Package 'statVisual' reference manual

Title:	Statistical Visualization Tools
Description:	Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Authors:	Wenfei Zhang [aut, cre], Weiliang Qiu [aut, ctb], Xuan Lin [aut, ctb], Donghui Zhang [aut, ctb]
Maintainer:	Wenfei Zhang <[email protected]>
License:	GPL (>= 2)
Version:	1.2.1
Built:	2025-02-17 03:46:42 UTC
Source:	https://github.com/gefeizhang/statvisual

Compare Groups Based on Barplots Across Time

Description

This function is to compare groups using barplots at each time point. In addition, line segments are used to connect the mean/median of each barplot of the same group across time to show the differences between the mean trajectories. Also, for each barplot the barplot of mean $+/-$ standard error will be plot.

Usage

barPlot(
    data, 
    x = NULL, 
    y, 
    group = NULL,
    semFlag = TRUE,
    xFlag = FALSE, 
    bar.width = 0.5, 
    dodge.width = 0.8, 
    jitter = FALSE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.1, 
    line = NULL, 
    line.color = "black", 
    xlab = x, 
    ylab = line, 
    theme_classic = TRUE, 
    group.lab = group, 
    title = "bar plots", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)
barPlot(
    data, 
    x = NULL, 
    y, 
    group = NULL,
    semFlag = TRUE,
    xFlag = FALSE, 
    bar.width = 0.5, 
    dodge.width = 0.8, 
    jitter = FALSE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.1, 
    line = NULL, 
    line.color = "black", 
    xlab = x, 
    ylab = line, 
    theme_classic = TRUE, 
    group.lab = group, 
    title = "bar plots", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`x`	character. The column name of `data` that indicates the first grouping variable
`y`	character. The column name of `data` that indicates the variable on y axis
`group`	character. The column name of `data` that indicates the subject groups. The barplots will be drawn for each of the subject group within each category of `x`.
`semFlag`	logical. Indicate if sem or se should be used to draw error bar
`xFlag`	logical. Indicate if `x` should be treated as continuous (`xFlag=TRUE`)
`bar.width`	numeric. error bar width
`dodge.width`	numeric. dodge width for error bar and jitter (prevent overlapping)
`jitter`	logical, plot jitter or not, default TRUE
`jitter.alpha`	numeric. jitter transparency
`jitter.width`	numeric. jitter width in error bar
`line`	character. line connect error bar, default uses mean, can be set as 'median', NULL (no line)
`line.color`	character. connection line color, only available when group = NULL
`xlab`	character. x axis label
`ylab`	character. y axis label
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`group.lab`	character. label of group variable
`title`	character. title of plot
`xLevel`	character. A character vector indicating the order of the elements of `x` to be shown on x-axis if `is.null(x)==FALSE`.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list of the following 9 elements: “data”, “layers”, “scales”, “mapping”, “theme”, “coordinates”, “facet”, “plot_env”, “labels”.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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") 


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")

Compare Patterns of Two Outcomes in One Scatter Plot

Description

Compare patterns of two outcomes with different scales across the range of the common predictor using error bar plots. Each bar plot displays mean $+/-$ standard error.

Usage

BiAxisErrBar(dat, 
	     group, 
	     y.left, 
	     y.right, 
	     title = "Bi-Axis Error Bar Plot",
	     xlab = group, 
	     ylab.left = y.left, 
	     ylab.right = y.right, 
	     legendLabel = "y axis variables",
	     delta = NULL, 
	     cvThresh = 0.01, 
             Ntick = 5,
             semFlag = TRUE, #semFlag = FALSE if SE is required
	     GroupLevel = NULL,
	     addThemeFlag = FALSE
	     )
BiAxisErrBar(dat, 
	     group, 
	     y.left, 
	     y.right, 
	     title = "Bi-Axis Error Bar Plot",
	     xlab = group, 
	     ylab.left = y.left, 
	     ylab.right = y.right, 
	     legendLabel = "y axis variables",
	     delta = NULL, 
	     cvThresh = 0.01, 
             Ntick = 5,
             semFlag = TRUE, #semFlag = FALSE if SE is required
	     GroupLevel = NULL,
	     addThemeFlag = FALSE
	     )

Arguments

`dat`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`group`	character. A categorical variable in `data` that indicates the predictor.
`y.left`	character. The column name of `data` that indicates the first outcome variable, the error bar plot of which will be drawn on the left side.
`y.right`	character. The column name of `data` that indicates the second outcome variable, the error bar plot of which will be drawn on the right side.
`title`	character. title of the plot.
`xlab`	character. Label for the x-axis.
`ylab.left`	character. Label for the left-side y-axis.
`ylab.right`	character. Label for the right-side y-axis.
`legendLabel`	character. Legend label.
`delta`	numeric. A small number so that the second error bar plot will shift `delta` distance from the first error bar plot.
`cvThresh`	numeric. A small positive number. If the coefficient of variation (CV) is smaller than `cvThresh`, then the scaling factor will be set to one.
`Ntick`	integer. Number of ticks on the two y-axes.
`semFlag`	logical. Indicating if standard error of the mean (`semFlag = TRUE`) or standard error (`semFlag = FALSE`) will be used to construct the error bars.
`GroupLevel`	A vector of unique values of `group` indicating the order of `group` shown in x-axis.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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")

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")

Compare Groups Based on Boxplots Across Time

Description

This function is to compare groups using boxplots at each time point. In addition, line segments are used to connect the mean/median of each boxplot of the same group across time to show the differences between the mean trajectories.

