revdep/checks.noindex/statVisual/old/statVisual.Rcheck/statVisual-Ex.R
In gbm-developers/gbm: Generalized Boosted Regression Models
pkgname <- "statVisual"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('statVisual')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("BiAxisErrBar")
### * BiAxisErrBar
flush(stderr()); flush(stdout())
### Name: BiAxisErrBar
### Title: Compare Patterns of Two Outcomes in One Scatter Plot
### Aliases: BiAxisErrBar
### Keywords: method
### ** Examples
library(tidyverse)
library(ggplot2)
print(head(mtcars))
print(table(mtcars$gear, useNA="ifany"))
statVisual(type = "BiAxisErrBar",
dat= mtcars,
group = "gear",
y.left = "mpg",
y.right = "wt")
BiAxisErrBar(
dat = mtcars,
group = "gear",
y.left = "mpg",
y.right = "wt")
cleanEx()
nameEx("Box")
### * Box
flush(stderr()); flush(stdout())
### Name: Box
### Title: Compare Groups Based on Boxplots Across Time
### Aliases: Box
### Keywords: method
### ** Examples
library(dplyr)
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'Box',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Boxplots across time")
Box(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Boxplots across time")
cleanEx()
nameEx("BoxROC")
### * BoxROC
flush(stderr()); flush(stdout())
### Name: BoxROC
### Title: Compare Boxplots with ROC Curve
### Aliases: BoxROC
### Keywords: method
### ** Examples
library(dplyr)
library(gridExtra)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'BoxROC',
data = pDat,
group = 'grp',
y = 'probe1',
point.size = 1)
BoxROC(
data = pDat,
group = 'grp',
y = 'probe1',
point.size = 1)
cleanEx()
nameEx("Den")
### * Den
flush(stderr()); flush(stdout())
### Name: Den
### Title: Compare Groups Based on Density Plots
### Aliases: Den
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Den',
data = pDat,
y = 'probe1',
group = 'grp')
Den(
data = pDat,
y = 'probe1',
group = 'grp')
cleanEx()
nameEx("Dendro")
### * Dendro
flush(stderr()); flush(stdout())
### Name: Dendro
### Title: Compare Groups Based on Dendrogram
### Aliases: Dendro
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
statVisual(type = 'Dendro',
x = pDat[, c(3:8)],
group = pDat$grp)
Dendro(
x = pDat[, c(3:8)],
group = pDat$grp)
cleanEx()
nameEx("ErrBar")
### * ErrBar
flush(stderr()); flush(stdout())
### Name: ErrBar
### Title: Compare Groups Based on dotplots Across Time
### Aliases: ErrBar
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'ErrBar',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Dot plots across time")
ErrBar(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Dot plots across time")
cleanEx()
nameEx("Heat")
### * Heat
flush(stderr()); flush(stdout())
### Name: Heat
### Title: Heatmap with Row Names Colored by Group
### Aliases: Heat
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Heat',
data = pDat[, c(2:8)],
group = 'grp')
Heat(
data = pDat[, c(2:8)],
group = 'grp')
cleanEx()
nameEx("Hist")
### * Hist
flush(stderr()); flush(stdout())
### Name: Hist
### Title: Compare Groups Based on Histograms
### Aliases: Hist
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Hist',
data = pDat,
y = 'probe1',
group = 'grp')
Hist(
data = pDat,
y = 'probe1',
group = 'grp')
cleanEx()
nameEx("ImpPlot")
### * ImpPlot
flush(stderr()); flush(stdout())
### Name: ImpPlot
### Title: Plot of Variable Importance
### Aliases: ImpPlot
### Keywords: method
### ** Examples
library(dplyr)
