Package 'geneHapR'

Title: Gene Haplotype Statistics, Phenotype Association and Visualization
Description: Import genome variants data and perform gene haplotype Statistics, visualization and phenotype association with 'R'.
Authors: Zhang Renliang [aut, cre], Jia Guanqing [aut]
Maintainer: Zhang Renliang <[email protected]>
License: GPL-3
Version: 1.2.5
Built: 2024-11-05 05:17:47 UTC
Source: https://github.com/zhangrenl/genehapr

Help Index

Add Infomation to Haplotype Results

Description

add annotations to INFO fields used for plotHapTable()

Usage

addINFO(hap,
        tag = "", values = values,
        replace = FALSE, sep = ";")

sites(hap)

Arguments

hap

object of hapResult or hapSummary class
tag

tag names, usually is a single word used before "="
values

annotation for each site. Length of values must be equal with sites in hapResult
replace

whether replace origin INFOs in hapResult or not. Default as FALSE
sep

a character string to separate the terms. Not NA_character_.

Value

object of hapSummary or hapResult class with added/replaced INFOs

See Also

plotHapTable()

plotHapTable()

Examples

data("geneHapR_test")

# length of values must be equal with number of sites in hap result
values <- paste0("newInfo",c(1:9))
hapResult <- addINFO(hapResult, tag = "new", values = values, replace = TRUE)

data("geneHapR_test")

# check how many sites were concluded in hapResult/hapSummary
sites(hapResult)

add promoter to annotation

Description

add promoter to annotation

Usage

addPromoter(anno, PromoterLength = 1500, bedFile = NULL)

Arguments

anno

anotation, imported gff/bed
PromoterLength

the length of promoter region, default as 1500
bedFile

the output bed file name

Examples

data("geneHapR_test")
bed <- addPromoter(gff)

as.haplotype

Description

convert hapSummary or hapResult class into haplotype class (pegas)

Usage

as.haplotype(hap)

Arguments

hap

object of hapSummary or hapResult class

Value

haplotype class

Note

It's not advised for hapSummary or hapResult with indels, due to indels will convert to SNPs with equal length of each indel.

Examples

data("geneHapR_test")
hap <- as.haplotype(hapResult)
hapSummary <- hap_summary(hapResult)
hap <- as.haplotype(hapSummary)

Datasets gff contains a example of gff file used for test of visualization mutations on gene model.

Description

pheno contains a simulated test pheno data used for test of comparison between different haps

vcf, a vcfR object provide a data set for test of seq2hap(). vcf contains indels, snps, biallelic sites and multiallelic sites.

AccINFO a data.frame provide addtional information of accessions, including accession type, source and location.

Display Variants on Gene Model

Description

show variants on gene model using hapSummary and gene annotations

Usage

displayVarOnGeneModel(
  hapSummary,
  gff,
  Chr,
  startPOS,
  endPOS,
  type = "pin",
  cex = 0.7,
  CDS_h = 0.05,
  fiveUTR_h = 0.02,
  threeUTR_h = 0.01,
  geneElement = geneElement,
  hap
)

Arguments

hapSummary, hap

haplotype result
gff

gff
Chr

the chromosome name. If missing, the first element in the hapSummary will be used
startPOS

If missing, will use the min position in hapSummary
endPOS

If missing, will use the max position in hapSummary
type

character. Could be "circle", "pie", "pin", "pie.stack" or "flag"
cex

a numeric control the size of circle
CDS_h, fiveUTR_h, threeUTR_h

The height of CDS 5'UTR and 3'UTR in gene model
geneElement

ploted elements, eg.: c("CDS","five_prime_UTR")

Value

No return value

Examples

data("geneHapR_test")
hapSummary <- hap_summary(hapResult)
displayVarOnGeneModel(hapSummary, gff,
                      startPOS = 4100,
                      endPOS = 8210,
                      cex = 0.75)

Filter hap

Description

filter hapResult or hapSummary by remove positions or accessions or haplotypes

Usage

filter_hap(hap,
           rm.mode = c("position", "accession", "haplotype", "freq"),
           position.rm = position.rm,
           accession.rm = accession.rm,
           haplotype.rm = haplotype.rm,
           freq.min = 5)

