# Batch Effects Quality Control Software

## Documentation for package ‘BatchQC’ version 2.2.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

BatchQC | Run BatchQC shiny app
batchqc_explained_variation | Returns a list of explained variation by batch and condition combinations
batch_correct | Batch Correct This function allows you to Add batch corrected count matrix to the SE object
batch_design | This function allows you to make a batch design matrix
batch_indicator | Batch and Condition indicator for signature data

bladder_data_upload

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

check_valid_input | Helper function to save variables as factors if not already factors
color_palette | Color palette
combat_correction | Combat Correction This function applies combat correction to your summarized experiment object
combat_seq_correction | Combat-Seq Correction This function applies combat-seq correction to your summarized experiment object
confound_metrics | Combine std. Pearson correlation coefficient and Cramer's V
cor_props | This function allows you to calculate correlation properties
covariates_not_confounded | Returns list of covariates not confounded by batch; helper function for explained variation and for populating shiny app condition options
cramers_v | This function allows you to calculate Cramer's V
dendrogram_alpha_numeric_check | Dendrogram alpha or numeric checker
dendrogram_color_palette | Dendrogram color palette
dendrogram_plotter | Dendrogram Plot
DE_analyze | Differential Expression Analysis
EV_plotter | This function allows you to plot explained variation
EV_table | EV Table Returns table with percent variation explained for specified number of genes
get.res | Helper function to get residuals
goodness_of_fit_DESeq2 | This function calculates goodness-of-fit pvalues for all genes by looking at how the NB model by DESeq2 fit the data
heatmap_num_to_char_converter | Heatmap numeric to character converter
heatmap_plotter | Heatmap Plotter
nb_histogram | This function creates a histogram from the negative binomial goodness-of-fit pvalues.
nb_proportion | This function determines the proportion of p-values below a specific value and compares to the previously determined threshold of 0.42 for extreme low values.
normalize_SE | This function allows you to add normalized count matrix to the SE object
PCA_plotter | This function allows you to plot PCA
plot_data | This function formats the PCA plot using ggplot
preprocess | Preprocess assay data
process_dendrogram | Process Dendrogram
protein_data | Protein data with 39 protein expression levels
protein_sample_info | Batch and Condition indicator for protein expression data
pval_plotter | P-value Plotter This function allows you to plot p-values of explained variation
pval_summary | Returns summary table for p-values of explained variation
ratio_plotter | This function allows you to plot ratios of explained variation
signature_data | Signature data with 1600 gene expression levels
std_pearson_corr_coef | Calculate a standardized Pearson correlation coefficient
summarized_experiment | This function creates a summarized experiment object from count and metadata files uploaded by the user
variation_ratios | Creates Ratios of batch to variable variation statistic
volcano_plot | Volcano plot