Package 'AlphaBeta' reference manual

Title:	Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants
Description:	AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging.
Authors:	Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut]
Maintainer:	Yadollah Shahryary Dizaji <[email protected]>
License:	GPL-3
Version:	1.19.0
Built:	2024-09-11 05:56:51 UTC
Source:	https://github.com/bioc/AlphaBeta

Run Model with no selection (ABneutral)

Description

This model assumes that heritable gains and losses in cytosine methylation are selectively neutral.

Usage

ABneutral(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)
ABneutral(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	weight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABneutral RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABneutral"
out <- ABneutral(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABneutral"
out <- ABneutral(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Model with no selection (outneutral)

Description

This model assumes that somatically heritable gains and losses in cytosine methylation are selectively neutral.

Usage

ABneutralSOMA(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)
ABneutralSOMA(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	weight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABneutralSoma RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABneutralSOMA_CG_estimates"
out <- ABneutralSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABneutralSOMA_CG_estimates"
out <- ABneutralSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Run model that considers no accumulation of epimutations (ABnull)

Description

Run model that considers no accumulation of epimutations (ABnull)

Usage

ABnull(pedigree.data, out.dir, out.name)
ABnull(pedigree.data, out.dir, out.name)

Arguments

`pedigree.data`	Generation table name, you can find sample file in
`out.dir`	outputdirectory
`out.name`	name of file

Value

ABnull RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
out.name <- "CG_global_estimates_ABnull"
out <- ABnull(pedigree.data = pedigree,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
out.name <- "CG_global_estimates_ABnull"
out <- ABnull(pedigree.data = pedigree,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Plotting estimates

Description

Plotting Estimating epimutation

Usage

ABplot(
  pedigree.names,
  output.dir,
  out.name,
  alpha = 0.5,
  geom.point.size = 2,
  geom.line.size = 0.9,
  plot.height = 8,
  plot.width = 11,
  plot.type = "both",
  lsq.line = "theory",
  intract = FALSE
)
ABplot(
  pedigree.names,
  output.dir,
  out.name,
  alpha = 0.5,
  geom.point.size = 2,
  geom.line.size = 0.9,
  plot.height = 8,
  plot.width = 11,
  plot.type = "both",
  lsq.line = "theory",
  intract = FALSE
)

Arguments

`pedigree.names`	Models output AB*.Rdata
`output.dir`	output directory
`out.name`	filename
`alpha`	ggplot parameters
`geom.point.size`	ggplot parameters
`geom.line.size`	ggplot parameters
`plot.height`	ggplot parameters
`plot.width`	ggplot parameters
`plot.type`	type of plot (data.only, fit.only, both)
`lsq.line`	Least Square Regression line (theory or pred)
`intract`	to see intarctive plot. (useing plotly)

Value

plot

Examples

# Get some toy data
file <- system.file("extdata/dm/","Col_CG_global_estimates_ABneutral.Rdata", package="AlphaBeta")
ABplot(pedigree.names=file, output.dir=getwd(), out.name="ABneutral")
# Get some toy data
file <- system.file("extdata/dm/","Col_CG_global_estimates_ABneutral.Rdata", package="AlphaBeta")
ABplot(pedigree.names=file, output.dir=getwd(), out.name="ABneutral")

Run model with selection against spontaneous gain of methylation (ABselectMM)

Description

This model assumes that heritable losses of cytosine methylation are under negative selection.

Usage

ABselectMM(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)
ABselectMM(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	nweight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABselectMM RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABselectMM"
out <- ABselectMM(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABselectMM"
out <- ABselectMM(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Model with selection against spontaneous gain of methylation (outselectMM)

Description

This model assumes that somatically heritable gains of cytosine methylation are under negative selection.

Usage

ABselectMMSOMA(
  pedigree.data,
  p0uu,
  eqp,
  eqp.weight,
  Nstarts,
  out.dir,
  out.name
)
ABselectMMSOMA(
  pedigree.data,
  p0uu,
  eqp,
  eqp.weight,
  Nstarts,
  out.dir,
  out.name
)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	weight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABneutralSoma RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABselectMMSOMA_CG_estimates"
out <- ABselectMMSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABselectMMSOMA_CG_estimates"
out <- ABselectMMSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Run model with selection against spontaneous loss of methylation (ABselectUU)

Description

This model assumes that heritable gains of cytosine methylation are under negative selection.

Usage

ABselectUU(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)
ABselectUU(pedigree.data, p0uu, eqp, eqp.weight, Nstarts, out.dir, out.name)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	weight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABselectMM RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABselectUU"
out3 <- ABselectUU(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out3)

#Get some toy data
inFile <- readRDS(system.file("extdata/dm/","output.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 1
Nstarts <- 2
out.name <- "CG_global_estimates_ABselectUU"
out3 <- ABselectUU(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out3)

Model with selection against spontaneous loss of methylation (outselectUU)

Description

This model assumes that somatically heritable gains of cytosine methylation are under negative selection.

