hitheatmap: Visualization of the PMM results

Description Usage Arguments Details Value Author(s) Examples

View source: R/hitheatmap.R

Description

This function visualizes the results of PMM.

Usage

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hitheatmap(fit, threshold = 0.2, sharedness.score = FALSE,
                  main = "", na.action = "use", ...)

Arguments

fit

data frame returned by the pmm function.

threshold

threshold for the false discovery rate. Genes are counted as hits if they are below this threshold. Default is 0.2.

sharedness.score

logical value that indicates whether the sharedness score among the conditions should be additionally plotted. Default is FALSE.

main

the title at the top of the plot.

na.action

a function that indicates what happens if fit contains NAs. There are two options: "na.omit" or "use" (default). In the case of "na.omit" the hitheatmap is plotted for na.omit(fit) and in the other case the hitheatmap plots all data in fit.

...

further arguments passed to plot and par function.

Details

The heat map represents the effects c_cg estimated by PMM. Red color indicates a positive c_cg coefficient, blue color a negative c_cg coefficient. The darker the color, the stronger is the c_cg effect. The heat map contains only the genes with false discovery rate below the given threshold for at least one condition. The yellow star indicates the hit genes in each condition. If sharedness.score = TRUE, an additional row is plotted. It represents the strength of sharedness for a gene among the conditions. The darker the color, the stronger is the sharedness effect. If na.omit = "use" then NAs are plotted in white color and marked by "NA".

Value

A heat map

Author(s)

Anna Drewek <adrewek@stat.math.ethz.ch>

Examples

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data(kinome)
fit1 <- pmm(kinome,"InfectionIndex","weight_library")

hitheatmap(fit1, threshold = 0.4)
hitheatmap(fit1, threshold = 0.2, main = "Results PMM")
hitheatmap(fit1, sharedness.score = TRUE)

## NA-Handling
kinome$InfectionIndex[kinome$GeneID == 3611 & kinome$condition ==
"ADENO"] <- rep(NA,12)
fit2 <- pmm(kinome,"InfectionIndex","weight_library")
hitheatmap(fit2, main = "Results PMM with NA")

## Using par options
hitheatmap(fit1, sharedness.score = TRUE, cex.main = 2,
                main = "My modified plot", col.main = "white",
                col.axis = "white", cex.axis = 0.8, bg = "black",
                mar = c(7,6,4,6))

pmm documentation built on April 29, 2020, 3:07 a.m.