markerScatter: Scatter Plot for Gene Expression

In eegc: Engineering Evaluation by Gene Categorization (eegc)

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 = "")

eegc documentation built on Nov. 8, 2020, 5:27 p.m.