Usage

ABselectUUSOMA(
  pedigree.data,
  p0uu,
  eqp,
  eqp.weight,
  Nstarts,
  out.dir,
  out.name
)
ABselectUUSOMA(
  pedigree.data,
  p0uu,
  eqp,
  eqp.weight,
  Nstarts,
  out.dir,
  out.name
)

Arguments

`pedigree.data`	pedigree data.
`p0uu`	initial proportion of unmethylated cytosines.
`eqp`	equilibrium proportion of unmethylated cytosines.
`eqp.weight`	weight assigned to equilibrium function.
`Nstarts`	iterations for non linear LSQ optimization.
`out.dir`	output directory.
`out.name`	output file name.

Value

ABneutralSoma RData file.

Examples

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABselectUUSOMA_CG_estimates"
out <- ABselectUUSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

#Get some toy data
inFile <- readRDS(system.file("extdata/soma/","outputSoma.rds", package="AlphaBeta"))
pedigree <- inFile$Pdata
p0uu_in <- inFile$tmpp0
eqp.weight <- 0.001
Nstarts <- 2
out.name <- "ABselectUUSOMA_CG_estimates"
out <- ABselectUUSOMA(pedigree.data = pedigree,
                  p0uu=p0uu_in,
                  eqp=p0uu_in,
                  eqp.weight=eqp.weight,
                  Nstarts=Nstarts,
                  out.dir=getwd(),
                  out.name=out.name)

summary(out)

Bootstrap analysis with the best model

Description

Bootstrap analysis with the best model

Usage

BOOTmodel(pedigree.data, Nboot, out.dir, out.name)
BOOTmodel(pedigree.data, Nboot, out.dir, out.name)

Arguments

`pedigree.data`	pedigree data.
`Nboot`	number of boot.
`out.dir`	output directory.
`out.name`	output file name.

Value

bootstrap result.

Examples

## Get some toy data
inFile <- system.file("extdata/models/","ABneutral_CG_global_estimates.Rdata", package="AlphaBeta")
Nboot <- 4
out.name <-"Boot_CG_global_estimates_ABneutral"
Bout <- BOOTmodel(pedigree.data=inFile,
                Nboot=Nboot,
                out.dir=getwd(),
                out.name=out.name)
summary(Bout)
## Get some toy data
inFile <- system.file("extdata/models/","ABneutral_CG_global_estimates.Rdata", package="AlphaBeta")
Nboot <- 4
out.name <-"Boot_CG_global_estimates_ABneutral"
Bout <- BOOTmodel(pedigree.data=inFile,
                Nboot=Nboot,
                out.dir=getwd(),
                out.name=out.name)
summary(Bout)

Building Pedigree

Description

calculate divergence times of the pedigree

Usage

buildPedigree(nodelist, edgelist, cytosine = "CG", posteriorMaxFilter = 0.99)
buildPedigree(nodelist, edgelist, cytosine = "CG", posteriorMaxFilter = 0.99)

Arguments

`nodelist`	input file containing information on generation times and pedigree lineages "extdata" called "nodelist.fn"
`edgelist`	input file containing edges
`cytosine`	Type of cytosine (CHH/CHG/CG)
`posteriorMaxFilter`	Filter value, based on posteriorMax

Value

generating divergence matrices file.

Examples

# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
df<-read.csv(file)
df$filename <- gsub("^", paste0(dirname(dirname(file)),"/"), df$filename )
write.csv(df, file = paste0(dirname(file),"/", "tmp_nodelist.fn"), row.names=FALSE, quote=FALSE)
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
file2 <- system.file("extdata/dm/","edgelist.fn", package="AlphaBeta")
buildPedigree(nodelist = file, edgelist=file2, cytosine="CG", posteriorMaxFilter=0.99)
# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
df<-read.csv(file)
df$filename <- gsub("^", paste0(dirname(dirname(file)),"/"), df$filename )
write.csv(df, file = paste0(dirname(file),"/", "tmp_nodelist.fn"), row.names=FALSE, quote=FALSE)
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
file2 <- system.file("extdata/dm/","edgelist.fn", package="AlphaBeta")
buildPedigree(nodelist = file, edgelist=file2, cytosine="CG", posteriorMaxFilter=0.99)

Constructing D-Matrices

Description

Estimating epimutation rates from high-throughput DNA methylation data

Usage

dMatrix(nodelist, cytosine, posteriorMaxFilter)
dMatrix(nodelist, cytosine, posteriorMaxFilter)

Arguments

`nodelist`	list of samples, you can find sample file in "extdata" called "nodelist.fn"
`cytosine`	Type of cytosine (CHH/CHG/CG)
`posteriorMaxFilter`	Filter value, based on posteriorMax ex: >= 0.95 or 0.99

Value

generating divergence matrices file.

