson(\"Sean\", \"Lu\", , \"sl1729@njms.rutgers.edu\", role = \"ctb\")\n)", "Description": "Samples (-omics, imaging, single cell, etc) often are\ncollected or processed in multiple batches or at different\ntimes. This often produces technical biases that can lead to\nincorrect results in the downstream analysis. BatchQC is a\nsoftware tool that streamlines batch preprocessing and\nevaluation by providing interactive diagnostics,\nvisualizations, and statistical analyses to explore the extent\nto which batch variation impacts the data. BatchQC diagnostics\nhelp determine whether batch adjustment needs to be done, and\nhow correction should be applied before proceeding with a\ndownstream analysis. Moreover, BatchQC interactively applies\nmultiple common batch effect approaches to the data and the\nuser can quickly see the benefits of each method. BatchQC is\ndeveloped as a Shiny App. The output is organized into multiple\ntabs and each tab features an important part of the batch\neffect analysis and visualization of the data. The BatchQC\ninterface has the following analysis groups: Summary,\nDifferential Expression, Median Correlations, Heatmaps,\nCircular Dendrogram, PCA Analysis, Shape, ComBat and SVA.", "License": "MIT + file LICENSE", "URL": "https://github.com/wejlab/BatchQC", "BugReports": "https://github.com/wejlab/BatchQC/issues", "VignetteBuilder": "knitr", "biocViews": "BatchEffect, DifferentialExpression, GeneExpression,\nGraphAndNetwork, ImmunoOncology, Microarray, Normalization,\nPreprocessing, PrincipalComponent, QualityControl, RNASeq,\nSequencing, ShinyApps, Software, Visualization", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE)", "Collate": "'Normalization.R' 'batch_correction.R' 'color_palette.R'\n'compute_aic.R' 'covariates_not_confounded.R' 'data.R'\n'dendrogram.R' 'dendrogram_alpha_numeric_check.R'\n'dendrogram_color_palette.R' 'differential_expression.R'\n'explained_variation.R' 'global_vars.R' 'heatmap.R'\n'heatmap_num_to_char_convertor.R' 'import.R' 'kBET-utils.R'\n'kBET.R' 'lambda_statistic.R' 'negative_binomial_check.R'\n'pca.R' 'preprocess.R' 'runApp.R' 'summary_statistics.R'\n'umap.R' 'variation_ratios.R' 'volcano_plot.R'", "Config/roxygen2/version": "8.0.0", "Config/pak/sysreqs": "cmake libfontconfig1-dev libfreetype6-dev\nlibfribidi-dev libglpk-dev make libharfbuzz-dev libicu-dev\nlibjpeg-dev libpng-dev libtiff-dev libuv1-dev libwebp-dev\nlibxml2-dev libssl-dev python3 libx11-dev zlib1g-dev", "Repository": "Bioconductor 3.23", "Date/Publication": "2026-09-14 19:54:49 UTC", "RemoteUrl": "https://github.com/bioc/BatchQC", "RemoteRef": "RELEASE_3_23", "RemoteSha": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-14 11:33:37 UTC", "User": "root" }, "Author": "Jessica Anderson [aut] (ORCID: ),\nW. Evan Johnson [aut, fnd] (ORCID:\n),\nYaoan Leng [ctb, cre] (ORCID: ),\nSolaiappan Manimaran [aut],\nHeather Selby [ctb],\nClaire Ruberman [ctb],\nKwame Okrah [ctb],\nHector Corrada Bravo [ctb],\nMichael Silverstein [ctb],\nRegan Conrad [ctb],\nZhaorong Li [ctb],\nEvan Holmes [ctb],\nSolomon Joseph [ctb],\nHoward Fan [ctb],\nSean Lu [ctb]", "Maintainer": "Yaoan Leng ", "_user": "bioc-release", "_type": "src", "_file": "BatchQC_2.8.3.tar.gz", "_fileid": "https://r2.ropensci.org/46f5001875d1d77b8bb01d48112c130284129a9adb32acc64252d80eb69fd9d7", "_filesize": 3157255, "_sha256": "46f5001875d1d77b8bb01d48112c130284129a9adb32acc64252d80eb69fd9d7", "_expires": "2027-01-22T11:44:55.000Z", "_created": "2026-10-14T11:33:37.000Z", "_published": "2026-10-14T11:44:57.160Z", "_buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024", "_status": "success", "_host": "GitHub-Actions", "_bioccheck": { "error": 0, "warning": 0, "note": 3 }, "_upstream": "https://github.com/bioc/BatchQC", "_commit": { "id": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "author": "technophilic03 ", "committer": "technophilic03 ", "message": "Bump version to 2.8.3\n", "time": 1789415689 }, "_maintainer": { "name": "Yaoan Leng", "email": "leng@bu.edu", "login": "technophilic03", "description": "", "uuid": 160270307, "orcid": "0009-0002-3957-5250" }, "_distro": "resolute", "_jobs": [ { "job": 115830189233, "time": 235, "config": "bioc-checks", "r": "4.6.1", "check": "NOTE", "artifact": "11890910898" }, { "job": 115830189401, "time": 595, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11892115501" }, { "job": 115830189275, "time": 580, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11891710732" }, { "job": 115830189220, "time": 418, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11891076201" }, { "job": 115830189242, "time": 466, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11891475586" }, { "job": 115827490309, "time": 412, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11890593769" }, { "job": 115830189214, "time": 202, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11890830826" }, { "job": 115830189329, "time": 616, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11890866918" }, { "job": 115830189322, "time": 558, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11891540838" }, { "job": 115830189308, "time": 599, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11890931916" } ], "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.5.0", "role": "Depends" }, { "package": "data.table", "role": "Imports" }, { "package": "DESeq2", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "EBSeq", "role": "Imports" }, { "package": "edgeR", "role": "Imports" }, { "package": "FNN", "role": "Imports" }, { "package": "ggdendro", "role": "Imports" }, { "package": "ggnewscale", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "ggpubr", "role": "Imports" }, { "package": "Harman", "role": "Imports" }, { "package": "limma", "role": "Imports" }, { "package": "matrixStats", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "pheatmap", "role": "Imports" }, { "package": "RColorBrewer", "role": "Imports" }, { "package": "reader", "role": "Imports" }, { "package": "reshape2", "role": "Imports" }, { "package": "scran", "role": "Imports" }, { "package": "shiny", "role": "Imports" }, { "package": "shinyjs", "role": "Imports" }, { "package": "shinythemes", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "SummarizedExperiment", "role": "Imports" }, { "package": "sva", "role": "Imports" }, { "package": "S4Vectors", "role": "Imports" }, { "package": "tibble", "role": "Imports" }, { "package": "tidyr", "role": "Imports" }, { "package": "tidyverse", "role": "Imports" }, { "package": "umap", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "BiocManager", "role": "Suggests" }, { "package": "BiocStyle", "role": "Suggests" }, { "package": "bladderbatch", "role": "Suggests" }, { "package": "curatedTBData", "role": "Suggests" }, { "package": "devtools", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "lintr", "role": "Suggests" }, { "package": "MultiAssayExperiment", "role": "Suggests" }, { "package": "plotly", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "bioc", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2025-42", "n": 1 }, { "week": "2025-47", "n": 1 }, { "week": "2025-49", "n": 1 }, { "week": "2025-51", "n": 1 }, { "week": "2026-03", "n": 3 }, { "week": "2026-05", "n": 3 }, { "week": "2026-06", "n": 3 }, { "week": "2026-07", "n": 2 }, { "week": "2026-18", "n": 1 }, { "week": "2026-34", "n": 5 }, { "week": "2026-35", "n": 3 }, { "week": "2026-38", "n": 2 } ], "_tags": [], "_bioc": [ { "branch": "devel", "version": "2.9.3", "bioc": "3.24", "repository": "bioc" }, { "branch": "release", "version": "2.8.3", "bioc": "3.23", "repository": "bioc" } ], "_topics": [ "batcheffect", "differentialexpression", "geneexpression", "graphandnetwork", "immunooncology", "microarray", "normalization", "preprocessing", "principalcomponent", "qualitycontrol", "rnaseq", "sequencing", "shinyapps", "software", "visualization" ], "_stars": 11, "_contributors": [ { "user": "jessmcc22", "count": 432, "uuid": 102758461 }, { "user": "mani2012", "count": 283, "uuid": 9969212 }, { "user": "technophilic03", "count": 97, "uuid": 160270307 }, { "user": "solomonsjoseph", "count": 72, "uuid": 36000051 }, { "user": "michaelsilverstein", "count": 61, "uuid": 22639569 }, { "user": "rdconrad", "count": 57, "uuid": 73078126 }, { "user": "zha0rong", "count": 39, "uuid": 34935078 }, { "user": "hjfan527", "count": 22, "uuid": 26697227 }, { "user": "wevanjohnson", "count": 18, "uuid": 9968444 }, { "user": "jwokaty", "count": 16, "uuid": 1744257 }, { "user": "nturaga", "count": 14, "uuid": 2746443 }, { "user": "tfaits", "count": 13, "uuid": 12431337 }, { "user": "selbyh", "count": 6, "uuid": 2808394 }, { "user": "seanlu96", "count": 6, "uuid": 89270441 }, { "user": "dtenenba", "count": 4, "uuid": 2286826 }, { "user": "hcorrada", "count": 4, "uuid": 2072476 }, { "user": "aubreyodom", "count": 2, "uuid": 25541738 }, { "user": "hpages", "count": 2, "uuid": 8810451 }, { "user": "claireruberman", "count": 2, "uuid": 5376094 }, { "user": "vobencha", "count": 2, "uuid": 2466173 }, { "user": "link-ny", "count": 1, "uuid": 4392950 }, { "user": "zhangyuqing", "count": 1, "uuid": 8079770 }, { "user": "evanjh19", "count": 1, "uuid": 98492526 } ], "_userbio": { "uuid": 2286807, "type": "organization", "name": "Bioconductor", "followers": 477, "description": "Software for the analysis and comprehension of high-throughput genomic data" }, "_downloads": { "count": 582, "source": "https://www.bioconductor.org/packages/stats/bioc/BatchQC" }, "_mentions": 8, "_devurl": "https://github.com/wejlab/batchqc", "_searchresults": 65, "_rbuild": "4.6.1", "_assets": [ "extra/BatchQC.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_homeurl": "https://github.com/wejlab/batchqc", "_realowner": "bioc", "_cranurl": false, "_exports": [ "batch_correct", "batch_design", "BatchQC", "batchqc_explained_variation", "bisect", "bladder_data_upload", "color_palette", "compute_aic", "compute_lambda", "confound_metrics", "cor_props", "covariates_not_confounded", "cramers_v", "DE_analyze", "dendrogram_alpha_numeric_check", "dendrogram_color_palette", "dendrogram_plotter", "EV_plotter", "EV_table", "goodness_of_fit_nb", "heatmap_num_to_char_converter", "heatmap_plotter", "is_design_balanced", "kBET", "normalize_SE", "PCA_plotter", "plot_kBET", "process_dendrogram", "pval_plotter", "pval_summary", "ratio_plotter", "run_kBET", "run_lambda", "std_pearson_corr_coef", "summarized_experiment", "summary_stats_EV_table", "tb_data_upload", "umap", "variation_ratios", "volcano_plot" ], "_datasets": [ { "name": "batch_indicator", "title": "Batch and Condition indicator for signature data", "object": "batch_indicator", "file": "batch_indicator.rda", "class": [ "data.frame" ], "fields": [ "batch", "condition" ], "rows": 89, "table": true, "tojson": true }, { "name": "merged_IDs", "title": "BMI and matched sample names for TB data", "object": "merged_IDs", "file": "merged_IDs.rda", "class": [ "data.frame" ], "fields": [ "subjectID_curatedTBData", "subjectID_TB_Paper", "BMI" ], "rows": 91, "table": true, "tojson": true }, { "name": "protein_data", "title": "Protein data with 39 protein expression levels", "object": "protein_data", "file": "protein_data.rda", "class": [ "data.frame" ], "fields": [ "X21591S2", "X21731S2", "X21671S2", "X20921S2", "X22131S2", "X22461S2", "X22171S2", "X23591S2", "X22511S2", "X24061S2", "X20811S2", "X23441S2", "X23851S2", "X21941S2", "X23671S2", "X20551S2", "X20681S2", "X20771S1", "X21111S1", "X22001S2", "X21601S2", "X21721S2", "X22191S2", "X22891S2" ], "rows": 39, "table": true, "tojson": true }, { "name": "protein_sample_info", "title": "Batch and Condition indicator for protein expression data", "object": "protein_sample_info", "file": "protein_sample_info.rda", "class": [ "data.frame" ], "fields": [ "batch", "category" ], "rows": 24, "table": true, "tojson": true }, { "name": "signature_data", "title": "Signature data with 1600 gene expression levels", "object": "signature_data", "file": "signature_data.rda", "class": [ "data.frame" ], "fields": [ "Control.1", "Control.2", "Control.3", "Control.4", "Control.5", "Control.6", "Pathway1.7", "Pathway1.8", "Pathway1.9", "Pathway1.10", "Pathway1.11", "Pathway1.12", "Control.13", "Control.14", "Control.15", "Control.16", "Control.17", "Control.18", "Control.19", "Control.20", "Control.21", "Control.22", "Control.23", "Control.24", "Pathway2.25", "Pathway2.26", "Pathway2.27", "Pathway2.28", "Pathway2.29", "Pathway2.30", "Pathway3.31", "Pathway3.32", "Pathway3.33", "Pathway3.34", "Pathway3.35", "Pathway3.36", "Pathway4.37", "Pathway4.38", "Pathway4.39", "Pathway4.40", "Pathway4.41", "Pathway5.42", "Pathway5.43", "Pathway5.44", "Pathway5.45", "Pathway5.46", "Pathway5.47", "Pathway6.48", "Pathway6.49", "Pathway6.50", "Pathway6.51", "Pathway6.52", "Pathway6.53", "Control.54", "Control.55", "Control.56", "Control.57", "Control.58", "Control.59", "Control.60", "Control.61", "Control.62", "Pathway7.63", "Pathway7.64", "Pathway7.65", "Pathway7.66", "Pathway7.67", "Pathway7.68", "Pathway7.69", "Pathway7.70", "Pathway7.71", "Pathway8.72", "Pathway8.73", "Pathway8.74", "Pathway8.75", "Pathway8.76", "Pathway8.77", "Pathway8.78", "Pathway8.79", "Pathway8.80", "Pathway9.81", "Pathway9.82", "Pathway9.83", "Pathway9.84", "Pathway9.85", "Pathway9.86", "Pathway9.87", "Pathway9.88", "Pathway9.89" ], "rows": 1600, "table": true, "tojson": true } ], "_help": [ { "page": "AIC_boxplots", "title": "Boxplots for the distribution of AIC for each method", "topics": [ "AIC_boxplots" ] }, { "page": "batch_correct", "title": "Batch Correct This function allows you to Add batch corrected count matrix to the SE object", "topics": [ "batch_correct" ] }, { "page": "batch_design", "title": "This function allows you to make a batch design matrix", "topics": [ "batch_design" ] }, { "page": "batch_indicator", "title": "Batch and Condition indicator for signature data", "topics": [ "batch_indicator" ] }, { "page": "BatchQC", "title": "Run BatchQC shiny app", "topics": [ "BatchQC" ] }, { "page": "batchqc_explained_variation", "title": "Returns a list of explained variation by batch and condition combinations", "topics": [ "batchqc_explained_variation" ] }, { "page": "bisect", "title": "bisect - a generic bisection function", "topics": [ "bisect" ] }, { "page": "bladder_data_upload", "title": "Bladder data upload This function uploads the Bladder data set from the bladderbatch package. This dataset is from bladder cancer data with 22,283 different microarray gene expression data. It has 57 bladder samples with 3 metadata variables (batch, outcome and cancer). It contains 5 batches, 3 cancer types (cancer, biopsy, control), and 5 outcomes (Biopsy, mTCC, sTCC-CIS, sTCC+CIS, and Normal). Batch 1 contains only cancer, 2 has cancer and controls, 3 has only controls, 4 contains only biopsy, and 5 contains cancer and biopsy", "topics": [ "bladder_data_upload" ] }, { "page": "BMI_data", "title": "This function returns BMI data that comes form the data in \"Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler\" paper. Subject IDs were matched as shown on \"github.com/jessmcc22/BatchQCv2_Manuscript/blob/devel/R/subjectID_match.R\"", "topics": [ "BMI_data" ] }, { "page": "check_valid_input", "title": "Helper function to save variables as factors if not already factors", "topics": [ "check_valid_input" ] }, { "page": "color_palette", "title": "Color palette", "topics": [ "color_palette" ] }, { "page": "ComBat_correction", "title": "ComBat Correction This function applies ComBat correction to your summarized experiment object", "topics": [ "ComBat_correction" ] }, { "page": "ComBat_seq_correction", "title": "ComBat-Seq Correction This function applies ComBat-seq correction to your summarized experiment object", "topics": [ "ComBat_seq_correction" ] }, { "page": "commentary", "title": "This function creates the commentary recommendation when there are more than 20 samples.", "topics": [ "commentary" ] }, { "page": "compute_aic", "title": "Compute the AIC for lognormal (ComBat) model, negative binomial (ComBat-seq) model and the Voom model", "topics": [ "compute_aic" ] }, { "page": "compute_lambda", "title": "Compute the lambda index for determining a need for batch correction", "topics": [ "compute_lambda" ] }, { "page": "confound_metrics", "title": "Combine std. Pearson correlation coefficient and Cramer's V", "topics": [ "confound_metrics" ] }, { "page": "cor_props", "title": "This function allows you to calculate correlation properties", "topics": [ "cor_props" ] }, { "page": "covariates_not_confounded", "title": "Returns list of covariates not confounded by batch; helper function for explained variation and for populating shiny app condition options", "topics": [ "covariates_not_confounded" ] }, { "page": "cramers_v", "title": "This function allows you to calculate Cramer's V", "topics": [ "cramers_v" ] }, { "page": "DE_analyze", "title": "Differential Expression Analysis", "topics": [ "DE_analyze" ] }, { "page": "dendrogram_alpha_numeric_check", "title": "Dendrogram alpha or numeric checker", "topics": [ "dendrogram_alpha_numeric_check" ] }, { "page": "dendrogram_color_palette", "title": "Dendrogram color palette", "topics": [ "dendrogram_color_palette" ] }, { "page": "dendrogram_plotter", "title": "Dendrogram Plot", "topics": [ "dendrogram_plotter" ] }, { "page": "DESeq_large_analysis", "title": "This function calculated the goodness of fit of DESeq2 for larger sample sizes (intended for more than 150 samples).", "topics": [ "DESeq_large_analysis" ] }, { "page": "DESeq2_small_size", "title": "This function calculated the goodness of fit of DESeq2 for small sample sizes (intended for less than 20 samples).", "topics": [ "DESeq2_small_size" ] }, { "page": "edgeR_large_analysis", "title": "This function calculated the goodness of fit of edgeR for larger sample sizes (intended for more than 150 samples).", "topics": [ "edgeR_large_analysis" ] }, { "page": "edgeR_small_size", "title": "This function calculated the goodness of fit of edgeR for small sample sizes (intended for less than or equal to 20 samples).", "topics": [ "edgeR_small_size" ] }, { "page": "EV_plotter", "title": "This function allows you to plot explained variation", "topics": [ "EV_plotter" ] }, { "page": "EV_table", "title": "EV Table Returns table with percent variation explained for specified number of genes", "topics": [ "EV_table" ] }, { "page": "get.res", "title": "Helper function to get residuals", "topics": [ "get.res" ] }, { "page": "goodness_of_fit_nb", "title": "This function calculates goodness-of-fit pvalues for all genes by looking at how the NB model by edgeR or DESeq2 fit the data", "topics": [ "goodness_of_fit_nb" ] }, { "page": "harman_correction", "title": "Harman Correction This function applies Harman correction to a summarized experiment object with and reconstructs the data back into the original feature space", "topics": [ "Harman_correction" ] }, { "page": "heatmap_num_to_char_converter", "title": "Heatmap numeric to character converter", "topics": [ "heatmap_num_to_char_converter" ] }, { "page": "heatmap_plotter", "title": "Heatmap Plotter", "topics": [ "heatmap_plotter" ] }, { "page": "is_design_balanced", "title": "Check if the experimental design is balanced or unbalanced", "topics": [ "is_design_balanced" ] }, { "page": "kBET", "title": "kBET - k-nearest neighbour batch effect test", "topics": [ "kBET" ] }, { "page": "limma_correction", "title": "Limma Correction This function applies limma batch correction to your provided assay", "topics": [ "limma_correction" ] }, { "page": "merged_IDs", "title": "BMI and matched sample names for TB data", "topics": [ "merged_IDs" ] }, { "page": "nb_histogram", "title": "This function creates a histogram from the negative binomial goodness-of-fit adjusted pvalues.", "topics": [ "nb_histogram" ] }, { "page": "nb_proportion", "title": "This function determines the proportion of p-values below a specific value and compares to the previously determined threshold", "topics": [ "nb_proportion" ] }, { "page": "normalize_SE", "title": "This function allows you to add normalized count matrix to the SE object", "topics": [ "normalize_SE" ] }, { "page": "PCA_plotter", "title": "PCA Plot", "topics": [ "PCA_plotter" ] }, { "page": "permuted_DESeq", "title": "This function performs DESeq on the permuted dataset.", "topics": [ "permuted_DESeq" ] }, { "page": "permuted_edgeR", "title": "This function performs edgeR on the permuted dataset adjusted pvalues.", "topics": [ "permuted_edgeR" ] }, { "page": "plot_data", "title": "This function formats the PCA plot using ggplot", "topics": [ "plot_data" ] }, { "page": "plot_kBET", "title": "kBET Rejection Plotter", "topics": [ "plot_kBET" ] }, { "page": "possible_distances", "title": "Create potential min_distance values for exploratory analysis based on the value of spread", "topics": [ "possible_distances" ] }, { "page": "possible_k_neighbors", "title": "Create a vector of possible nearest neighbor values from 5, 15, 25, 50, and 100", "topics": [ "possible_k_neighbors" ] }, { "page": "preprocess", "title": "Preprocess assay data", "topics": [ "preprocess" ] }, { "page": "process_dendrogram", "title": "Process Dendrogram", "topics": [ "process_dendrogram" ] }, { "page": "protein_data", "title": "Protein data with 39 protein expression levels", "topics": [ "protein_data" ] }, { "page": "protein_sample_info", "title": "Batch and Condition indicator for protein expression data", "topics": [ "protein_sample_info" ] }, { "page": "pval_plotter", "title": "P-value Plotter This function allows you to plot p-values of explained variation", "topics": [ "pval_plotter" ] }, { "page": "pval_summary", "title": "Returns summary table for p-values of explained variation", "topics": [ "pval_summary" ] }, { "page": "ratio_plotter", "title": "This function allows you to plot ratios of explained variation", "topics": [ "ratio_plotter" ] }, { "page": "run_AIC_models", "title": "Helper function that contains the code to run the lognormal, voom, and negative binomial AIC models for 'compute_aic'", "topics": [ "run_AIC_models" ] }, { "page": "run_kBET", "title": "kBET rejection rate", "topics": [ "run_kBET" ] }, { "page": "run_lambda", "title": "Provide a recommendation on batch correction based on lambda calculation", "topics": [ "run_lambda" ] }, { "page": "signature_data", "title": "Signature data with 1600 gene expression levels", "topics": [ "signature_data" ] }, { "page": "std_pearson_corr_coef", "title": "Calculate a standardized Pearson correlation coefficient", "topics": [ "std_pearson_corr_coef" ] }, { "page": "summarized_experiment", "title": "This function creates a summarized experiment object from count and metadata