Arguments

hap

object of hapSummary or hapResult class
rm.mode

filter mode, one of "position", "accession", "haplotype"
position.rm

numeric vector contains positions need to be removed
accession.rm

character vector contains accessions need to be removed, only hapResult can be filtered by accessions
haplotype.rm

character vector contains haplotypes need to be removed
freq.min

numeric, hapltypes with accessions number less than freq.min will be removed

Value

hapSummary or hapResult depend input

Examples

data("geneHapR_test")
hap <- filter_hap(hapResult,
                  rm.mode = c("position", "accession", "haplotype", "freq"),
                  position.rm = c(4879, 4950),
                  accession.rm = c("C1", "C9"),
                  haplotype.rm = c("H009", "H008"),
                  freq.min = 5)

filter variants in hapmap format

Description

filter variants in hapmap format

Usage

filter_hmp(
  x,
  mode = c("POS", "type", "both"),
  Chr = Chr,
  start = start,
  end = end,
  gff = gff,
  type = type,
  cusTyp = cusTyp,
  geneID = geneID
)

Arguments

x

genotype dataset in hapmap format, object of data.frame class
mode

filter mode, one of "POS", "type", "both"
Chr

chromosome name, needed if mode set to "POS" or "both"
start

start position, needed if mode set to "POS" or "both"
end

end position, needed if mode set to "POS" or "both"
gff

object of GRanges class, genome annotations imported by import_gff()
type

filter type, needed if mode set to "type" or "both", one of "CDS", "exon", "gene", "genome", "custom", if type was set to "custom", then custom_type is needed.
cusTyp

character vector, custom filter type, needed if type set to "custom"
geneID

gene ID

Examples

# create a dataset of genotype in hapmap format
hmp <- hap2hmp(hapResult);

# example
hmp <- filter_hmp(hmp, mode = "POS",
                      Chr = "scaffold_1", start = 4100, end = 5000)

filter_plink.pedmap

Description

used for filtration of p.link

Usage

filter_plink.pedmap(x,
                    mode = c("POS", "type", "both"),
                    Chr = Chr, start = start, end = end,
                    gff = gff, type = type, cusTyp = cusTyp,
                    geneID = geneID)

Arguments

x

a list stored the p.link information
mode

filtration mode, one of c("POS", "type", "both")
Chr

the chromosome name, need if mode set as POS or both
start, end

numeric, the range of filtration, and the start should smaller than end
gff

the imported gff object
type

should be in unique(gff$type), usually as "CDS", "genome".
cusTyp

if type set as custom, then cusTyp is needed
geneID

geneID

Value

list, similar with x, but filtered

Examples

pedfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.ped",
                          package = "geneHapR")
   mapfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.map",
                          package = "geneHapR")
   p.link <- import_plink.pedmap(pedfile = pedfile, mapfile = mapfile,
                                 sep_map = "\t", sep_ped = "\t")
   p.link <- filter_plink.pedmap(p.link, mode = "POS",
                                 Chr = "chr08", start = 25948004,
                                 end = 25949944)
   hapResult <- plink.pedmap2hap(p.link, hapPrefix = "H",
                                 hetero_remove = TRUE,
                                 na_drop = TRUE)

filter variants stored in table

Description

filter variants stored in table

Usage

filter_table(
  x,
  mode = c("POS", "type", "both"),
  Chr = Chr,
  start = start,
  end = end,
  gff = gff,
  type = type,
  cusTyp = cusTyp,
  geneID = geneID
)

Arguments

x

genotype dataset in hapmap format, object of data.frame class
mode

filter mode, one of "POS", "type", "both"
Chr

chromosome name, needed if mode set to "POS" or "both"
start

start position, needed if mode set to "POS" or "both"
end

end position, needed if mode set to "POS" or "both"
gff

object of GRanges class, genome annotations imported by import_gff()
type

filter type, needed if mode set to "type" or "both", one of "CDS", "exon", "gene", "genome", "custom", if type was set to "custom", then custom_type is needed.
cusTyp

character vector, custom filter type, needed if type set to "custom"
geneID

gene ID

Examples

# example
data("geneHapR_test")
table <- filter_table(gt.geno, mode = "POS",
                      Chr = "scaffold_1", start = 4100, end = 5000)

Filter VCF

Description

filter VCF by GFF annotation or by position or both

Usage

filter_vcf(vcf, gff = gff,
           mode = c("POS", "type", "both"),
           Chr = Chr, start = start, end = end,
           type = c("CDS", "exon", "gene", "genome", "custom"),
           cusTyp = c("CDS", "five_prime_UTR", "three_prime_UTR"),
           geneID = geneID)