Usage

Box(
    data, 
    x = NULL, 
    y, 
    group = NULL, 
    fill = NULL, 
    theme_classic = TRUE, 
    fill.alpha = 0.7, 
    box.width = 0.5, 
    dodge.width = 0.8, 
    jitter = TRUE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.2, 
    point.size = 1, 
    xlab = x, 
    ylab = y, 
    group.lab = group, 
    fill.lab = group, 
    title = "Boxplot", 
    line = "mean", 
    line.color = "black", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)
Box(
    data, 
    x = NULL, 
    y, 
    group = NULL, 
    fill = NULL, 
    theme_classic = TRUE, 
    fill.alpha = 0.7, 
    box.width = 0.5, 
    dodge.width = 0.8, 
    jitter = TRUE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.2, 
    point.size = 1, 
    xlab = x, 
    ylab = y, 
    group.lab = group, 
    fill.lab = group, 
    title = "Boxplot", 
    line = "mean", 
    line.color = "black", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`x`	character. The column name of `data` that indicates the first grouping variable (e.g. observation time)
`y`	character. The column name of `data` that indicates the variable on y axis
`group`	character. The column name of `data` that indicates the subject groups (e.g., treatment group). The boxplots will be drawn for each of the subject group within each category of `x`.
`fill`	boxplot inside color indicated by the categories of `group`
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`fill.alpha`	boxplot transparency
`box.width`	boxplot width
`dodge.width`	dodge width for boxplot and jitter (prevent overlapping)
`jitter`	logical. plot jitter or not, default TRUE
`jitter.alpha`	jitter transparency
`jitter.width`	jitter width in boxplot
`point.size`	size of a jitter point
`xlab`	character. x axis label
`ylab`	character. y axis label
`group.lab`	label of group variable
`fill.lab`	label of fill variable
`title`	character. title of plot
`line`	line connect boxes, default plot mean, can be set as 'median', or NULL (no line)
`line.color`	connection line color, only available when group = NULL
`xLevel`	character. A character vector indicating the order of the elements of `x` to be shown on x-axis if `is.null(x)==FALSE`.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with the following 9 elements: data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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") 


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")

Compare Boxplots with ROC Curve

Description

Compare boxplots with ROC curve. The value of the variable y will be jittered shown on the boxplots. The area under ROC curve will also be calculated and shown in the plot of ROC curve.

Usage

BoxROC(
    data, 
    group.var, 
    y, 
    box.xlab = group.var, 
    box.ylab = y, 
    box.group.lab = group.var, 
    jitter.alpha = 0.8, 
    jitter.width = 0.1, 
    point.size = 3, 
    roc.xlab = "Specificity", 
    roc.ylab = "Sensitivity",
    addThemeFlag = TRUE)
BoxROC(
    data, 
    group.var, 
    y, 
    box.xlab = group.var, 
    box.ylab = y, 
    box.group.lab = group.var, 
    jitter.alpha = 0.8, 
    jitter.width = 0.1, 
    point.size = 3, 
    roc.xlab = "Specificity", 
    roc.ylab = "Sensitivity",
    addThemeFlag = TRUE)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`group.var`	character. The column name of `data` that indicates the two subject groups. It also indicates the color of the two boxplots.
`y`	character. The column name of `data` that indicates the variable, for which the box will be drawn.
`box.xlab`	character. boxplot x axis label (default: `group.var`)
`box.ylab`	character. boxplot y axis label (default: `y`)
`box.group.lab`	character. boxplot legend label (default: `group.var`)
`jitter.alpha`	numeric. transparency of jitters
`jitter.width`	numeric. width of jitters
`point.size`	size of a jitter point
`roc.xlab`	character. roc curve x axis label (default: `Specificities`)
`roc.ylab`	character. roc curve y axis label (default: `Sensitivities`)
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.

Value

A list with the following 12 elements: grobs, layout, widths, heights, respect, rownames, colnames, name, gp, vp, children, childrenOrder.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)

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)

Plot the Cross-Validation Curve Produced by cv.glmnet

Description

Plots the cross-validation curve, and upper and lower standard error curves, as a function of the values of the tuning parameter lambda.

Usage

cv_glmnet_plot(x, 
	       y, 
	       family = "binomial", 
	       addThemeFlag = TRUE, 
	       ...)
cv_glmnet_plot(x, 
	       y, 
	       family = "binomial", 
	       addThemeFlag = TRUE, 
	       ...)

Arguments

`x`	a matrix with rows are subjects and columns are numeric variables (predictors). No missing values are allowed.
`y`	a vector of response. The number of elements of `y` is the same as the number of rows of `x`.
`family`	character. Indicating response type. see the description in `glmnet`.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for `glmnet` function.

