Package: BatchQC 2.1.2

Jessica McClintock

BatchQC: Batch Effects Quality Control Software

Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA.

Authors:Jessica McClintock [aut, cre], W. Evan Johnson [aut], Solaiappan Manimaran [aut], Heather Selby [ctb], Claire Ruberman [ctb], Kwame Okrah [ctb], Hector Corrada Bravo [ctb], Michael Silverstein [ctb], Regan Conrad [ctb], Zhaorong Li [ctb], Evan Holmes [aut], Solomon Joseph [ctb]

BatchQC_2.1.2.tar.gz
BatchQC_2.1.2.zip(r-4.5)BatchQC_2.1.2.zip(r-4.4)BatchQC_2.1.2.zip(r-4.3)
BatchQC_2.1.2.tgz(r-4.4-any)BatchQC_2.1.2.tgz(r-4.3-any)
BatchQC_2.1.2.tar.gz(r-4.5-noble)BatchQC_2.1.2.tar.gz(r-4.4-noble)
BatchQC_2.1.2.tgz(r-4.4-emscripten)BatchQC_2.1.2.tgz(r-4.3-emscripten)
BatchQC.pdf |BatchQC.html✨
BatchQC/json (API)
NEWS

# Install 'BatchQC' in R:

install.packages('BatchQC', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/wejlab/batchqc/issues

Datasets:

batch_indicator - Batch and Condition indicator for signature data
protein_data - Protein data with 39 protein expression levels
protein_sample_info - Batch and Condition indicator for protein expression data
signature_data - Signature data with 1600 gene expression levels

On BioConductor:BatchQC-2.1.2(bioc 3.20)BatchQC-2.0.0(bioc 3.19)

bioconductor-package

28 exports 2.57 score 200 dependencies 8 mentions

Last updated 2 months agofrom:8c3938e467

Exports:batch_correct batch_design BatchQC batchqc_explained_variation bladder_data_upload color_palette confound_metrics cor_props covariates_not_confounded cramers_v DE_analyze dendrogram_alpha_numeric_check dendrogram_color_palette dendrogram_plotter EV_plotter EV_table heatmap_num_to_char_converter heatmap_plotter normalize_SE PCA_plotter process_dendrogram pval_plotter pval_summary ratio_plotter std_pearson_corr_coef summarized_experiment variation_ratios volcano_plot

Dependencies:abind annotate AnnotationDbi askpass backports base64enc beachmat BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular Biostrings bit bit64 bitops blob blockmodeling bluster brio broom bslib cachem callr caTools cellranger cli clipr cluster codetools colorspace commonmark conflicted cpp11 crayon curl data.table DBI dbplyr DelayedArray DelayedMatrixStats desc DESeq2 diffobj digest dplyr dqrng dtplyr EBSeq edgeR evaluate fansi farver fastmap fontawesome forcats formatR fs futile.logger futile.options gargle genefilter generics GenomeInfoDb GenomeInfoDbData GenomicRanges ggdendro ggnewscale ggplot2 glue googledrive googlesheets4 gplots gtable gtools haven highr hms htmltools httpuv httr ids igraph IRanges irlba isoband jquerylib jsonlite KEGGREST KernSmooth knitr labeling lambda.r later lattice lifecycle limma locfit lubridate magrittr MASS Matrix MatrixGenerics matrixStats memoise metapod mgcv mime modelr munsell NCmisc nlme openssl pheatmap pillar pkgbuild pkgconfig pkgload plogr plyr png praise prettyunits processx progress promises ps purrr R6 ragg rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppHNSW reader readr readxl rematch rematch2 reprex reshape2 rlang rmarkdown rprojroot RSQLite rstudioapi rsvd rvest S4Arrays S4Vectors sass ScaledMatrix scales scran scuttle selectr shiny shinyjs shinythemes SingleCellExperiment sitmo snow sourcetools SparseArray sparseMatrixStats statmod stringi stringr SummarizedExperiment survival sva sys systemfonts testthat textshaping tibble tidyr tidyselect tidyverse timechange tinytex tzdb UCSC.utils utf8 uuid vctrs viridisLite vroom waldo withr xfun XML xml2 xtable XVector yaml zlibbioc

BatchQC Examples

W. Evan Johnson, Jessica McClintock, Solaiappan Manimaran

Rendered fromBatchQC_examples.Rmdusingknitr::rmarkdownon Jun 21 2024.

Last update: 2024-04-05
Started: 2023-08-03

Introduction to BatchQC

W. Evan Johnson, Jessica McClintock

Rendered fromBatchQC_Intro.Rmdusingknitr::rmarkdownon Jun 21 2024.

Last update: 2024-05-21
Started: 2022-04-29

Help page	Topics
Batch Correct This function allows you to Add batch corrected count matrix to the SE object	batch_correct
This function allows you to make a batch design matrix	batch_design
Batch and Condition indicator for signature data	batch_indicator
Run BatchQC shiny app	BatchQC
Returns a list of explained variation by batch and condition combinations	batchqc_explained_variation
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	bladder_data_upload
Helper function to check for valid input	check_valid_input
Color palette	color_palette
Combat Correction This function applies combat correction to your summarized experiment object	combat_correction
Combat-Seq Correction This function applies combat-seq correction to your summarized experiment object	combat_seq_correction
Combine std. Pearson correlation coefficient and Cramer's V	confound_metrics
This function allows you to calculate correlation properties	cor_props
Returns list of covariates not confounded by batch; helper function for explained variation and for populating shiny app condition options	covariates_not_confounded
This function allows you to calculate Cramer's V	cramers_v
Differential Expression Analysis	DE_analyze
Dendrogram alpha or numeric checker	dendrogram_alpha_numeric_check
Dendrogram color palette	dendrogram_color_palette
Dendrogram Plot	dendrogram_plotter
This function allows you to plot explained variation	EV_plotter
EV Table Returns table with percent variation explained for specified number of genes	EV_table
Helper function to get residuals	get.res
Heatmap numeric to character converter	heatmap_num_to_char_converter
Heatmap Plotter	heatmap_plotter
This function allows you to add normalized count matrix to the SE object	normalize_SE
This function allows you to plot PCA	PCA_plotter
This function formats the PCA plot using ggplot	plot_data
Preprocess assay data	preprocess
Process Dendrogram	process_dendrogram
Protein data with 39 protein expression levels	protein_data
Batch and Condition indicator for protein expression data	protein_sample_info
P-value Plotter This function allows you to plot p-values of explained variation	pval_plotter
Returns summary table for p-values of explained variation	pval_summary
This function allows you to plot ratios of explained variation	ratio_plotter
Signature data with 1600 gene expression levels	signature_data
Calculate a standardized Pearson correlation coefficient	std_pearson_corr_coef
This function creates a summarized experiment object from count and metadata files uploaded by the user	summarized_experiment
Creates Ratios of batch to variable variation statistic	variation_ratios
Volcano plot	volcano_plot

Package: BatchQC 2.1.2

BatchQC: Batch Effects Quality Control Software

BatchQC Examples

Introduction to BatchQC

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)