Package 'sistmr'

Bland-Altman plot function

Description

Bland-Altman plot function

Usage

BlandAltmanPlot(
  var1,
  var2,
  with_gradient = FALSE,
  line_color = c("blue", "lightblue"),
  extremum_pctg = TRUE
)

Arguments

var1	a vector of numerics for the 1rst group to be compared.
var2	a vector of numerics for the 2nd group to be compared.
with_gradient	a logical indicating if you have a lot of measures, use with_gradient=TRUE to have gradient scale and not points. Default value is FALSE.
line_color	a vector of color for the three lines : average difference and upper and lower limits of the confidence interval for the average difference.
extremum_pctg	a logical indicating if you want to add the percentage of points outside the confidence interval for the upper and lower limits. Default is TRUE.

Value

a ggplot2 object

Examples

library(ggplot2)

#Small sample
#Generate data
x <- rnorm(30)
y <- rnorm(30, mean = 5, sd = 3)
#Plotting
BlandAltmanPlot(var1 = x, var2 = y) 
#Add color by group 
gr  <- c(rep("G1", 15), rep("G2", 15))
BlandAltmanPlot(var1 = x, var2 = y) + geom_point(aes(color = gr))

#High sample
#Generate data
x <- rnorm(10000)
y <- rnorm(10000, mean = 5, sd = 3)
#Plotting with gradient
BlandAltmanPlot(var1 = x, var2 = y, with_gradient = TRUE)

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Multiple boxplots for many times

Description

Multiple boxplots for many times

Usage

multipleBoxplots(data, x_var, y_var, add_points = TRUE)

Arguments

data	a dataset from which the variable x_var and y_var should be taken.
x_var	corresponding to the x coordinates for the plot, it must be a factor to obtain multiple boxplots.
y_var	corresponding to the y coordinates for the plot.
add_points	if you want to add points on boxplots. Default value is TRUE.

Value

a ggplot2 object

Examples

library(ggplot2)

#Generate data
x_ex <- factor(c(rep("J0", 10), rep("J7", 10), rep("J14", 10)), levels = c("J0", "J7", "J14"))
y_ex <- rnorm(30)

data_ex <- cbind.data.frame(x_ex, y_ex)

#Plotting
multipleBoxplots(data = data_ex, x_var = x_ex, y_var = y_ex)

multipleBoxplots(data = data_ex, x_var = x_ex, y_var = y_ex) + 
labs(x = "Time", y = "Value") + 
theme(legend.position = "none")

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Functions

Description

Functions

Usage

normal_distribution(vec)

Arguments

vec

a vector

Value

a vector

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sistmr.

Description

This package contains functions common to members of the SISTM team.

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Volcano plot function

Description

Volcano plot function

Usage

volcanoPlot(
  log2fc,
  pValue,
  data,
  FDR_threshold = 0.05,
  LFC_threshold = log2(1.5),
  color = c("red", "black"),
  geneNames = NULL,
  nb_geneTags = 20,
  logTransformPVal = TRUE
)

Arguments

log2fc	a magnitude of change (fold-change) in base log 2 corresponding to the x-axis.
pValue	a statistical significance (p-value) corresponding to the y-axis.
data	a data.frame of differentially expressed results from which the variable log2fc, pValue and geneNames (if it is used) should be taken.
FDR_threshold	a threshold of false discovery rate.
LFC_threshold	a threshold of log fold change.
color	a vector of two colors for significant or not significant points.
geneNames	a vector of gene names if you want to put gene tags on the volcano plot. Default is NULL.
nb_geneTags	number of tags for the significant genes if geneNames is not NULL. Default is 20 to obtain the 20 first significant genes.
logTransformPVal	If TRUE, the p-values will have a negative logarithm transformation (base 10). Default is TRUE.

Value

a ggplot2 object

Examples

genes <- paste0("G", 1:500)
pval <- runif(500, max = 0.5)
log2FC <- runif(500, min = -4, max = 4)

data <- cbind.data.frame(genes, pval, log2FC)

rm(genes, pval, log2FC)
volcanoPlot(log2FC, pval, data, geneNames = genes)

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