Arguments

vcf

object of vcfR class, VCF file imported by import_vcf()
gff

object of GRanges class, genome annotations imported by import_gff()
mode

filter mode, one of "POS", "type", "both"
Chr

chromosome name, needed if mode set to "POS" or "both"
start

start position, needed if mode set to "POS" or "both"
end

end position, needed if mode set to "POS" or "both"
type

filter type, needed if mode set to "type" or "both", one of "CDS", "exon", "gene", "genome", "custom", if type was set to "custom", then custom_type is needed.
cusTyp

character vector, custom filter type, needed if type set to "custom"
geneID

gene ID

Value

vcfR

Examples

# filtet vcf
data("geneHapR_test")
vcf_f1 <- filter_vcf(vcf, mode = "POS",
                    Chr = "scaffold_1",
                    start = 4300, end = 5890)

vcf_f2 <- filter_vcf(vcf, mode = "type",
                    gff = gff,
                    type = "CDS")
vcf_f3 <- filter_vcf(vcf, mode = "both",
                    Chr = "scaffold_1",
                    start = 4300, end = 5890,
                    gff = gff,
                    type = "CDS")

Pre-process of Large VCF File(s)

Description

Filter/extract one or multiple gene(s)/range(s) from a large p.link file.

Usage

filterLargeP.link(
  root,
  rootOut = rootOut,
  Chr = Chr,
  POS = NULL,
  start = start,
  end = end,
  override = TRUE,
  sep = "\t"
)

Arguments

root

The file name without suffix. This function only support p.link file format stored in "map" and "ped" format, the file names after removed suffix should be same with each other.
rootOut

Path(s) of output p.link file stored in "ped&map" format.
Chr

a single CHROM name or CHROM names vector.
POS, start, end

provide the chromosome name should be extract from orignal p.link dataset. POS: a vector consist with start and end position, eg.: c(1,200) indicates 3 ranges (1~200, 300~500 and 300~400). if POS is NULL, start and end are needed.
override

whether override existed file or not, default as TRUE.
sep

a character indicate the separation of map and ped file, default is ⁠\t⁠.

Details

This package import P.link files. However, import a large P.link file is time and memory consuming. It's suggested that extract variants in target range with filterLargeP.link() before identification of haplotype.

When filter/extract multi genes/ranges, the parameter of Chr and POS must have equal length. Results will save to a single file if the user provide a single file path or save to multiple P.link file(s) when a equal length vector consist with file paths is provided.

Value

No return value

Examples

# The filteration of P.link of regular size should be done with `filter_plink.pedmap()`.
 # however, here, we use a mini vcf instead just for example and test

 pedfile <- system.file("extdata",
                        "snp3kvars-CHR8-25947258-25951166-plink.ped",
                        package = "geneHapR")
 mapfile <- system.file("extdata",
                        "snp3kvars-CHR8-25947258-25951166-plink.map",
                        package = "geneHapR")
 oldDir <- getwd()
 temp_dir <- tempdir()
 if(! dir.exists(temp_dir))
   dir.create(temp_dir)
 setwd(temp_dir)
 file.copy(pedfile, "test.ped")
 file.copy(mapfile, "test.map")

 # extract a single gene/range from large vcf
 filterLargeP.link(root = "test",
                   rootOut = "filtered_test",
                   Chr = "scaffold_1", POS = c(4300,5000), override = TRUE)

setwd(oldDir)

# delete temp_dir
unlink(temp_dir, recursive = TRUE)

Pre-process of Large VCF File(s)

Description

Filter/extract one or multiple gene(s)/range(s) from a large ⁠*.vcf/*.vcf.gz⁠ file.

Usage

filterLargeVCF(VCFin = VCFin, VCFout = VCFout,
                Chr = Chr,
                POS = NULL,
                start = start,
                end = end,
                override = TRUE)

Arguments

VCFin

Path of input ⁠*.vcf/*.vcf.gz⁠ file.
VCFout

Path(s) of output ⁠*.vcf/*.vcf.gz⁠ file.
Chr

a single CHROM name or CHROM names vector.
POS, start, end

provide the range should be extract from orignal vcf. POS: a vector consist with start and end position or a list with length equal to Chr, eg.: list(c(1,200), c(300,500), c(300,400)) indicates 3 ranges (1~200, 300~500 and 300~400). if POS is NULL, start and end are needed, eg.: start = c(1, 30) and end = c(200, 150) indicates 2 ranges (1~200 and 30~150)
override

whether override existed file or not, default as TRUE.

Details

This package import VCF files with 'vcfR' which is more efficient to import/manipulate VCF files in 'R'. However, import a large VCF file is time and memory consuming. It's suggested that filter/extract variants in target range with filterLargeVCF().

When filter/extract multi genes/ranges, the parameter of Chr and POS must have equal length. Results will save to a single file if the user provide a single file path or save to multiple VCF file(s) when a equal length vector consist with file paths is provided.

However, if you have hundreds gene/ranges need to extract from very large VCF file(s), it's prefer to process with other linux tools in a script on server, such as: 'vcftools' and 'bcftools'.