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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")

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")

Compare Groups Based on Density Plots

Description

Compare groups based on density plots.

Usage

Den(
    data, 
    y, 
    group = NULL, 
    fill = group, 
    border.color = NULL, 
    inner.color = NULL, 
    theme_classic = TRUE, 
    xlab = y, 
    ylab = "density", 
    group.lab = group, 
    title = "Density plot", 
    alpha = 0.3, 
    addThemeFlag = TRUE,
    ...)
Den(
    data, 
    y, 
    group = NULL, 
    fill = group, 
    border.color = NULL, 
    inner.color = NULL, 
    theme_classic = TRUE, 
    xlab = y, 
    ylab = "density", 
    group.lab = group, 
    title = "Density plot", 
    alpha = 0.3, 
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`y`	character. The column name of `data` that indicates the variable, for which the histogram will be drawn. The string `y` can also indicate a function of the variable, e.g., $\log(y)$ .
`group`	character. The column name of `data` that indicates the subject groups. The density will be drawn for each of the subject group. It also indicates the border colors of the densities.
`fill`	grouping variable, density inside color
`border.color`	density border color, only available when group & fill are NULL
`inner.color`	density inside color, only available when group & fill are NULL
`theme_classic`	Use classic background without grids (default: FALSE)
`xlab`	x axis label
`ylab`	y axis label
`group.lab`	label of group variable
`title`	title of plot
`alpha`	transparency of density inside color
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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') 

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')

Compare Groups Based on Dendrogram

Description

Compare groups based on dendrogram. The nodes of the dendrogram will be colored by group.

Usage

Dendro(
    x, 
    group = NULL, 
    xlab = NULL, 
    ylab = NULL, 
    title = NULL, 
    cor.use = "pairwise.complete.obs", 
    cor.method = "pearson", 
    distance = "rawdata", 
    distance.method = "euclidean", 
    hclust.method = "complete", 
    yintercept = NULL, 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)
Dendro(
    x, 
    group = NULL, 
    xlab = NULL, 
    ylab = NULL, 
    title = NULL, 
    cor.use = "pairwise.complete.obs", 
    cor.method = "pearson", 
    distance = "rawdata", 
    distance.method = "euclidean", 
    hclust.method = "complete", 
    yintercept = NULL, 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)

Arguments

`x`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`group`	character. The column name of `data` that indicates the subject groups. The nodes of the dendrogram will be colored by info provided by `group`.
`xlab`	x axis label
`ylab`	y axis label
`title`	title of the plot
`cor.use`	character. Indicate which data will be used to compute correlation coefficients. It can take values “everything”, “all.obs”, “complete.obs”, “na.or.complete”, “pairwise.complete.obs”.
`cor.method`	character. Indicate which type of correlation coefficients will be calculated: “pearson”, “kendall”, “spearman”.
`distance`	character. Indicate which type of data will be used to calculate distance: “rawdata” (i.e., using raw data to calculate distance), “cor” (i.e., using correlation coefficients as distance), “1-cor” (i.e., using ( $1-$ correlation coefficients) as distance), “1-\|cor\|” (i.e., using ( $1-$ \|correlation coefficients\|) as distance).
`distance.method`	character. Available when ‘distance = "rawdata"’. Indicate the definition of distance: distance used in calculate dist “rawdata” (i.e., using raw data to calculate distance), “cor” (i.e., using correlation coefficients as distance), “1-cor” (i.e., using ( $1-$ correlation coefficients) as distance), “1-\|cor\|” (i.e., using ( $1-$ \|correlation coefficients\|) as distance).
`hclust.method`	character. Indicate which agglomeration method will be used to perform hierarchical clustering. This should be (an unambiguous abbreviation of) one of “ward.D”, “ward.D2”, “single”, “complete”, “average”, “mcquitty”, “median”, or “centroid”. Please refer to `hclust`.
`yintercept`	numeric. A line indicating the height of the dendrogram, for example, indicating where the dendrogram should be cut to obtain clusters.
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)



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)

A Dataset for Differential Correlation Analysis

Description

A dataset for differential correlation analysis.

Usage

data("diffCorDat")data("diffCorDat")

Format

A data frame with 100 observations on the following 3 variables.

probe1: numeric. expression level for probe1
probe2: numeric. expression level for probe2
grp: character. a factor with levels cases controls

Details

The simulated data set contains expression levels of 2 gene probes for 50 cases and 50 controls. The expression levels of probe1 are generated from $N(0, 1)$ . The expression levels of probe2 for controls are also generated from $N(0, 1)$ . The expression levels of probe 2 for cases are generated from the formula $probe2_{i} = -probe1_{i} + e_i$ , $i=1, \ldots, nCases$ , where $e_i\sim N(0, 0.3^2)$ .

That is, the expression levels of probe 1 and probe 2 are negatively correlated in cases, but not correlated in controls.