library(randomForest)
library(tibble)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
rf_m = randomForest(
x = pDat[, c(3:8)],
y = pDat$grp,
importance = TRUE, proximity = TRUE
)
statVisual(type = 'ImpPlot', model = rf_m)
ImpPlot(model = rf_m)
cleanEx()
nameEx("LinePlot")
### * LinePlot
flush(stderr()); flush(stdout())
### Name: LinePlot
### Title: Compare Groups Based on Trajectory Plots
### Aliases: LinePlot
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = "LinePlot",
data = longDat,
x = 'time',
y = 'y',
sid = 'sid',
group = 'grp')
LinePlot(
data = longDat,
x = 'time',
y = 'y',
sid = 'sid',
group = 'grp')
cleanEx()
nameEx("PCA_score")
### * PCA_score
flush(stderr()); flush(stdout())
### Name: PCA_score
### Title: Scatter Plot of 2 Specified Principal Components
### Aliases: PCA_score
### Keywords: method
### ** Examples
library(factoextra)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
###
pca.obj = iprcomp(pDat[, c(3:8)], scale. = TRUE)
# scree plot
factoextra::fviz_eig(pca.obj, addlabels = TRUE)
# scatter plot of PC1 vs PC2
statVisual(type = 'PCA_score',
prcomp_obj = pca.obj,
dims = c(1, 2),
data = pDat,
color = 'grp',
loadings = FALSE)
PCA_score(prcomp_obj = pca.obj,
dims = c(1, 3),
data = pDat,
color = 'grp',
loadings = FALSE)
cleanEx()
nameEx("PVCA")
### * PVCA
flush(stderr()); flush(stdout())
### Name: PVCA
### Title: Principal Variance Component Analysis (PVCA)
### Aliases: PVCA
### Keywords: method
### ** Examples
library(pvca)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# create a fake Batch variable
esSim$Batch=c(rep("A", 4), rep("B", 6), rep("C", 10))
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
statVisual(type = 'PVCA',
clin_data = pData(esSim),
clin_subjid = "sid",
gene_data = exprs(esSim),
batch.factors = c("grp", "Batch"))
PVCA(
clin_data = pData(esSim),
clin_subjid = "sid",
gene_data = exprs(esSim),
batch.factors = c("grp", "Batch"))
cleanEx()
nameEx("Volcano")
### * Volcano
flush(stderr()); flush(stdout())
### Name: Volcano
### Title: Volcano Plot
### Aliases: Volcano
### Keywords: method
### ** Examples
library(ggrepel)
library(limma)
# load the simulated dataset
data(esSim)
print(esSim)
# expression levels
y = exprs(esSim)
print(dim(y))
print(y[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat)
# design matrix
design = model.matrix(~grp, data = pDat)
print(design)
options(digits = 3)
# Ordinary fit
fit <- lmFit(y, design)
fit2 <- eBayes(fit)
# get result data frame
resFrame = topTable(fit2,coef = 2, number = nrow(esSim))
print(dim(resFrame))
print(resFrame[1:2,])
resFrame$sigFlag = resFrame$adj.P.Val < 0.05
resFrame$probe = rownames(resFrame)
# make sure set NA to genes non-differentially expressed
resFrame$probe[which(resFrame$sigFlag == FALSE)] = NA
print(resFrame[1:2,])
print(table(resFrame$sigFlag, useNA = "ifany"))
statVisual(type = 'Volcano',
resFrame = resFrame,
stats = 'logFC',
p.value = 'P.Value',
group = 'sigFlag',
rowname.var = 'probe',
point.size = 1)
Volcano(
resFrame = resFrame,
stats = 'logFC',
p.value = 'P.Value',
group = 'sigFlag',
rowname.var = 'probe',
point.size = 1)
cleanEx()
nameEx("XYscatter")
### * XYscatter
flush(stderr()); flush(stdout())
### Name: XYscatter
### Title: Compare Groups Based on Scatter Plots
### Aliases: XYscatter
### Keywords: method
### ** Examples
data(diffCorDat)