Value

No return value

Examples

# The filteration of small vcf should be done with `filter_vcf()`.
 # however, here, we use a mini vcf instead just for example and test.

 vcfPath <- system.file("extdata", "var.vcf.gz", package = "geneHapR")

 oldDir <- getwd()
 temp_dir <- tempdir()
 if(! dir.exists(temp_dir))
   dir.create(temp_dir)
 setwd(temp_dir)
 # extract a single gene/range from large vcf
 filterLargeVCF(VCFin = vcfPath, VCFout = "filtered.vcf.gz",
                Chr = "scaffold_1", POS = c(4300,5000), override = TRUE)

 # extract multi genes/ranges from large vcf
 filterLargeVCF(VCFin = vcfPath,
                VCFout = c("filtered1.vcf.gz",
                           "filtered2.vcf.gz",
                           "filtered3.vcf.gz"),
                Chr = rep("scaffold_1", 3),
                POS = list(c(4300, 5000),
                           c(5000, 6000),
                           c(5000, 7000)),
                override = TRUE)

setwd(oldDir)

Get Gene Position

Description

Get Gene Position

Usage

getGenePOS(gff= gff,
           geneID = geneID,
           type = type,
           gffTermContaingeneID = "Parent")

Arguments

gff

imported gff
geneID

target geneID
type

vector consist with one or more types in gff
gffTermContaingeneID

which term contains the geneID in your gff, defalt is Parent

Value

named vectors contains start, end and strand

Examples

data("geneHapR_test")
genePOS <- getGenePOS(gff = gff,
           geneID = "test1G0387",
           type = "CDS",
           gffTermContaingeneID = "Parent")

Get Gene Ranges

Description

Get Gene Ranges

Usage

getGeneRanges(gff= gff,
             geneID = geneID,
             type = type,
             gffTermContaingeneID = "Parent")

Arguments

gff

imported gff
geneID

target geneID
type

vector consist with one or more types in gff
gffTermContaingeneID

which term contains the geneID in your gff, defalt is Parent

Value

GRanges

Examples

data("geneHapR_test")
geneRanges <- getGeneRanges(gff = gff,
                            geneID = "test1G0387",
                            type = "CDS",
                            gffTermContaingeneID = "Parent")

Summary Hap Results

Description

A function used for summarize hapResult to visualization and calculation.

Usage

hap_summary(hap,
            hapPrefix = "H",
            file = file)

Arguments

hap

object of hapResult class, generated by vcf2hap() or seqs2hap or import_hap()
hapPrefix

prefix of hap names, default as "H"
file

file path where to save the hap summary result. If missing, nothing will be saved to disk.

Details

It is suggested to use the result of vcf2hap() or seqs2hap() as input directly. However the user can import previously hap result from local file with import_hap()

Value

hapSummary, first four rows are fixed to meta information: CHR, POS, INFO, ALLELE Hap names were placed in first column, Accessions and freqs were placed at the last two columns.

Note

If the user have changed the default hapPrefix in vcf2hap() or seqs2hap(), then the parameter hapPrefix is needed. Furthermore, a multi-letter prefix of hap names is possible.

Examples

data("geneHapR_test")
hapSummary <- hap_summary(hapResult, hapPrefix = "H")

Convert hapResult object to hapmap (hmp) format, for interact with other packages

Description

Convert hapResult object to hapmap (hmp) format, for interact with other packages

Usage

hap2hmp(hap)

hmp2hap(hmp, hapPrefix = "H", hetero_remove = TRUE, na_drop = TRUE, ...)

Arguments

hap

object of "hapResult" class
hmp

object of "data.frame" class in hapmap format
hapPrefix

prefix of haplotype names
hetero_remove

whether remove accessions contains hyb-sites, Character not A T C G
na_drop

whether drop accessions contains missing data ("N", "NA", ".")
...

Arguments passed on to table2hap

x

a data.frame contains variants information. The first file column are fix as Chrome name, position, reference nuclieotide, alter nuclieotide and INFO. Accession genotype should be in followed columns. "-" will be treated as Indel. "." and "N" will be treated as missing data. Heterozygotes should be "A/T", "AAA/A"

pad

The number length in haplotype names should be extend to.

Value

a data.frame in hapmap format.