Examples

data(diffCorDat)

print(dim(diffCorDat))
print(diffCorDat[1:2,])
data(diffCorDat)

print(dim(diffCorDat))
print(diffCorDat[1:2,])

Compare Groups Based on dotplots Across Time

Description

This function is to compare groups using dotplots at each time point. In addition, line segments are used to connect the mean/median of each dotplot of the same group across time to show the differences between the mean trajectories. Also, for each dotplot the barplot of mean $+/-$ standard error will be plot.

Usage

ErrBar(
    data, 
    x = NULL, 
    y, 
    group = NULL,
    semFlag = TRUE,
    xFlag = FALSE,
    bar.width = 0.5, 
    dodge.width = 0.8, 
    jitter = TRUE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.1, 
    line = "mean", 
    line.color = "black", 
    xlab = x, 
    ylab = line, 
    theme_classic = TRUE, 
    group.lab = group, 
    title = "Dot plots", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)
ErrBar(
    data, 
    x = NULL, 
    y, 
    group = NULL,
    semFlag = TRUE,
    xFlag = FALSE,
    bar.width = 0.5, 
    dodge.width = 0.8, 
    jitter = TRUE, 
    jitter.alpha = 0.7, 
    jitter.width = 0.1, 
    line = "mean", 
    line.color = "black", 
    xlab = x, 
    ylab = line, 
    theme_classic = TRUE, 
    group.lab = group, 
    title = "Dot plots", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`x`	character. The column name of `data` that indicates the first grouping variable
`y`	character. The column name of `data` that indicates the variable on y axis
`group`	character. The column name of `data` that indicates the subject groups. The dotplots will be drawn for each of the subject group within each category of `x`.
`semFlag`	logical. Indicate if sem or se should be used to draw error bar
`xFlag`	logical. Indicate if `x` should be treated as continuous (`xFlag=TRUE`)
`bar.width`	numeric. error bar width
`dodge.width`	numeric. dodge width for error bar and jitter (prevent overlapping)
`jitter`	logical, plot jitter or not, default TRUE
`jitter.alpha`	numeric. jitter transparency
`jitter.width`	numeric. jitter width in error bar
`line`	character. line connect error bar, default uses mean, can be set as 'median', NULL (no line)
`line.color`	character. connection line color, only available when group = NULL
`xlab`	character. x axis label
`ylab`	character. y axis label
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`group.lab`	character. label of group variable
`title`	character. title of plot
`xLevel`	character. A character vector indicating the order of the elements of `x` to be shown on x-axis if `is.null(x)==FALSE`.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list of the following 9 elements: “data”, “layers”, “scales”, “mapping”, “theme”, “coordinates”, “facet”, “plot_env”, “labels”.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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") 


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")

A Simulated Gene Expression Dataset

Description

A simulated gene expression dataset for differential expression analysis.

Usage

data("esSim")data("esSim")

Format

The format is: Formal class 'ExpressionSet' [package "Biobase"] with expression levels of 100 probes for 20 samples.

The phenotype data contain 2 phenotype variables: sid (subject id) and grp (group indicator: 1 stands for case; 0 stands for control).

The feature data contain 4 feature variables: probeid (probe id), gene (fake gene symbol), chr (fake chromosome number), and memProbes (probe significance indicator: 1 stands for probes over-expressed (OE) in cases; -1 stands for probes under-expressed (UE) in cases; and 0 stands for non-differentially expressed (NE) probes). There are 3 OE probes, 2 UE probes, and 95 NE probes.

Details

The dataset was generated based on the R code in the manual of the function lmFit of the R Bioconductor package limma. There are 100 probes and 20 samples (10 controls and 10 cases). The first 3 probes are over-expressed in cases. The 4-th and 5-th probes are under-expressed in cases. The remaining 95 probes are non-differentially expressed between cases and controls. Expression levels for 100 probes were first generated from normal distribution with mean 0 and standard deviation varying between probes ( $sd=0.3\sqrt{4/\chi^2_4}$ ). For the 3 OE probes, we add 2 to the expression levels of the 10 cases. For the 2 UE probes, we subtract 2 from the expression levels of the 10 cases.

References

Please see the example in the manual for the function lmFit in the R Bioconductor package limma.

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))

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))

An ExpressionSet Object Storing Simulated Genotype Data

Description

An ExpressionSet object storing simulated genotype data with 10 SNPs and 100 subjects.

Usage

data("genoSim")data("genoSim")

Details

The simulated genotype data contain 50 cases and 50 controls. Each subject has genotype data for 10 SNPs. The first 2 SNPs have different minor allele frequencies (MAFs) between cases and controls (MAF for cases is 0.4 and MAF for controls is 0.2). We assume Hardy Weinberg Equilibrium. The remaining 8 SNPs have the same MAF ( $MAF=0.2$ ) in both cases and controls.

Examples

data(genoSim)

print(genoSim)
data(genoSim)

print(genoSim)

Heatmap with Row Names Colored by Group

Description

Heatmap with row names colored by group.

