# plotRowStats: Plot row-wise statistics In psichomics: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation

## Description

Scatter plot to compare between the row-wise mean, median, variance or range from a data frame or matrix. Also supports transformations of those variables, such as `log10(mean)`. If `y = NULL`, a density plot is rendered instead.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```plotRowStats( data, x, y = NULL, subset = NULL, xmin = NULL, xmax = NULL, ymin = NULL, ymax = NULL, xlim = NULL, ylim = NULL, cache = NULL, verbose = FALSE, data2 = NULL, legend = FALSE, legendLabels = c("Original", "Highlighted") ) ```

## Arguments

 `data` Data frame or matrix containing samples per column and, for instance, gene or alternative splicing event per row `x, y` Character: statistic to calculate and display in the plot per row; choose between `mean`, `median`, `var` or `range` (or transformations of those variables, e.g. `log10(var)`); if `y = NULL`, the density of `x` will be plot instead `subset` Boolean or integer: `data` points to highlight `xmin, xmax, ymin, ymax` Numeric: minimum and maximum X and Y values to draw in the plot `xlim, ylim` Numeric: X and Y axis range `cache` List of summary statistics for `data` previously calculated to avoid repeating calculations (output also returns cache in attribute named `cache` with appropriate data) `verbose` Boolean: print messages of the steps performed `data2` Same as `data` argument but points in `data2` are highlighted (unless `data2 = NULL`) `legend` Boolean: show legend? `legendLabels` Character: legend labels

## Value

Plot of `data`

Other functions for gene expression pre-processing: `convertGeneIdentifiers()`, `filterGeneExpr()`, `normaliseGeneExpression()`, `plotGeneExprPerSample()`, `plotLibrarySize()`
Other functions for PSI quantification: `filterPSI()`, `getSplicingEventTypes()`, `listSplicingAnnotations()`, `loadAnnotation()`, `quantifySplicing()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```library(ggplot2) # Plotting gene expression data geneExpr <- readFile("ex_gene_expression.RDS") plotRowStats(geneExpr, "mean", "var^(1/4)") + ggtitle("Mean-variance plot") + labs(y="Square Root of the Standard Deviation") # Plotting alternative splicing quantification annot <- readFile("ex_splicing_annotation.RDS") junctionQuant <- readFile("ex_junctionQuant.RDS") psi <- quantifySplicing(annot, junctionQuant, eventType=c("SE", "MXE")) medianVar <- plotRowStats(psi, x="median", y="var", xlim=c(0, 1)) + labs(x="Median PSI", y="PSI variance") medianVar rangeVar <- plotRowStats(psi, x="range", y="log10(var)", xlim=c(0, 1)) + labs(x="PSI range", y="log10(PSI variance)") rangeVar ```