Examples

data("geneHapR_test")
hmp <- hap2hmp(hapResult)
hap <- hmp2hap(hmp)

Display of Geography Distribution

Description

show distribution of intereted haplotypes on maps

Usage

hapDistribution(
  hap,
  AccINFO,
  LON.col,
  LAT.col,
  hapNames,
  database = "world",
  regions = ".",
  hap.color = hap.color,
  zColours = zColours,
  legend = TRUE,
  symbolSize = 1,
  symbol.lim = c(1, 10),
  ratio = 1,
  cex.legend = 0.8,
  lwd.pie = 1,
  borderCol.pie = NA,
  lty.pie = 1,
  showlabel = TRUE,
  label.col = "black",
  label.cex = 0.8,
  label.font = 1,
  label.adj = c(0.5, 0.5),
  map.fill.color = 1,
  ...
)

Arguments

hap

an object of hapResult class
AccINFO

a data.frame contains accession information
LON.col, LAT.col

column names of longitude(LON.col) and latitude(LAT.col)
hapNames

haplotype names used for display
database

character string naming a geographical database, a list of x, y, and names obtained from a previous call to map or a spatial object of class SpatialPolygons or SpatialLines. The string choices include a world map, three USA databases (usa, state, county), and more (type help(package='maps') to see the package index). If the requied database is in a different package that has not been attached, the string may be started with "packagename::". The location of the map databases may be overridden by setting the R_MAP_DATA_DIR environment variable.

regions

character vector that names the polygons to draw. Each database is composed of a collection of polygons, and each polygon has a unique name. When a region is composed of more than one polygon, the individual polygons have the name of the region, followed by a colon and a qualifier, as in michigan:north and michigan:south. Each element of regions is matched against the polygon names in the database and, according to exact, a subset is selected for drawing. The regions may also be defined using (perl) regular expressions. This makes it possible to use 'negative' expressions like "Norway(?!:Svalbard)", which means Norway and all islands except Svalbard. All entries are case insensitive. The default selects all polygons in the database.

hap.color, zColours

colors to apply to the pie section for each attribute column, "zColours" will be detached in future.
legend

a keyword specified the position of legend, one of "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center"; or a numeric vector of length two contains x,y coordinate of the legend
symbolSize

a numeric specified the symbol size. It will be detached in future. Please use "symbol.lim" instead.
symbol.lim

a numeric vector give the maximum and minimum size of each symbol
ratio

the ratio of Y to N in the output map, set to 1 as default
cex.legend

character expansion factor for legend relative to current par("cex")
lwd.pie

line width of the pies
borderCol.pie

The color of pie's border, default is NA, which means no border will be plotted
lty.pie

the line type of pie border
showlabel

a bool vector indicates whether show the labels which represens number of individuals. Default as TRUE.
label.col

color of the labels, default as "black"
label.cex

a number indicates the text size in label, default as 0.8
label.font

Font of label, 1 for normal, 2 for bold, 3 for italica, 4 for bold-italica
label.adj

the position of label, default as c(0.5, 0.5)
map.fill.color

vector of colors. If fill is FALSE, the first color is used for plotting all lines, and any other colors are ignored. Otherwise, the colors are matched one-one with the polygons that get selected by the region argument (and are reused cyclically, if necessary). If fill = TRUE, the default boundary line colour is given by par("fg"). To change this, you can use the border argument (see '...'). A color of NA causes the corresponding region to be deleted from the list of polygons to be drawn. Polygon colors are assigned after polygons are deleted due to values of the xlim and ylim arguments
...

Extra arguments passed to polygon or lines. Of particular interest may be the options border andlty that control the color and line type of the polygon borders when fill = TRUE.

Value

No return value

Examples

data("geneHapR_test")
data(china)
hapDistribution(hapResult,
                AccINFO = AccINFO,
                LON.col = "longitude",
                LAT.col = "latitude",
                hapNames = c("H001", "H002", "H003"))

hapVsPheno

Description

hapVsPheno

Usage

hapVsPheno(
  hap,
  pheno,
  phenoName,
  hapPrefix = "H",
  title = "",
  comparisons = comparisons,
  method = "t.test",
  method.args = list(),
  symnum.args = list(),
  mergeFigs = FALSE,
  angle = angle,
  hjust = hjust,
  vjust = vjust,
  minAcc = minAcc,
  freq.min = freq.min,
  outlier.rm = TRUE,
  ...
)

Arguments

hap

object of hapResult class, generate withvcf2hap() or seqs2hap()
pheno

object of data.frame class, imported by import_pheno()
phenoName

pheno name for plot, should be one column name of pheno
hapPrefix

prefix of hapotypes, default as "H"
title

a charater which will used for figure title
comparisons

a list contains comparison pairs eg. list(c("H001", "H002"), c("H001", "H004")), or a character vector contains haplotype names for comparison, or "none" indicates do not add comparisons.
method

a character string indicating which method to be used for comparing means.
method.args

a list of additional arguments used for the test method. For example one might use method.args = list(alternative = "greater") for wilcoxon test.
symnum.args

a list of arguments to pass to the function symnum for symbolic number coding of p-values. For example, symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns")).