print(dim(diffCorDat))
print(diffCorDat[1:2,])
statVisual(type = 'XYscatter',
data = diffCorDat,
x = 'probe1',
y = 'probe2',
group = 'grp',
title = 'Scatter Plot: probe1 vs probe2')
XYscatter(
data = diffCorDat,
x = 'probe1',
y = 'probe2',
group = 'grp',
title = 'Scatter Plot: probe1 vs probe2')
cleanEx()
nameEx("barPlot")
### * barPlot
flush(stderr()); flush(stdout())
### Name: barPlot
### Title: Compare Groups Based on Barplots Across Time
### Aliases: barPlot
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'barPlot',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Bar plots across time")
barPlot(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Bar plots across time")
cleanEx()
nameEx("cv_glmnet_plot")
### * cv_glmnet_plot
flush(stderr()); flush(stdout())
### Name: cv_glmnet_plot
### Title: Plot the Cross-Validation Curve Produced by cv.glmnet
### Aliases: cv_glmnet_plot
### Keywords: method
### ** Examples
library(dplyr)
library(tibble)
library(glmnet)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = "cv_glmnet_plot",
x = as.matrix(pDat[, c(3:8)]),
y = pDat$grp,
family = "binomial")
cv_glmnet_plot(x = as.matrix(pDat[, c(3:8)]),
y = pDat$grp,
family = "binomial")
cleanEx()
nameEx("diffCorDat")
### * diffCorDat
flush(stderr()); flush(stdout())
### Name: diffCorDat
### Title: A Dataset for Differential Correlation Analysis
### Aliases: diffCorDat
### Keywords: datasets
### ** Examples
data(diffCorDat)
print(dim(diffCorDat))
print(diffCorDat[1:2,])
cleanEx()
nameEx("esSim")
### * esSim
flush(stderr()); flush(stdout())
### Name: esSim
### Title: A Simulated Gene Expression Dataset
### Aliases: esSim
### Keywords: datasets
### ** Examples
data(esSim)
print(esSim)
###
dat=exprs(esSim)
print(dim(dat))
print(dat[1:2,])
###
pDat=pData(esSim)
print(dim(pDat))
print(pDat)
# subject group status
print(table(esSim$grp))
###
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2, ])
# probe's status of differential expression
print(table(fDat$memProbes))
cleanEx()
nameEx("genoSim")
### * genoSim
flush(stderr()); flush(stdout())
### Name: genoSim
### Title: An ExpressionSet Object Storing Simulated Genotype Data
### Aliases: genoSim
### Keywords: datasets
### ** Examples
data(genoSim)
print(genoSim)
cleanEx()
nameEx("iprcomp")
### * iprcomp
flush(stderr()); flush(stdout())
### Name: iprcomp
### Title: Improved Function for Obtaining Principal Components
### Aliases: iprcomp
### Keywords: method
### ** Examples
# generate simulated data
set.seed(1234567)
dat.x = matrix(rnorm(500), nrow = 100, ncol = 5)
dat.y = matrix(rnorm(500, mean = 2), nrow = 100, ncol = 5)
dat = rbind(dat.x, dat.y)
grp = c(rep(0, 100), rep(1, 100))
print(dim(dat))
res = iprcomp(dat, center = TRUE, scale. = FALSE)
# for each row, set one artificial missing value
dat.na=dat
nr=nrow(dat.na)
nc=ncol(dat.na)
for(i in 1:nr)
{
posi=sample(x=1:nc, size=1)
dat.na[i,posi]=NA
}
res.na = iprcomp(dat.na, center = TRUE, scale. = FALSE)
##
# pca plot
##
par(mfrow = c(3,1))
# original data without missing values
plot(x = res$x[,1], y = res$x[,2], xlab = "PC1", ylab = "PC2")
# perturbed data with one NA per probe
# the pattern of original data is captured
plot(x = res.na$x[,1], y = res.na$x[,2], xlab = "PC1", ylab = "PC2", main = "with missing values")
par(mfrow = c(1,1))