files uploaded by the user", "topics": [ "summarized_experiment" ] }, { "page": "summary_stats_EV_table", "title": "Summary Stats EV Table Returns table with min, 1st quartile, mean, 2nd quartile, and max for each variable in the explained variation boxplot", "topics": [ "summary_stats_EV_table" ] }, { "page": "sva_correction", "title": "sva Correction This function applies sva correction to a summarized experiment object (implementation adapted from sva::psva)", "topics": [ "sva_correction" ] }, { "page": "svaseq_correction", "title": "svaseq Correction This function applies sva correction to a summarized experiment object with count based RNA-seq data", "topics": [ "svaseq_correction" ] }, { "page": "tb_data_upload", "title": "TB data upload This function uploads the TB data set from the curatedTBData package.", "topics": [ "tb_data_upload" ] }, { "page": "umap", "title": "UMAP plot", "topics": [ "umap" ] }, { "page": "variation_ratios", "title": "Creates Ratios of batch to variable variation statistic", "topics": [ "variation_ratios" ] }, { "page": "volcano_plot", "title": "Volcano plot", "topics": [ "volcano_plot" ] } ], "_readme": "https://github.com/bioc/BatchQC/raw/RELEASE_3_23/README.md", "_rundeps": [ "abind", "annotate", "AnnotationDbi", "askpass", "assorthead", "backports", "base64enc", "beachmat", "BH", "Biobase", "BiocGenerics", "BiocNeighbors", "BiocParallel", "BiocSingular", "Biostrings", "bit", "bit64", "bitops", "blob", "blockmodeling", "bluster", "boot", "brio", "broom", "bslib", "cachem", "callr", "car", "carData", "caTools", "cellranger", "Ckmeans.1d.dp", "cli", "clipr", "cluster", "codetools", "colorspace", "commonmark", "conflicted", "corrplot", "cowplot", "cpp11", "crayon", "curl", "data.table", "DBI", "dbplyr", "DelayedArray", "Deriv", "desc", "DESeq2", "diffobj", "digest", "doBy", "dplyr", "dqrng", "dtplyr", "EBSeq", "edgeR", "evaluate", "farver", "fastmap", "FNN", "fontawesome", "forcats", "forecast", "formatR", "Formula", "fracdiff", "fs", "futile.logger", "futile.options", "gargle", "genefilter", "generics", "GenomicRanges", "ggdendro", "ggnewscale", "ggplot2", "ggpubr", "ggrepel", "ggsci", "ggsignif", "glue", "googledrive", "googlesheets4", "gplots", "gridExtra", "gtable", "gtools", "Harman", "haven", "here", "highr", "hms", "htmltools", "httpuv", "httr", "ids", "igraph", "IRanges", "irlba", "isoband", "jquerylib", "jsonlite", "KEGGREST", "KernSmooth", "knitr", "labeling", "lambda.r", "later", "lattice", "lifecycle", "limma", "lme4", "lmtest", "locfit", "lubridate", "magrittr", "MASS", "Matrix", "MatrixGenerics", "MatrixModels", "matrixStats", "memoise", "metapod", "mgcv", "mime", "minqa", "modelr", "NCmisc", "nlme", "nloptr", "nnet", "numDeriv", "openssl", "otel", "pbkrtest", "pheatmap", "pillar", "pkgbuild", "pkgconfig", "pkgload", "plyr", "png", "polynom", "praise", "prettyunits", "processx", "progress", "promises", "ps", "purrr", "quantreg", "R6", "ragg", "rappdirs", "rbibutils", "RColorBrewer", "Rcpp", "RcppArmadillo", "RcppEigen", "RcppTOML", "Rdpack", "reader", "readr", "readxl", "reformulas", "rematch", "rematch2", "reprex", "reshape2", "reticulate", "rlang", "rmarkdown", "rprojroot", "RSpectra", "RSQLite", "rstatix", "rstudioapi", "rsvd", "rvest", "S4Arrays", "S4Vectors", "S7", "sass", "ScaledMatrix", "scales", "scran", "scuttle", "selectr", "Seqinfo", "shiny", "shinyjs", "shinythemes", "SingleCellExperiment", "sitmo", "snow", "sourcetools", "SparseArray", "SparseM", "statmod", "stringi", "stringr", "SummarizedExperiment", "survival", "sva", "sys", "systemfonts", "testthat", "textshaping", "tibble", "tidyr", "tidyselect", "tidyverse", "timechange", "timeDate", "tinytex", "tzdb", "umap", "urca", "utf8", "uuid", "vctrs", "viridisLite", "vroom", "waldo", "withr", "xfun", "XML", "xml2", "xtable", "XVector", "yaml", "zoo" ], "_vignettes": [ { "source": "BatchQC_examples.Rmd", "filename": "BatchQC_examples.html", "title": "BatchQC Examples", "author": "W. Evan Johnson, Jessica Anderson, Solaiappan Manimaran", "engine": "knitr::rmarkdown", "headings": [ "Example 1: Protein Data", "Example 2: Signature Data", "Example 3: Bladderbatch Data", "Example 4: TB Data", "Session info" ], "created": "2023-08-03 21:05:18", "modified": "2026-08-18 22:33:09", "commits": 10 }, { "source": "BatchQC_Intro.Rmd", "filename": "BatchQC_Intro.html", "title": "Introduction to BatchQC", "author": "W. Evan Johnson, Jessica McClintock", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Installation", "Bioconductor Version", "Github Version", "Load BatchQC and Launch Shiny App", "Example Usage", "1. Upload data set", "2. Normalization/Batch Correction/Data Distribution Check", "Data Distribution Check", "AIC", "Negative Binomial GoF", "Apply Normalization methods", "Apply Batch Effect Correction", "3. Experimental Design", "Batch Design", "Confounding Statistics", "Pearson Correlation Coefficient", "Cramer's V", "4. lambda/Variation Analysis", "5. kBET", "6. Visualizations", "Heatmaps", "Sample Correlations", "Heatmap", "Dendrograms", "Dendrogram", "Circular Dendrogram", "PCA Analysis", "UMAP Analysis", "7. Differential Expression Analysis", "Volcano Plot", "8. Data Download", "Conclusion", "Session info" ], "created": "2022-04-29 18:43:15", "modified": "2026-08-28 19:07:51", "commits": 43 } ], "_score": 9.178999955662857, "_indexed": false, "_nocasepkg": "batchqc", "_universes": [ "bioc-release" ], "_indexurl": "https://bioc.r-universe.dev/BatchQC", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.8.3", "date": "2026-10-14T11:38:20.000Z", "distro": "resolute", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/64e35d83a828c774bb2a87f616057216e8c1c49de23cd82617dc6f4067a6d1b7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.6.1", "os": "linux", "version": "2.8.3", "date": "2026-10-14T11:38:19.000Z", "distro": "resolute", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/44d824d01970359320100b7a216a87ee0db1484a04abd6c514d85e96fc4aae88", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.5.3", "os": "mac", "version": "2.8.3", "date": "2026-10-14T11:35:59.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/4aa68b1e9a4870cf14a353d3c59807bbdc0115c14f869d5586648b2b79acddb0", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.6.1", "os": "mac", "version": "2.8.3", "date": "2026-10-14T11:36:17.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/3e84fe13c365a4c8bd779e4cdb7c59350536d67632487217c4ab60d7e8212213", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.6.0", "os": "wasm", "version": "2.8.3", "date": "2026-10-14T11:37:31.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/ca55c2e255aaa891a4ed114fdc2e48e58ecbe75a27e019a43b3917f236ed565a", "status": "success", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.7.0", "os": "win", "version": "2.8.3", "date": "2026-10-14T11:36:58.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/988963688d8f03c0066a53e7870ed9887b52cad8cea3392dab99146c72c68f45", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.5.3", "os": "win", "version": "2.8.3", "date": "2026-10-14T11:36:43.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/e498d0aac36b3606f8e3c188172d6035f99039ca0999a81ae2aa409c72e283c1", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" }, { "r": "4.6.1", "os": "win", "version": "2.8.3", "date": "2026-10-14T11:36:55.000Z", "commit": "9daabc96fa6b48fda3354cf80bb0991ab227512a", "fileid": "https://r2.ropensci.org/156ec89e9c1092a7efcfd2480c2cadee3a126d962814a30d3ef2d1abe6e050fe", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc-release/actions/runs/38556648024" } ] }