BatchQCout-class        The BatchQC output class to output BatchQC
                        results
batchQC                 Checks for presence of batch effect and creates
                        a html report with information including
                        whether the batch needs to be adjusted
batchQC_analyze         Checks for presence of batch effect and reports
                        whether the batch needs to be adjusted
batchQC_condition_adjusted
                        Returns adjusted data after remove the
                        variation across conditions
batchQC_filter_genes    Returns a datset after filtering genes of zero
                        variance across batch and condition
                        combinations
batchQC_fsva_adjusted   Use frozen surrogate variable analysis to
                        remove the surrogate variables inferred from
                        sva
batchQC_num.sv          Returns the number of surrogate variables to
                        estimate in the model using a permutation based
                        procedure
batchQC_shapeVariation
                        Perform Mean and Variance batch variation
                        analysis
batchQC_sva             Estimate the surrogate variables using the 2
                        step approach proposed by Leek and Storey 2007
batchQC_svregress_adjusted
                        Regress the surrogate variables out of the
                        expression data
batchqc_circosplot      Produce Circos plot
batchqc_correlation     Produce correlation heatmap plot
batchqc_corscatter      Produce Median Correlation plot
batchqc_explained_variation
                        Returns a list of explained variation by batch
                        and condition combinations
batchqc_heatmap         Produce heatmap plots for the given data
batchqc_pc_explained_variation
                        Returns explained variation for each principal
                        components
batchqc_pca             Performs principal component analysis and
                        produces plot of the first two principal
                        components
batchqc_pca_svd         Performs PCA svd variance decomposition and
                        produces plot of the first two principal
                        components
batchtest               Performs test to check whether batch needs to
                        be adjusted
combatPlot              Adjust for batch effects using an empirical
                        Bayes framework ComBat allows users to adjust
                        for batch effects in datasets where the batch
                        covariate is known, using methodology described
                        in Johnson et al. 2007. It uses either
                        parametric or non-parametric empirical Bayes
                        frameworks for adjusting data for batch
                        effects. Users are returned an expression
                        matrix that has been corrected for batch
                        effects. The input data are assumed to be
                        cleaned and normalized before batch effect
                        removal.
example_batchqc_data    Batch and Condition indicator for signature
                        data captured when activating different growth
                        pathway genes in human mammary epithelial
                        cells.
getShinyInput           Getter function to get the shinyInput option
getShinyInputCombat     Getter function to get the shinyInputCombat
                        option
getShinyInputOrig       Getter function to get the shinyInputOrig
                        option
getShinyInputSVA        Getter function to get the shinyInputSVA option
getShinyInputSVAf       Getter function to get the shinyInputSVAf
                        option
getShinyInputSVAr       Getter function to get the shinyInputSVAr
                        option
gnormalize              Perform Genewise Normalization of the given
                        data matrix
lmFitC                  Fit linear model for each gene given a series
                        of arrays.  This is the standard lmFit function
                        from limma package with the modification to
                        accept an additional correlation matrix
                        parameter option
log2CPM                 Compute log2(counts per mil reads) and library
                        size for each sample
makeSVD                 Compute singular value decomposition
pcRes                   Compute variance of each principal component
                        and how they correlate with batch and cond
plotPC                  Plot first 2 principal components
plot_genewise_moments   Visualize gene-wise moments
plot_samplewise_moments
                        Visualize sample-wise moments
protein_example_data    Batch and Condition indicator for protein
                        expression data
rnaseq_sim              Generate simulated count data with batch
                        effects for ngenes
setShinyInput           Setter function to set the shinyInput option
setShinyInputCombat     Setter function to set the shinyInputCombat
                        option
setShinyInputOrig       Setter function to set the shinyInputOrig
                        option
setShinyInputSVA        Setter function to set the shinyInputSVA option
setShinyInputSVAf       Setter function to set the shinyInputSVAf
                        option
setShinyInputSVAr       Setter function to set the shinyInputSVAr
                        option