graphics::par(get("par.postscript", pos = 'CheckExEnv'))
cleanEx()
nameEx("longDat")
### * longDat
flush(stderr()); flush(stdout())
### Name: longDat
### Title: A Simulated Dataset for Longitudinal Data Analysis
### Aliases: longDat
### Keywords: datasets
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
cleanEx()
nameEx("stackedBarPlot")
### * stackedBarPlot
flush(stderr()); flush(stdout())
### Name: stackedBarPlot
### Title: Draw Stacked Bar Plots
### Aliases: stackedBarPlot
### Keywords: method
### ** Examples
data(genoSim)
pDat = pData(genoSim)
geno = exprs(genoSim)
pDat$snp1 = geno[1,]
print(table(pDat$snp1, pDat$grp, useNA="ifany"))
stackedBarPlot(dat = pDat,
catVar = "snp1",
group = "grp",
xlab = "snp1",
ylab = "Count",
group.lab = "grp",
title = "Stacked barplots of counts",
catVarLevel = NULL)
cleanEx()
nameEx("statVisual")
### * statVisual
flush(stderr()); flush(stdout())
### Name: statVisual
### Title: The Wrapper Function Incorporating All Wrapper Functions in
### statVisual
### Aliases: statVisual
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Hist',
data = pDat,
y = 'probe1',
group = 'grp')
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
gbm-developers/gbm documentation built on Feb. 16, 2024, 6:13 p.m.
R Package Documentation
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pkgname <- "statVisual"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('statVisual')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("BiAxisErrBar")
### * BiAxisErrBar
flush(stderr()); flush(stdout())
### Name: BiAxisErrBar
### Title: Compare Patterns of Two Outcomes in One Scatter Plot
### Aliases: BiAxisErrBar
### Keywords: method
### ** Examples
library(tidyverse)
library(ggplot2)
print(head(mtcars))
print(table(mtcars$gear, useNA="ifany"))
statVisual(type = "BiAxisErrBar",
dat= mtcars,
group = "gear",
y.left = "mpg",
y.right = "wt")
BiAxisErrBar(
dat = mtcars,
group = "gear",
y.left = "mpg",
y.right = "wt")
cleanEx()
nameEx("Box")
### * Box
flush(stderr()); flush(stdout())
### Name: Box
### Title: Compare Groups Based on Boxplots Across Time
### Aliases: Box
### Keywords: method
### ** Examples
library(dplyr)
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'Box',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Boxplots across time")
Box(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Boxplots across time")
cleanEx()
nameEx("BoxROC")
### * BoxROC
flush(stderr()); flush(stdout())
### Name: BoxROC
### Title: Compare Boxplots with ROC Curve
### Aliases: BoxROC
### Keywords: method
### ** Examples
library(dplyr)
library(gridExtra)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'BoxROC',
data = pDat,
group = 'grp',
y = 'probe1',
point.size = 1)
BoxROC(
data = pDat,
group = 'grp',
y = 'probe1',
point.size = 1)
cleanEx()
nameEx("Den")
### * Den
flush(stderr()); flush(stdout())
### Name: Den
### Title: Compare Groups Based on Density Plots
### Aliases: Den
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Den',
data = pDat,
y = 'probe1',
group = 'grp')
Den(
data = pDat,
y = 'probe1',
group = 'grp')
cleanEx()
nameEx("Dendro")
### * Dendro
flush(stderr()); flush(stdout())
### Name: Dendro
### Title: Compare Groups Based on Dendrogram
### Aliases: Dendro
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
statVisual(type = 'Dendro',
x = pDat[, c(3:8)],
group = pDat$grp)
Dendro(
