markerScatter {eegc} | R Documentation

## Scatter Plot for Gene Expression

### Description

Generates an expression profile of each gene catetory in one sample against another, alternatively plot the regression line from linear modeling fitting.

### Usage

    markerScatter(expr, log = FALSE, samples, cate.gene, markers = NULL, pch = 19, cex = 0.5,
    col = NULL, xlab = NULL, ylab = NULL, main = NULL, add.line = TRUE, text.cex = 1, legend.labels = NULL,
    ...  )

### Arguments

`expr` | a data frame with gene expression.
`log` | logical to determine if the gene expression data is log converted (add a small constant 2), default to FALSE.
`samples` | a vector of samples to compare on the x axis and y axis.
`cate.gene` | a list of the gene categories, alternatively output by `categorizeGene`.
`markers` | vector of marker genes to be highlighted in the plot. No gene is highlighted when it's `NULL`.
`pch, cex, col, xlab, ylab, main` | plot parameters, see details in `par`.
`add.line` | logical to determine if the linear model fitting line is added on the figure.
`text.cex` | font size for the text on `markers`, see details in `text`.
`legend.labels` | vector of labels for the marker legend.
`...` | other parameters in `plot`.

### Details

Visualization of gene expression in the five categories under each pair-wised comparison.

### Value

plot with gene expression profile.

### Examples

    #load the marker genes of somatic and primary cells
    data(markers)
    data(expr.filter)
    #scatterplot
    col = c("#abd9e9", "#2c7bb6", "#fee090", "#d7191c", "#fdae61")
    markerScatter(expr = expr.filter, log = TRUE, samples = c("CB", "DMEC"),
                  cate.gene = cate.gene[2:4], markers = markers, col = col[2:4],
                  xlab = expression('log'[2]*' expression in CB (target)'),
                 ylab = expression('log'[2]*' expression in DMEC (input)'),main = "")

[Package eegc version 1.6.1 Index]