Usage

Heat(data, 
     group = NULL, 
     fontsize_row=10,
     fontsize_col=10, 
     scale = "none",
     cluster_rows = TRUE,
     cluster_cols = TRUE,
     color = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(100),
     angle_col = c("270", "0", "45", "90", "315"), 
     ...) 
Heat(data, 
     group = NULL, 
     fontsize_row=10,
     fontsize_col=10, 
     scale = "none",
     cluster_rows = TRUE,
     cluster_cols = TRUE,
     color = colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(100),
     angle_col = c("270", "0", "45", "90", "315"), 
     ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects. Except the column indicating subject group, all columns of `data` should be numeric.
`group`	character. The column name of `data` that indicates the subject groups. The row names of the heatmap will be colored based on `group`.
`fontsize_col`	x axis label font size
`fontsize_row`	y axis label font size
`scale`	character. Indicate how data will be scaled: “none” (i.e., no scaling), “row” (i.e., row scaled), “column” (i.e., column scaled).
`cluster_rows`	logic. Indicates if rows should be clustered.
`cluster_cols`	logic. Indicates if columns should be clustered.
`color`	vector indicating colors used in heatmap
`angle_col`	angle of the column labels. Please refer to the manual in `pheatmap`
`...`	other input parameters for facet & theme.

Value

A list with 10 elements: “rowInd”, “colInd”, “call”, “carpet”, “rowDendrogram”, “colDendrogram”, “breaks”, “col”, “colorTable”, “layout”.

Note

This function is based on the function pheatmap in pheatmap R package.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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')

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')

Compare Groups Based on Histograms

Description

Compare groups based on histograms.

Usage

Hist(
    data, 
    y, 
    group = NULL, 
    fill = group, 
    border.color = NULL, 
    inner.color = NULL, 
    theme_classic = TRUE, 
    bins = NULL, 
    binwidth = NULL, 
    alpha = 0.8, 
    xlab = y, 
    ylab = "count", 
    group.lab = group, 
    title = "Histogram", 
    addThemeFlag = TRUE,
    ...)
Hist(
    data, 
    y, 
    group = NULL, 
    fill = group, 
    border.color = NULL, 
    inner.color = NULL, 
    theme_classic = TRUE, 
    bins = NULL, 
    binwidth = NULL, 
    alpha = 0.8, 
    xlab = y, 
    ylab = "count", 
    group.lab = group, 
    title = "Histogram", 
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`y`	character. The column name of `data` that indicates the variable, for which the histogram will be drawn. The string `y` can also indicate a function of the variable, e.g., $\log(y)$ .
`group`	character. The column name of `data` that indicates the subject groups. The histogram will be drawn for each of the subject group. It also indicates the border colors of the histograms.
`fill`	character. The column name of `data` that indicates the subject groups. It indicates the inside colors of the histograms.
`border.color`	Histogram border color, only available when group & fill are NULL.
`inner.color`	Histogram inside color, only available when group & fill are NULL.
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`bins`	integer. number of bins of histogram (default: 30).
`binwidth`	Bin width of histogram.
`alpha`	Transparency of histogram inside color.
`xlab`	x axis label
`ylab`	y axis label
`group.lab`	label of group variable
`title`	title of the plot
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with the following 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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') 


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')

Plot of Variable Importance

Description

Plot of variable importance based on results from randomForest or gbm.

Usage

ImpPlot(model, 
	theme_classic = TRUE, 
	n.trees = NULL, 
	addThemeFlag = TRUE,
	...)
ImpPlot(model, 
	theme_classic = TRUE, 
	n.trees = NULL, 
	addThemeFlag = TRUE,
	...)

Arguments

`model`	An object returned by `randomForest` or `gbm`
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`n.trees`	integer. The number of trees used to generate the plot used in the function `summary.gbm` in the R library `gbm`. Only the first n.trees trees will be used.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)

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)

Improved Function for Obtaining Principal Components

Description

Calculate principal components when data contains missing values.

Usage

iprcomp(dat, center = TRUE, scale. = FALSE)
iprcomp(dat, center = TRUE, scale. = FALSE)

Arguments

`dat`	n by p matrix. rows are subjects and columns are variables
`center`	logical. Indicates if each row of `dat` needs to be mean-centered
`scale.`	logical. Indicates if each row of `dat` needs to be scaled to have variance one

Details

We first set missing values as median of the corresponding variable, then call the function prcomp. This is a very simple solution. The user can use their own imputation methods before calling prcomp.

Value

A list of 3 elements

`sdev`	square root of the eigen values
`rotation`	a matrix with columns are eigen vectors, i.e., projection direction
`x`	a matrix with columns are principal components

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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))

# 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))

Compare Groups Based on Trajectory Plots

Description

Compare groups based on trajectory plots. Trajectories belonging to different groups will have different colors.