x = pDat[, c(3:8)],
group = pDat$grp)
cleanEx()
nameEx("ErrBar")
### * ErrBar
flush(stderr()); flush(stdout())
### Name: ErrBar
### Title: Compare Groups Based on dotplots Across Time
### Aliases: ErrBar
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'ErrBar',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Dot plots across time")
ErrBar(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Dot plots across time")
cleanEx()
nameEx("Heat")
### * Heat
flush(stderr()); flush(stdout())
### Name: Heat
### Title: Heatmap with Row Names Colored by Group
### Aliases: Heat
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Heat',
data = pDat[, c(2:8)],
group = 'grp')
Heat(
data = pDat[, c(2:8)],
group = 'grp')
cleanEx()
nameEx("Hist")
### * Hist
flush(stderr()); flush(stdout())
### Name: Hist
### Title: Compare Groups Based on Histograms
### Aliases: Hist
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Hist',
data = pDat,
y = 'probe1',
group = 'grp')
Hist(
data = pDat,
y = 'probe1',
group = 'grp')
cleanEx()
nameEx("ImpPlot")
### * ImpPlot
flush(stderr()); flush(stdout())
### Name: ImpPlot
### Title: Plot of Variable Importance
### Aliases: ImpPlot
### Keywords: method
### ** Examples
library(dplyr)
library(randomForest)
library(tibble)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
rf_m = randomForest(
x = pDat[, c(3:8)],
y = pDat$grp,
importance = TRUE, proximity = TRUE
)
statVisual(type = 'ImpPlot', model = rf_m)
ImpPlot(model = rf_m)
cleanEx()
nameEx("LinePlot")
### * LinePlot
flush(stderr()); flush(stdout())
### Name: LinePlot
### Title: Compare Groups Based on Trajectory Plots
### Aliases: LinePlot
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = "LinePlot",
data = longDat,
x = 'time',
y = 'y',
sid = 'sid',
group = 'grp')
LinePlot(
data = longDat,
x = 'time',
y = 'y',
sid = 'sid',
group = 'grp')
cleanEx()
nameEx("PCA_score")
### * PCA_score
flush(stderr()); flush(stdout())
### Name: PCA_score
### Title: Scatter Plot of 2 Specified Principal Components
### Aliases: PCA_score
### Keywords: method
### ** Examples
library(factoextra)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
pDat$grp = factor(pDat$grp)
###
pca.obj = iprcomp(pDat[, c(3:8)], scale. = TRUE)
# scree plot
factoextra::fviz_eig(pca.obj, addlabels = TRUE)
# scatter plot of PC1 vs PC2
statVisual(type = 'PCA_score',
prcomp_obj = pca.obj,
dims = c(1, 2),
data = pDat,
color = 'grp',
loadings = FALSE)
PCA_score(prcomp_obj = pca.obj,
dims = c(1, 3),
data = pDat,
color = 'grp',
loadings = FALSE)
cleanEx()
nameEx("PVCA")
### * PVCA
flush(stderr()); flush(stdout())
### Name: PVCA
### Title: Principal Variance Component Analysis (PVCA)
### Aliases: PVCA
### Keywords: method
### ** Examples
library(pvca)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# create a fake Batch variable
esSim$Batch=c(rep("A", 4), rep("B", 6), rep("C", 10))
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
statVisual(type = 'PVCA',
clin_data = pData(esSim),
clin_subjid = "sid",
gene_data = exprs(esSim),
batch.factors = c("grp", "Batch"))
PVCA(
clin_data = pData(esSim),
clin_subjid = "sid",
gene_data = exprs(esSim),
batch.factors = c("grp", "Batch"))
cleanEx()
nameEx("Volcano")
### * Volcano
flush(stderr()); flush(stdout())
### Name: Volcano
### Title: Volcano Plot
### Aliases: Volcano
### Keywords: method
### ** Examples
library(ggrepel)