Examples

# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
df<-read.csv(file)
df$filename<-sub("^",paste0(dirname(file),"/"),df$filename )
write.csv(df, file = paste0(dirname(file),"tmp_nodelist.fn"),row.names=FALSE,quote=FALSE)
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
dMatrix(file, "CG", 0.99)
# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
df<-read.csv(file)
df$filename<-sub("^",paste0(dirname(file),"/"),df$filename )
write.csv(df, file = paste0(dirname(file),"tmp_nodelist.fn"),row.names=FALSE,quote=FALSE)
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
dMatrix(file, "CG", 0.99)

Comparison of different models and selection of best model

Description

Comparison of different models and selection of best model

Usage

FtestRSS(pedigree.select, pedigree.null)
FtestRSS(pedigree.select, pedigree.null)

Arguments

`pedigree.select`	pedigree model.
`pedigree.null`	ABnull pedigree.

Value

result of Ftest.

Examples

## Get some toy data
file1 <- system.file("extdata/models/","ABneutral_CG_global_estimates.Rdata", package="AlphaBeta")
file2 <- system.file("extdata/models/","ABnull_CG_global_estimates.Rdata", package="AlphaBeta")
out <- FtestRSS(pedigree.select=file1,
                pedigree.null=file2)
## Get some toy data
file1 <- system.file("extdata/models/","ABneutral_CG_global_estimates.Rdata", package="AlphaBeta")
file2 <- system.file("extdata/models/","ABnull_CG_global_estimates.Rdata", package="AlphaBeta")
out <- FtestRSS(pedigree.select=file1,
                pedigree.null=file2)

Plot Pedigree

Description

Plotting Pedigree tree

Usage

plotPedigree(
  nodelist,
  edgelist,
  sampling.design,
  out.pdf = NULL,
  output.dir = NULL,
  plot.width = 11,
  plot.height = 8,
  vertex.label = NULL,
  vertex.size = 12,
  aspect.ratio = 2.5
)
plotPedigree(
  nodelist,
  edgelist,
  sampling.design,
  out.pdf = NULL,
  output.dir = NULL,
  plot.width = 11,
  plot.height = 8,
  vertex.label = NULL,
  vertex.size = 12,
  aspect.ratio = 2.5
)

Arguments

`nodelist`	input file containing information on generation times and pedigree lineages "extdata" called "nodelist.fn"
`edgelist`	input file containing edges "edgelist.fn"
`sampling.design`	"progenitor.intermediate"; "sibling"; "progenitor.endpoint";"tree"
`out.pdf`	output file name
`output.dir`	output directory
`plot.width`	plotting with
`plot.height`	plotting height
`vertex.label`	label vertix
`vertex.size`	size of vertix
`aspect.ratio`	aspect.ration

Value

plot pedigree matrices file.

Examples

# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
file2 <- system.file("extdata/dm/","edgelist.fn", package="AlphaBeta")
plotPedigree(nodelist = file, edgelist=file2, sampling.design="sibling",vertex.label=TRUE,
 out.pdf="Plot", output.dir=getwd() )
# Get some toy data
file <- system.file("extdata/dm/","nodelist.fn", package="AlphaBeta")
file2 <- system.file("extdata/dm/","edgelist.fn", package="AlphaBeta")
plotPedigree(nodelist = file, edgelist=file2, sampling.design="sibling",vertex.label=TRUE,
 out.pdf="Plot", output.dir=getwd() )

Calculating rc.Meth.lvl

Description

Estimating epimutation rates from high-throughput DNA methylation data

Usage

rc.meth.lvl(nodelist, cytosine, posteriorMaxFilter)
rc.meth.lvl(nodelist, cytosine, posteriorMaxFilter)

Arguments

`nodelist`	List of samples, you can find sample file in "extdata" called "nodelist.fn"
`cytosine`	Type of cytosine (CHH/CHG/CG)
`posteriorMaxFilter`	Filter value, based on posteriorMax

Value

rc meth lvl.

Examples

## Get some toy data
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
rc.meth.lvl(file, "CG", 0.99)
## Get some toy data
file <- system.file("extdata/dm/","tmp_nodelist.fn", package="AlphaBeta")
rc.meth.lvl(file, "CG", 0.99)

Package 'AlphaBeta'

Help Index

Run Model with no selection (ABneutral)

Description

Usage

Arguments

Value

Examples

Model with no selection (outneutral)

Description

Usage

Arguments

Value

Examples

Run model that considers no accumulation of epimutations (ABnull)

Description

Usage

Arguments

Value

Examples

Plotting estimates

Description

Usage

Arguments

Value

Examples

Run model with selection against spontaneous gain of methylation (ABselectMM)

Description

Usage

Arguments

Value

Examples

Model with selection against spontaneous gain of methylation (outselectMM)

Description

Usage

Arguments

Value

Examples

Run model with selection against spontaneous loss of methylation (ABselectUU)

Description

Usage

Arguments

Value

Examples

Model with selection against spontaneous loss of methylation (outselectUU)

Description

Usage

Arguments

Value

Examples

Bootstrap analysis with the best model

Description

Usage

Arguments

Value

Examples

Building Pedigree

Description

Usage

Arguments

Value

Examples

Constructing D-Matrices

Description

Usage

Arguments

Value

Examples

Comparison of different models and selection of best model

Description

Usage

Arguments

Value

Examples

Plot Pedigree

Description

Usage

Arguments

Value

Examples