Usage

LinePlot(
    data, 
    x, 
    y, 
    sid,
    group = NULL, 
    xFlag = FALSE,
    points = TRUE, 
    point.size = 1, 
    theme_classic = TRUE, 
    xlab = x, 
    ylab = y, 
    title = "Trajectory plot", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)
LinePlot(
    data, 
    x, 
    y, 
    sid,
    group = NULL, 
    xFlag = FALSE,
    points = TRUE, 
    point.size = 1, 
    theme_classic = TRUE, 
    xlab = x, 
    ylab = y, 
    title = "Trajectory plot", 
    xLevel = NULL,
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`x`	character. The column name of `data` that indicates the time.
`y`	character. The column name of `data` that indicates the variable on y axis
`sid`	character. The column name of `data` that indicates the subject id.
`group`	character. The column name of `data` that indicates the subject groups. The trajectories of subjects in the same group will have the same color.
`xFlag`	logical. Indicate if `x` should be treated as continuous (`xFlag=TRUE`)
`points`	logical. Indicates if points will be added to the trajectories on the coordinate (x, y).
`point.size`	numeric. size of the data points on the trajectories
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`xlab`	character. x axis label
`ylab`	character. y axis label
`title`	character. title of plot
`xLevel`	character. A character vector indicating the order of the elements of `x` to be shown on x-axis if `is.null(x)==FALSE`.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with the following 9 elements: data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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')

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')

A Simulated Dataset for Longitudinal Data Analysis

Description

A simulated dataset for longitudinal data analysis.

Usage

data("longDat")data("longDat")

Format

A data frame with 540 observations on the following 4 variables.

sid: subject id
time: time points. A factor with levels time1 time2 time3 time4 time5 time6
y: numeric. outcome variable
grp: subject group. A factor with levels grp1 grp2 grp3

Details

The dataset is generated from the following mixed effects model for repeated measures:

$y_{ij}=\beta_{0i}+\beta_1 t_{j} + \beta_2 grp_{2i} + \beta_3 grp_{3i} + \beta_4 \times\left(t_{j}\times grp_{2i}\right) + \beta_5 \times\left(t_{j}\times grp_{3i}\right) +\epsilon_{ij},$

where $y_{ij}$ is the outcome value for the $i$ -th subject measured at $j$ -th time point $t_{j}$ , $grp_{2i}$ is a dummy variable indicating if the $i$ -th subject is from group 2, $grp_{3i}$ is a dummy variable indicating if the $i$ -th subject is from group 3, $\beta_{0i}\sim N\left(\beta_0, \sigma_b^2\right)$ , $\epsilon_{ij}\sim N\left(0, \sigma_e^2\right)$ , $i=1,\ldots, n, j=1, \ldots, m$ , $n$ is the number of subjects, and $m$ is the number of time points.

When $t_j=0$ , the expected outcome value is

$E\left(y_{ij}\right)=\beta_0+\beta_2 dose_{2i} + \beta_3 dose_{3i}.$

Hence, we have at baseline

$E\left(y_{ij}\right)=\beta_0,\; \mbox{for dose 1 group}.$

$E\left(y_{ij}\right)=\beta_0 + \beta_2,\; \mbox{for dose 2 group}.$

$E\left(y_{ij}\right)=\beta_0 + \beta_3,\; \mbox{for dose 3 group}.$

For dose 1 group, the expected outcome values across time is

$E\left(y_{ij}\right)=\beta_0+\beta_1 t_{j}.$

We also can get the expected difference of outcome values between dose 2 group and dose 1 group, between dose 3 group and dose 1 group, and between dose 3 group and dose 2 group:

$E\left(y_{ij} - y_{i'j}\right) =\beta_2+\beta_4 t_{j},\;\mbox{for subject $i$ in dose 2 group and subject $i'$ in dose 1 group},$

$E\left(y_{kj} - y_{i'j}\right) =\beta_3+\beta_5 t_{j},\;\mbox{for subject $k$ in dose 3 group and subject $i'$ in dose 1 group},$

$E\left(y_{kj} - y_{ij}\right) =\left(\beta_3-\beta_2\right)+\left(\beta_5-\beta_4\right) t_{j},\;\mbox{for subject $i$ in dose 3 group and subject $i$ in dose 2 group}.$

We set $n=90$ , $m=6$ , $\beta_0=5$ , $\beta_1=0$ , $\beta_2=0$ , $\beta_3=0$ , $\beta_4=2$ , $\beta_5=-2$ , $\sigma_e=1$ , $\sigma_b=0.5$ , and $t_{ij}=j, j=1, \ldots, m$ .

That is, the trajectories for dose 1 group are horizontal with mean intercept at $5$ , the trajectories for dose 2 group are linearly increasing with slope $2$ and mean intercept $5$ , and the trajectories for dose 3 group are linearly decreasing with slope $-2$ and mean intercept $5$ .

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))
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))

Scatter Plot of 2 Specified Principal Components

Description

Scatter plot of 2 specified principal components. The size of the data points on the PCA plot indicates the Mahalanobis distance (distance between each point and mean value).