In other words, we use the following convention for symbols indicating statistical significance:

  • ns: p > 0.05

  • *: p <= 0.05

  • **: p <= 0.01

  • ***: p <= 0.001

  • ****: p <= 0.0001

mergeFigs

bool type, indicate whether merge the heat map and box plot or not. Default as FALSE
angle

the angle of x labels
hjust, vjust

hjust and vjust of x labels
minAcc, freq.min

If observations number of a Hap less than this number will not be compared with others or be ploted. Should not less than 3 due to the t-test will meaninglessly. Default as 5
outlier.rm

whether remove ouliers, default as TRUE
...

Arguments passed on to ggpubr::ggviolin

data

a data frame

x

character string containing the name of x variable.

y

character vector containing one or more variables to plot

combine

logical value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, create a multi-panel plot by combining the plot of y variables.

merge

logical or character value. Default is FALSE. Used only when y is a vector containing multiple variables to plot. If TRUE, merge multiple y variables in the same plotting area. Allowed values include also "asis" (TRUE) and "flip". If merge = "flip", then y variables are used as x tick labels and the x variable is used as grouping variable.

color

outline color.

fill

fill color.

palette

the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".

alpha

color transparency. Values should be between 0 and 1.

xlab

character vector specifying x axis labels. Use xlab = FALSE to hide xlab.

ylab

character vector specifying y axis labels. Use ylab = FALSE to hide ylab.

facet.by

character vector, of length 1 or 2, specifying grouping variables for faceting the plot into multiple panels. Should be in the data.

panel.labs

a list of one or two character vectors to modify facet panel labels. For example, panel.labs = list(sex = c("Male", "Female")) specifies the labels for the "sex" variable. For two grouping variables, you can use for example panel.labs = list(sex = c("Male", "Female"), rx = c("Obs", "Lev", "Lev2") ).

short.panel.labs

logical value. Default is TRUE. If TRUE, create short labels for panels by omitting variable names; in other words panels will be labelled only by variable grouping levels.

linetype

line types.

trim

If TRUE (default), trim the tails of the violins to the range of the data. If FALSE, don't trim the tails.

size

Numeric value (e.g.: size = 1). change the size of points and outlines.

width

violin width.

draw_quantiles

If not(NULL) (default), draw horizontal lines at the given quantiles of the density estimate.

select

character vector specifying which items to display.

remove

character vector specifying which items to remove from the plot.

order

character vector specifying the order of items.

add

character vector for adding another plot element (e.g.: dot plot or error bars). Allowed values are one or the combination of: "none", "dotplot", "jitter", "boxplot", "point", "mean", "mean_se", "mean_sd", "mean_ci", "mean_range", "median", "median_iqr", "median_hilow", "median_q1q3", "median_mad", "median_range"; see ?desc_statby for more details.

add.params

parameters (color, shape, size, fill, linetype) for the argument 'add'; e.g.: add.params = list(color = "red").

error.plot

plot type used to visualize error. Allowed values are one of c("pointrange", "linerange", "crossbar", "errorbar", "upper_errorbar", "lower_errorbar", "upper_pointrange", "lower_pointrange", "upper_linerange", "lower_linerange"). Default value is "pointrange" or "errorbar". Used only when add != "none" and add contains one "mean_*" or "med_*" where "*" = sd, se, ....

label

the name of the column containing point labels. Can be also a character vector with length = nrow(data).

font.label

a list which can contain the combination of the following elements: the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of labels. For example font.label = list(size = 14, face = "bold", color ="red"). To specify only the size and the style, use font.label = list(size = 14, face = "plain").

label.select

can be of two formats:

  • a character vector specifying some labels to show.

  • a list containing one or the combination of the following components:

    • top.up and top.down: to display the labels of the top up/down points. For example, label.select = list(top.up = 10, top.down = 4).

    • criteria: to filter, for example, by x and y variabes values, use this: label.select = list(criteria = "`y` > 2 & `y` < 5 & `x` %in% c('A', 'B')").

repel

a logical value, whether to use ggrepel to avoid overplotting text labels or not.

label.rectangle

logical value. If TRUE, add rectangle underneath the text, making it easier to read.

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

ggtheme

function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void(), ....

