DGCA (version 1.0.1)

ddcorAll: Calls the DGCA pairwise pipeline.

Description

Runs the full discovery of differential correlation (ddcor) section for comparing pairwise correlations across conditions in the Differential Gene Correlation Analysis (DGCA) package.

Usage

ddcorAll(inputMat, design, compare, inputMatB = NULL, splitSet = NULL,
  impute = FALSE, corrType = "pearson", nPairs = "all",
  sortBy = "zScoreDiff", adjust = "perm", nPerms = 10, classify = TRUE,
  sigThresh = 1, corSigThresh = 0.05, heatmapPlot = FALSE,
  color_palette = NULL, verbose = FALSE, plotFdr = FALSE,
  corr_cutoff = 0.99, signType = "none", getDCorAvg = FALSE,
  dCorAvgType = "gene_average", dCorAvgMethod = "median",
  oneSidedPVal = FALSE, customize_heatmap = FALSE, heatmapClassic = FALSE,
  corPower = 2, ...)

Arguments

inputMat

The matrix (or data.frame) of values (e.g., gene expression values from an RNA-seq or microarray study) that you are interested in analyzing. The rownames of this matrix should correspond to the identifiers whose correlations and differential correlations you are interested in analyzing, while the columns should correspond to the rows of the design matrix and should be separable into your groups.

design

A standard model.matrix created design matrix. Rows correspond to samples and colnames refer to the names of the conditions that you are interested in analyzing. Only 0's or 1's are allowed in the design matrix. Please see vignettes for more information.

compare

Vector of two character strings, each corresponding to one group name in the design matrix, that should be compared.

inputMatB

Optional, secondary input matrix that allows you to calculate correlation and differential correlation for the rows between inputMat and imputMatB. Default = NULL.

splitSet

Optional character vector that splits the first matrix into two matrices and calculates differential correlation across these matrices. Common use case is when you want the differential correlation of a small set of identifiers (e.g., one), compared with all of the other identifiers in the matrix in each condition. Cannot be used when a second matrix is inputted -- setting both of arguments to non-NULL values will result in an error.

impute

A binary variable specifying whether values should be imputed if there are missing values. Note that the imputation is performed in the full input matrix (i.e., prior to subsetting) and uses k-nearest neighbors.

corrType

The correlation type of the analysis, limited to "pearson" or "spearman". Default = "pearson".

nPairs

Either a number, specifying the number of top differentially correlated identifier pairs to display in the resulting table, or a the string "all" specifying that all of the pairs should be returned. If splitSet is specified, this is reset to the number of non-splitSet identifiers in the input matrix, and therefore will not be evaluated.

sortBy

Character string specifying the way by which you'd like to sort the resulting table.

adjust

Allows for resulting p-values to be corrected for multiple hypothesis tests, optional. Some non-default choices require the "fdrtool" package or the "qvalue". Default = "none", which means that no p-value adjustment is performed. Other options include "perm" to use permutation samples, methods in ?p.adjust (i.e., "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr"), and methods in ?fdrtool (i.e., "fndr", "pct0", "locfdr").

nPerms

Number of permutations to generate. If NULL, permutation testing will not be performed. Default = "10".

classify

Binary value specifying whether the correlation values in each condition and differential correlation scores should be used to classifying the resulting identifiers into groups. Default = TRUE

sigThresh

If classify = TRUE, this numeric value specifies the p-value threshold at which a differential correlation p-value is deemed significant for differential correlation class calculation. Default = 1, as investigators may use different cutoff thresholds; however, this can be lowered to establish significant classes as desired.

corSigThresh

If classify = TRUE, this numeric value specifies the p-value threshold at which a correlation p-value is deemed significant. Default = 0.05.

heatmapPlot

Option indicating whether a heatmap of the differential correlations between the two conditions should be plotted. Default = TRUE.

color_palette

Color palette for plotting the heatmap. If not specified, the heatmap defaults to a red-green color-blind palette with bluish green indicating negative correlations and vermillion indicating positive correlations. Default = NULL

verbose

Option indicating whether the program should give more frequent updates about its operations. Default = FALSE.

plotFdr

Allows for plotting of fdrtool p-value adjustment result OR empirical FDR q-value adjustment technique, if either of these are chosen. Requires fdrtool package OR qvalue package. Default = FALSE.

corr_cutoff

Cutoff specifying correlation values beyond which will be truncated to this value, to reduce the effect of outlier correlation values when using small sample sizes. Note that this does NOT affect the underlying correlation values, but does affect the z-score difference of correlation calculation in the dcTopPairs table. Default = 0.99

signType

Coerce all correlation coefficients to be either positive (via "positive"), negative (via "negative"), or none (via "none") prior to calculating differential correlation. This could be used if, e.g., you think that going from a positive to a negative correlation is unlikely to occur biologically and is more likely to be due to noise, and you want to ignore these effects. Note that this does NOT affect the reported underlying correlation values, but does affect the z-score difference of correlation calculation. Default = "none", for no coercing.

getDCorAvg

Logical, specifying whether the average difference in correlation between groups should be calculated. Default = FALSE

dCorAvgType

Character vector specifying the type of average differential correlation calculation that should be performed. Only evaluated if dCorAge is TRUE. Types = c("gene_average", "total_average", "both"). gene_average calculates whether each genes' differential correlation with all others is more than expected via permutation samples (and empirical FDR adjustment, in the case of > 1 gene), while total_average calculates whether the total average differential correlation is higher than expected via permutation samples. "both" performs both of these. If splitSet is specified, then only genes in the splitSet have their average gene differential correlation calculated if gene_average is chosen.

dCorAvgMethod

Character vector specifying the method for calculating the "average" differential correlation calculation that should be used. Options = "median", "mean".

oneSidedPVal

If the dCorAvgType test is total_average, this option specifies whether a one-sided p-value should be reported, as opposed to a two-sided p-value. That is, if the average difference of z-scores is greater than zero, test whether the permutation average difference of z-scores are less than that average to get the p-value, and vice versa for the case that the average difference of z-scores is less than 0. Otherwise, test whether the absolute value of the average difference in z-scores is greater than the absolute values of the permutation average difference in z-scores. Default = FALSE.

customize_heatmap

Option to remove some default options in the heatmap plot, to allow users to add custom options.

heatmapClassic

Option to make the heatmap more granular (e.g., not showing the individual gene symbols) and more of a "classic" type of heatmap. Overrides most other heatmap options.

corPower

The power to raise the correlations to before plotting the classic heatmap. Larger correlation powers emphasize larger correlation values relatively more compared to smaller correlation values.

...

Additional plotting arguments if heatmapPlot = TRUE.

Value

Typically, the returned object is a data frame of the table of differential correlations between conditions. In the case that dCorAvg is calculated, the returned object is instead a list containing that table as well as the object summarizing the difference in average correlation for the specified portion of the data set.

Examples

Run this code
# NOT RUN {
data(darmanis); data(design_mat); darmanis_subset = darmanis[1:30, ]
ddcor_res = ddcorAll(inputMat = darmanis_subset, design = design_mat,
	compare = c("oligodendrocyte", "neuron"))
# }

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