library(limma)
# load the simulated dataset
data(esSim)
print(esSim)
# expression levels
y = exprs(esSim)
print(dim(y))
print(y[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat)
# design matrix
design = model.matrix(~grp, data = pDat)
print(design)
options(digits = 3)
# Ordinary fit
fit <- lmFit(y, design)
fit2 <- eBayes(fit)
# get result data frame
resFrame = topTable(fit2,coef = 2, number = nrow(esSim))
print(dim(resFrame))
print(resFrame[1:2,])
resFrame$sigFlag = resFrame$adj.P.Val < 0.05
resFrame$probe = rownames(resFrame)
# make sure set NA to genes non-differentially expressed
resFrame$probe[which(resFrame$sigFlag == FALSE)] = NA
print(resFrame[1:2,])
print(table(resFrame$sigFlag, useNA = "ifany"))
statVisual(type = 'Volcano',
resFrame = resFrame,
stats = 'logFC',
p.value = 'P.Value',
group = 'sigFlag',
rowname.var = 'probe',
point.size = 1)
Volcano(
resFrame = resFrame,
stats = 'logFC',
p.value = 'P.Value',
group = 'sigFlag',
rowname.var = 'probe',
point.size = 1)
cleanEx()
nameEx("XYscatter")
### * XYscatter
flush(stderr()); flush(stdout())
### Name: XYscatter
### Title: Compare Groups Based on Scatter Plots
### Aliases: XYscatter
### Keywords: method
### ** Examples
data(diffCorDat)
print(dim(diffCorDat))
print(diffCorDat[1:2,])
statVisual(type = 'XYscatter',
data = diffCorDat,
x = 'probe1',
y = 'probe2',
group = 'grp',
title = 'Scatter Plot: probe1 vs probe2')
XYscatter(
data = diffCorDat,
x = 'probe1',
y = 'probe2',
group = 'grp',
title = 'Scatter Plot: probe1 vs probe2')
cleanEx()
nameEx("barPlot")
### * barPlot
flush(stderr()); flush(stdout())
### Name: barPlot
### Title: Compare Groups Based on Barplots Across Time
### Aliases: barPlot
### Keywords: method
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
statVisual(type = 'barPlot',
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Bar plots across time")
barPlot(
data = longDat,
x = 'time',
y = 'y',
group = 'grp',
title = "Bar plots across time")
cleanEx()
nameEx("cv_glmnet_plot")
### * cv_glmnet_plot
flush(stderr()); flush(stdout())
### Name: cv_glmnet_plot
### Title: Plot the Cross-Validation Curve Produced by cv.glmnet
### Aliases: cv_glmnet_plot
### Keywords: method
### ** Examples
library(dplyr)
library(tibble)
library(glmnet)
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first 6 probes (3 OE probes, 2 UE probes, and 1 NE probe)
pDat$probe1 = dat[1,]
pDat$probe2 = dat[2,]
pDat$probe3 = dat[3,]
pDat$probe4 = dat[4,]
pDat$probe5 = dat[5,]
pDat$probe6 = dat[6,]
print(pDat[1:2, ])
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = "cv_glmnet_plot",
x = as.matrix(pDat[, c(3:8)]),
y = pDat$grp,
family = "binomial")
cv_glmnet_plot(x = as.matrix(pDat[, c(3:8)]),
y = pDat$grp,
family = "binomial")
cleanEx()
nameEx("diffCorDat")
### * diffCorDat
flush(stderr()); flush(stdout())
### Name: diffCorDat
### Title: A Dataset for Differential Correlation Analysis
### Aliases: diffCorDat
### Keywords: datasets
### ** Examples
data(diffCorDat)
print(dim(diffCorDat))
print(diffCorDat[1:2,])
cleanEx()
nameEx("esSim")
### * esSim
flush(stderr()); flush(stdout())
### Name: esSim
### Title: A Simulated Gene Expression Dataset
### Aliases: esSim
### Keywords: datasets
### ** Examples
data(esSim)
print(esSim)
###
dat=exprs(esSim)
print(dim(dat))
print(dat[1:2,])
###
pDat=pData(esSim)
print(dim(pDat))