Value

list. A list contains a character vector with Haps were applied student test, a mattrix contains p-value of each compare of Haps and a ggplot2 object named as figs if mergeFigs set as TRUE, or two ggplot2 objects names as fig_pvalue and fig_Violin

Examples

data("geneHapR_test")
# plot the figs directly
hapVsPheno(hap = hapResult,
           pheno = pheno,
           phenoName = "GrainWeight.2021",
           minAcc = 3)

# do not merge the files
results <- hapVsPheno(hap = hapResult,
                      pheno = pheno,
                      phenoName = "GrainWeight.2021",
                      minAcc = 3,
                      mergeFigs = FALSE)
plot(results$fig_pvalue)
plot(results$fig_Violin)

hapVsPhenoPerSite

Description

Comparie phenotype site by site.

Usage

hapVsPhenoPerSite(
  hap,
  pheno,
  phenoName,
  sitePOS,
  fileName,
  fileType = NULL,
  freq.min = 5,
  ...
)

Arguments

hap

an R object of hapresult class
pheno, phenoName

pheno, a data.frame contains the phenotypes; Only one phenotype name is required.
sitePOS

the coordinate of site
fileName, fileType

file name and file type will be needed for saving result, file type could be one of "png, tiff, jpg"
freq.min

miner allies frequency less than freq.min will not be skipped
...

addtional params will be passed to plot saving function like tiff(), png(), pdf()

Examples

data("geneHapR_test")
hapVsPhenoPerSite(hapResult, pheno, sitePOS = "4300")

hapVsPhenos

Description

hapVsPhenos

Usage

hapVsPhenos(
  hap,
  pheno,
  outPutSingleFile = TRUE,
  hapPrefix = "H",
  title = "Seita.0G000000",
  width = 12,
  height = 8,
  res = 300,
  compression = "lzw",
  filename.prefix = filename.prefix,
  filename.surfix = "pdf",
  filename.sep = "_",
  outlier.rm = TRUE,
  mergeFigs = TRUE,
  ...
)

Arguments

hap

object of hapResult class, generate withvcf2hap() or seqs2hap()
pheno

object of data.frame class, imported by import_pheno()
outPutSingleFile

TRUE or FALSE indicate whether put all figs into to each pages of single file or generate multi-files. Only worked while file type is pdf
hapPrefix

prefix of hapotypes, default as "H"
title

a charater which will used for figure title
width

manual option for determining the output file width in inches. (default: 12)
height

manual option for determining the output file height in inches. (default: 8)
res

The nominal resolution in ppi which will be recorded in the bitmap file, if a positive integer. Also used for units other than the default, and to convert points to pixels
compression

the type of compression to be used.
filename.prefix, filename.surfix, filename.sep

if multi files generated, file names will be formed by prefix filename.prefix, a seperate charcter filename.sep, pheno name, a dot and surfix filename.surfix, and file type was decide by filename.surfix; if single file was generated, file name will be formed by prefix filename.prefix, a dot and surfix filename.surfix
outlier.rm

whether remove ouliers, default as TRUE
mergeFigs

bool type, indicate whether merge the heat map and box plot or not. Default as FALSE
...

Arguments passed on to hapVsPheno

phenoName

pheno name for plot, should be one column name of pheno

minAcc,freq.min

If observations number of a Hap less than this number will not be compared with others or be ploted. Should not less than 3 due to the t-test will meaninglessly. Default as 5

angle

the angle of x labels

hjust,vjust

hjust and vjust of x labels

comparisons

a list contains comparison pairs eg. list(c("H001", "H002"), c("H001", "H004")), or a character vector contains haplotype names for comparison, or "none" indicates do not add comparisons.

method

a character string indicating which method to be used for comparing means.

method.args

a list of additional arguments used for the test method. For example one might use method.args = list(alternative = "greater") for wilcoxon test.

symnum.args

a list of arguments to pass to the function symnum for symbolic number coding of p-values. For example, symnum.args <- list(cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, Inf), symbols = c("****", "***", "**", "*", "ns")).

In other words, we use the following convention for symbols indicating statistical significance:

  • ns: p > 0.05

  • *: p <= 0.05

  • **: p <= 0.01

  • ***: p <= 0.001

  • ****: p <= 0.0001

Value

No return value

Examples

data("geneHapR_test")

oriDir <- getwd()
temp_dir <- tempdir()
if(! dir.exists(temp_dir))
  dir.create(temp_dir)
setwd(temp_dir)
# analysis all pheno in the data.frame of pheno
hapVsPhenos(hapResult,
            pheno,
            outPutSingleFile = TRUE,
            hapPrefix = "H",
            title = "Seita.0G000000",
            filename.prefix = "test",
            width = 12,
            height = 8,
            res = 300)
setwd(oriDir)

Import Accession Information from File

Description

import accession information including phenotype data, accession group, location from a tab delimed table file

Usage

import_AccINFO(file, comment.char = "#",
               check.names = FALSE, row.names = 1, ...)