Usage

PCA_score(
    prcomp_obj, 
    data, 
    dims = c(1, 2),
    color = NULL, 
    MD = TRUE, 
    loadings = FALSE, 
    loadings.color = "black", 
    loadings.label = FALSE,
    title = "pca plot",
    addThemeFlag = TRUE)
PCA_score(
    prcomp_obj, 
    data, 
    dims = c(1, 2),
    color = NULL, 
    MD = TRUE, 
    loadings = FALSE, 
    loadings.color = "black", 
    loadings.label = FALSE,
    title = "pca plot",
    addThemeFlag = TRUE)

Arguments

`prcomp_obj`	the object returned by the function `prcomp`.
`data`	A data frame. Rows are subjects; Columns are variables describing the subjects. The object `prcomp_obj` is based on `data`
`dims`	a numeric vector with 2 elements indicating which two principal components will be used to draw scatter plot.
`color`	character. The column name of `data` that indicates the subject groups. The data points on the PCA plot will be colored by the group info.
`MD`	logical. Indicate if the Mahalanobis distance (distance between each point and mean value) would be used to indicate the size of data points on the PCA plot
`loadings`	logical. Indicate if loading plot would be superimposed on the PCA plot. (default: FALSE)
`loadings.color`	character. Indicate the color of the loading axis.
`loadings.label`	logical. Indicating if loading labels should be added to the plot. (default: FALSE)
`title`	character. Figure title.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)

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)

Principal Variance Component Analysis (PVCA)

Description

Plot of weighted average proportion variance versus effects in principal variance component analysis (PVCA).

Usage

PVCA(
    clin_data, 
    clin_subjid, 
    gene_data, 
    pct_threshold = 0.8, 
    batch.factors, 
    theme_classic = FALSE, 
    addThemeFlag = TRUE,
    ...)
PVCA(
    clin_data, 
    clin_subjid, 
    gene_data, 
    pct_threshold = 0.8, 
    batch.factors, 
    theme_classic = FALSE, 
    addThemeFlag = TRUE,
    ...)

Arguments

`clin_data`	A data frame containing clinical information, including an id variable that corresponds to rownames of `gene_data`
`clin_subjid`	character. The column name of `clin_data` that indicates subject id. It corresponds to the rowname of `gene_data`.
`gene_data`	A data frame with genes as rows and subjects as columns.
`pct_threshold`	numeric. The percentile value of the minimum amount of the variabilities that the selected principal components need to explain
`batch.factors`	character. A vector of factors that the mixed linear model will be fit on.
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet, plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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"))

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"))

Draw Stacked Bar Plots

Description

Draw stacked bar plots.

Usage

stackedBarPlot(dat, 
	       catVar, 
	       group, 
	       xlab = catVar, 
	       ylab = "Count", 
	       group.lab = group, 
	       title = "Stacked barplots of counts", 
	       catVarLevel = NULL, 
	       groupLevel = NULL, 
	       addThemeFlag = TRUE)
stackedBarPlot(dat, 
	       catVar, 
	       group, 
	       xlab = catVar, 
	       ylab = "Count", 
	       group.lab = group, 
	       title = "Stacked barplots of counts", 
	       catVarLevel = NULL, 
	       groupLevel = NULL, 
	       addThemeFlag = TRUE)

Arguments

`dat`	A data frame object. Rows are subjects and columns are variables.
`catVar`	character. The name of the cateogrical variable to be shown in x-axis.
`group`	character. The name of variable indicating groups of subjects.
`xlab`	character. Label for x-axis.
`ylab`	character. Label for y-axis.
`group.lab`	character. Label for group in legend.
`title`	character. Figure title.
`catVarLevel`	character. A vector indicating the order of the unique elements of `catVar` should be shown in x-axis.
`groupLevel`	character. A vector indicating the order of the unique elements of `group` should be shown in figure and in legend.
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.

Value

A list of the following 9 elements: “data”, “layers”, “scales”, “mapping”, “theme”, “coordinates”, “facet”, “plot_env”, “labels”.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)

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)

The Wrapper Function Incorporating All Wrapper Functions in statVisual

Description

The wrapper function incorporating all wrapper functions in statVisual.

Usage

statVisual(type, ...)
statVisual(type, ...)

Arguments

`type`	character. Indicate the functions to be called. It can take the following values: “BiAxisErrBar”, “Box”, “BoxROC”, “cv_glmnet_plot”, “Den”, “Dendro”, “ErrBar”, “Heat”, “Hist”, “ImpPlot”, “iprcomp”, “LinePlot”, “PCA_score”, “PVCA”, “statVisual”, “Volcano”, “XYscatter”.
`...`	input parameters for the functions specified by `type`.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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') 


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')

Volcano Plot

Description

Volcano plot with the option to label the significant results.

