{ "_id": "6a103cf1acfb0bcc41c9c190", "Type": "Package", "Package": "BatchQC", "Title": "Batch Effects Quality Control Software", "Version": "2.9.0", "Date": "2026-02-10", "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": "Sequencing and microarray samples often are collected or\nprocessed in multiple batches or at different times. This often\nproduces technical biases that can lead to incorrect results in\nthe downstream analysis. BatchQC is a software tool that\nstreamlines batch preprocessing and evaluation by providing\ninteractive diagnostics, visualizations, and statistical\nanalyses to explore the extent to which batch variation impacts\nthe data. BatchQC diagnostics help determine whether batch\nadjustment needs to be done, and how correction should be\napplied before proceeding with a downstream analysis. Moreover,\nBatchQC interactively applies multiple common batch effect\napproaches to the data and the user can quickly see the\nbenefits of each method. BatchQC is developed as a Shiny App.\nThe output is organized into multiple tabs and each tab\nfeatures an important part of the batch effect analysis and\nvisualization of the data. The BatchQC interface has the\nfollowing analysis groups: Summary, Differential Expression,\nMedian Correlations, Heatmaps, Circular Dendrogram, PCA\nAnalysis, 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, GeneExpression, GraphAndNetwork, Microarray,\nNormalization, PrincipalComponent, Sequencing, Software,\nVisualization, QualityControl, RNASeq, Preprocessing,\nDifferentialExpression, ImmunoOncology", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "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/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": "https://bioc.r-universe.dev", "Date/Publication": "2026-04-28 12:43:18 UTC", "RemoteUrl": "https://github.com/bioc/BatchQC", "RemoteRef": "HEAD", "RemoteSha": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-11 10:05:58 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 ", "MD5sum": "fffe1452594d8c16bbd1116f487e7930", "_user": "bioc", "_type": "src", "_file": "BatchQC_2.9.0.tar.gz", "_fileid": "aedc8c07f62e0f6a28f802be5a9e71bddf1a2639464725e3fa23a51d017f0191", "_filesize": 3129744, "_sha256": "aedc8c07f62e0f6a28f802be5a9e71bddf1a2639464725e3fa23a51d017f0191", "_created": "2026-05-11T10:05:58.000Z", "_published": "2026-05-22T11:24:33.712Z", "_jobs": [ { "job": 77369530204, "time": 278, "config": "bioc-checks", "r": "4.6.0", "check": "NOTE", "artifact": "6915747430" }, { "job": 77369530116, "time": 624, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6915862076" }, { 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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": "This function allows you to plot PCA", "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": "Create a umap plot; wrapper function for umap package plus custom plotting", "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/HEAD/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", "microbenchmark", "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": "2025-11-21 18:03:15", "commits": 9 }, { "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": "2026-02-03 19:26:04", "commits": 41 } ], "_score": 9.387275898089937, "_indexed": true, "_nocasepkg": "batchqc", "_universes": [ "bioc", "technophilic03", "wejlab" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "2.9.0", "date": "2026-05-11T10:11:41.000Z", "distro": "noble", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "702c209d0ca1baabb47219d864caaf63888c7718e84c9de5028a0d5f5133bc3e", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.6.0", "os": "linux", "version": "2.9.0", "date": "2026-05-11T10:11:29.000Z", "distro": "noble", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "b994361e41a51b8620984ee89467c681bdbdd7cf210a38ff14c92cba4491e538", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.5.3", "os": "mac", "version": "2.9.0", "date": "2026-05-11T10:10:51.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "8b20c1571a44168109577d068b31b7fa0db1b01f4ca17f30ef8ff8532da498f4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.6.0", "os": "mac", "version": "2.9.0", "date": "2026-05-11T10:11:26.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "15d24f1682c55dab2033e95eee312c18d4552c19571b52c2a45d66fc55e95bf2", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.7.0", "os": "win", "version": "2.9.0", "date": "2026-05-11T10:11:23.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "7a5305605bf8a9114e97da18d216489ab57ce808ccea5741aa82973f3f34cbd7", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.5.3", "os": "win", "version": "2.9.0", "date": "2026-05-11T10:10:01.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "f9f65bb0b0a25907e96614ee2ecf9c0b7e427d66926d80111e253808b69d9baa", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.6.0", "os": "win", "version": "2.9.0", "date": "2026-05-11T10:10:14.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "f84129b0a907b8cee063907aa2a8fc02e0e1cbb1ac192462dbea4c13914381a4", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" }, { "r": "4.6.0", "os": "wasm", "version": "2.9.0", "date": "2026-05-22T11:24:01.000Z", "commit": "c30deb77cff40579d766f9cae59b0c94fd07dd86", "fileid": "190b36d98f48cffa4a86007e9844177dc54662bdb4a57aee7342b53bc20e9b6f", "status": "success", "buildurl": "https://github.com/r-universe/bioc/actions/runs/25663186243" } ] }