print(pDat)
# subject group status
print(table(esSim$grp))
###
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2, ])
# probe's status of differential expression
print(table(fDat$memProbes))
cleanEx()
nameEx("genoSim")
### * genoSim
flush(stderr()); flush(stdout())
### Name: genoSim
### Title: An ExpressionSet Object Storing Simulated Genotype Data
### Aliases: genoSim
### Keywords: datasets
### ** Examples
data(genoSim)
print(genoSim)
cleanEx()
nameEx("iprcomp")
### * iprcomp
flush(stderr()); flush(stdout())
### Name: iprcomp
### Title: Improved Function for Obtaining Principal Components
### Aliases: iprcomp
### Keywords: method
### ** Examples
# generate simulated data
set.seed(1234567)
dat.x = matrix(rnorm(500), nrow = 100, ncol = 5)
dat.y = matrix(rnorm(500, mean = 2), nrow = 100, ncol = 5)
dat = rbind(dat.x, dat.y)
grp = c(rep(0, 100), rep(1, 100))
print(dim(dat))
res = iprcomp(dat, center = TRUE, scale. = FALSE)
# for each row, set one artificial missing value
dat.na=dat
nr=nrow(dat.na)
nc=ncol(dat.na)
for(i in 1:nr)
{
posi=sample(x=1:nc, size=1)
dat.na[i,posi]=NA
}
res.na = iprcomp(dat.na, center = TRUE, scale. = FALSE)
##
# pca plot
##
par(mfrow = c(3,1))
# original data without missing values
plot(x = res$x[,1], y = res$x[,2], xlab = "PC1", ylab = "PC2")
# perturbed data with one NA per probe
# the pattern of original data is captured
plot(x = res.na$x[,1], y = res.na$x[,2], xlab = "PC1", ylab = "PC2", main = "with missing values")
par(mfrow = c(1,1))
graphics::par(get("par.postscript", pos = 'CheckExEnv'))
cleanEx()
nameEx("longDat")
### * longDat
flush(stderr()); flush(stdout())
### Name: longDat
### Title: A Simulated Dataset for Longitudinal Data Analysis
### Aliases: longDat
### Keywords: datasets
### ** Examples
data(longDat)
print(dim(longDat))
print(longDat[1:3,])
print(table(longDat$time, useNA = "ifany"))
print(table(longDat$grp, useNA = "ifany"))
print(table(longDat$sid, useNA = "ifany"))
print(table(longDat$time, longDat$grp))
cleanEx()
nameEx("stackedBarPlot")
### * stackedBarPlot
flush(stderr()); flush(stdout())
### Name: stackedBarPlot
### Title: Draw Stacked Bar Plots
### Aliases: stackedBarPlot
### Keywords: method
### ** Examples
data(genoSim)
pDat = pData(genoSim)
geno = exprs(genoSim)
pDat$snp1 = geno[1,]
print(table(pDat$snp1, pDat$grp, useNA="ifany"))
stackedBarPlot(dat = pDat,
catVar = "snp1",
group = "grp",
xlab = "snp1",
ylab = "Count",
group.lab = "grp",
title = "Stacked barplots of counts",
catVarLevel = NULL)
cleanEx()
nameEx("statVisual")
### * statVisual
flush(stderr()); flush(stdout())
### Name: statVisual
### Title: The Wrapper Function Incorporating All Wrapper Functions in
### statVisual
### Aliases: statVisual
### Keywords: method
### ** Examples
data(esSim)
print(esSim)
# expression data
dat = exprs(esSim)
print(dim(dat))
print(dat[1:2,])
# phenotype data
pDat = pData(esSim)
print(dim(pDat))
print(pDat[1:2,])
# feature data
fDat = fData(esSim)
print(dim(fDat))
print(fDat[1:2,])
# choose the first probe which is over-expressed in cases
pDat$probe1 = dat[1,]
# check histograms of probe 1 expression in cases and controls
print(table(pDat$grp, useNA = "ifany"))
statVisual(type = 'Hist',
data = pDat,
y = 'probe1',
group = 'grp')
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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