Usage

Volcano(
    resFrame, 
    stats, 
    p.value, 
    group = NULL, 
    xlab = "logFC", 
    ylab = "-log10(p value)", 
    title = NULL, 
    vline.col = "orange", 
    hline.col = "dodgerblue", 
    vline = list(xintercept = c(-1, 1), label = c(-1, 1)), 
    hline = list(
        yintercept = c(-log10(0.05), 
                       -log10(0.05/nrow(resFrame)), 
                       -log10(max(resFrame[p.adjust(resFrame[, p.value], 
                                       method = "fdr") <= 0.05, p.value]))), 
        label = c("p value: 0.05", "Bonferroni: 0.05", "FDR: 0.05")), 
    rowname.var = NULL, 
    point.size = 3, 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)
Volcano(
    resFrame, 
    stats, 
    p.value, 
    group = NULL, 
    xlab = "logFC", 
    ylab = "-log10(p value)", 
    title = NULL, 
    vline.col = "orange", 
    hline.col = "dodgerblue", 
    vline = list(xintercept = c(-1, 1), label = c(-1, 1)), 
    hline = list(
        yintercept = c(-log10(0.05), 
                       -log10(0.05/nrow(resFrame)), 
                       -log10(max(resFrame[p.adjust(resFrame[, p.value], 
                                       method = "fdr") <= 0.05, p.value]))), 
        label = c("p value: 0.05", "Bonferroni: 0.05", "FDR: 0.05")), 
    rowname.var = NULL, 
    point.size = 3, 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)

Arguments

`resFrame`	A data frame stored information about the results, including gene id, statistic (e.g., log fold change, odds ratio), p-value, and significance of a gene.
`stats`	character. The column name of `resFrame` that indicates the effect of a gene.
`p.value`	character. The column name of `resFrame` that indicates the p-value.
`group`	character. The column name of `resFrame` that indicates the significance of a gene.
`xlab`	x axis label
`ylab`	y axis label
`title`	title of the plot
`vline.col`	color of the vertical lines (default: “orange”)
`hline.col`	color of the horizontal lines (default: “dodgerblue”)
`vline`	A list with two elements: “xintercept” and “label”, where the former element is a numeric vector indicating the x-axis location to draw vertical color lines and the latter element is list of labels for the elements in “xintercept”.
`hline`	A list with two elements: “yintercept” and “label”, where the former element is a numeric vector indicating the y-axis location to draw horizontal color lines and the latter element is list of labels for the elements in “xintercept”.
`rowname.var`	character. The column name of `resFrame` that indicates which variable will be used to label the significant results in the volcano plot. The elements of this column for non-significant results should be set to be `NA`.
`point.size`	size of data points in the plot.
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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)


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)

Compare Groups Based on Scatter Plots

Description

Compare groups based on scatter plots.

Usage

XYscatter(
    data, 
    x, 
    y, 
    group = NULL, 
    alpha = 1, 
    point.size = 3, 
    xlab = x, 
    ylab = y, 
    group.lab = group, 
    title = "Scatter plot", 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)
XYscatter(
    data, 
    x, 
    y, 
    group = NULL, 
    alpha = 1, 
    point.size = 3, 
    xlab = x, 
    ylab = y, 
    group.lab = group, 
    title = "Scatter plot", 
    theme_classic = TRUE, 
    addThemeFlag = TRUE,
    ...)

Arguments

`data`	A data frame. Rows are subjects; Columns are variables describing the subjects.
`x`	character. The column name of `data` that indicates the variable on the x axis of the scatter plot
`y`	character. The column name of `data` that indicates the variable on the y axis of the scatter plot
`group`	character. The column name of `data` that indicates the subject groups. The scatter plot will be drawn for each of the subject group. It also indicates the colors of the data points in the scatter plots.
`alpha`	Transparency of histogram inside color.
`point.size`	numeric. Indicate the size of the data points
`xlab`	x axis label
`ylab`	y axis label
`group.lab`	label of group variable
`title`	title of the plot
`theme_classic`	logical. Use classic background without grids (default: TRUE).
`addThemeFlag`	logical. Indicates if light blue background and white grid should be added to the figure.
`...`	other input parameters for facet & theme

Value

A list with 9 elements. data, layers, scales, mapping, theme, coordinates, facet plot_env, and labels.

Author(s)

Wenfei Zhang <[email protected]>, Weiliang Qiu <[email protected]>, Xuan Lin <[email protected]>, Donghui Zhang <[email protected]>

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')

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')

Package 'statVisual'

Help Index

Compare Groups Based on Barplots Across Time

Description

Usage

Arguments

Value

Author(s)

Examples

Compare Patterns of Two Outcomes in One Scatter Plot

Description

Usage

Arguments

Value

Author(s)

Examples

Compare Groups Based on Boxplots Across Time

Description

Usage

Arguments

Value

Author(s)

Examples

Compare Boxplots with ROC Curve

Description

Usage

Arguments

Value

Author(s)

Examples

Plot the Cross-Validation Curve Produced by cv.glmnet

Description

Usage

Arguments

Value

Author(s)

Examples

Compare Groups Based on Density Plots

Description

Usage

Arguments

Value

Author(s)

Examples

Compare Groups Based on Dendrogram

Description

Usage

Arguments

Value

Author(s)

Examples

A Dataset for Differential Correlation Analysis

Description

Usage

Format

Details

Examples

Compare Groups Based on dotplots Across Time

Description

Usage

Arguments

Value

Author(s)

Examples

A Simulated Gene Expression Dataset

Description

Usage

Format

Details

References

Examples

An ExpressionSet Object Storing Simulated Genotype Data

Description

Usage

Details

Examples

Heatmap with Row Names Colored by Group

Description

Usage

Arguments