Arguments

file

file path, this file should be a tab delimed table
comment.char

character: a character vector of length one containing a single character or an empty string. Use "" to turn off the interpretation of comments altogether.
check.names

logical. If TRUE then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names. If necessary they are adjusted (by make.names) so that they are, and also to ensure that there are no duplicates.
row.names

a vector of row names. This can be a vector giving the actual row names, or a single number giving the column of the table which contains the row names, or character string giving the name of the table column containing the row names.

If there is a header and the first row contains one fewer field than the number of columns, the first column in the input is used for the row names. Otherwise if row.names is missing, the rows are numbered.

Using row.names = NULL forces row numbering. Missing or NULL row.names generate row names that are considered to be ‘automatic’ (and not preserved by as.matrix).

...

Further arguments to be passed to read.table.

Details

First column should be Accessions; phenos/accession information should begin from second column, phenoName/group/locations should located at the first row, If a dot '.' is located in pheno name, then the part before the dot will be set as y axis name and the latter will be set as foot when plot figures.

Value

data.frame, Accession names were set as rownames and columns were named by pheno/info names

Examples

oldDir <- getwd()
temp_dir <- tempdir()
if(! dir.exists(temp_dir))
  dir.create(temp_dir)
setwd(temp_dir)
data("geneHapR_test")
write.table(pheno, file = "test.pheno.txt", sep = "\t")
pheno <- import_AccINFO("test.pheno.txt")
pheno
setwd(oldDir)

import annotation files in BED format

Description

import bed files contains annotations into R as GRanges object

Usage

import_bed(con, quite = FALSE)

Arguments

con

A path, URL, connection or BEDFile object. For the functions ending in .bed, .bedGraph and .bed15, the file format is indicated by the function name. For the base export and import functions, the format must be indicated another way. If con is a path, URL or connection, either the file extension or the format argument needs to be one of “bed”, “bed15”, “bedGraph”, “bedpe”, “narrowPeak”, or “broadPeak”. Compressed files (“gz”, “bz2” and “xz”) are handled transparently.

quite

whether show message

Details

If there is no genome annotation file in GFF format for your interest species, a BED file is convenient to custom a simple annotation file for a gene. Here we suggest two type of BED format: BED6 and BED4.

As the definition of UCSC. The BED6 contains 6 columns, which are 1) chrom, 2) chromStart, 3) chromEnd, 4) name, 5) score and 6) strand. The BED4 format contains the first 4 column of BED6 format.

However, in gene haplotype statistics, we only care about the type of each site. Thus we use the fourth column to definition the transcripts name and "CDS" or "URTs", separated by a space, eg.:

Chr8 678 890 HD1.1 CDS . -

Chr8 891 989 HD1.1 five_prime_UTR . -

Chr8 668 759 HD1.2 CDS . -

Chr8 908 989 HD1.2 CDS . -

This example indicate a small gene named as HD1 have two transcripts, named as HD1.1 and HD1.2, separately. HD1 has a CDS and a UTR region; while HD1.2 has two CDS region.

Value

GRange object

Examples

bed.Path <- system.file("extdata", "annotation.bed6", package = "geneHapR")
bed <- import_bed(bed.Path)
bed

Import Annotations from GFF Format File

Description

import genome annotations in GFF/GFF3 format

Usage

import_gff(gffFile, format = "gff")

Arguments

gffFile

the gff file path
format

should be one of "gff", "gff1", "gff2", "gff3", "gvf", or "gtf". Default as gff

Value

GRange object

Examples

gff.Path <- system.file("extdata", "annotation.gff", package = "geneHapR")
gff <- import_gff(gff.Path, format = "gff")
gff

Import hapResult/hapSummary

Description

This function could be used for import hap result or hap summary result. The type of returned object is decided by input file, see details.

Usage

import_hap(file, type = "auto", ...)

Arguments

file

hapSummary or hapResult file path.
type

the content type of imported file, should be one of c("hapResult", "hapSummary")
...

extras will pass to read.delim()

Details

The hap result and hap summary result have common features. The common features of these two types are: First four rows contains extra information: CHR, POS, INFO and ALLELE Hap names were in the first column. The differences are: Hap summary result have a freq column while hap result not. Rows represent haplotypes in hap summary result, while rows represent accessions in hap result. In addtion, the accessions of each haplotype in hap summary result were separated by ";".

Value

hapSummary or hapResult

Examples

oldDir <- getwd()
temp_dir <- tempdir()
if(! dir.exists(temp_dir))
  dir.create(temp_dir)
setwd(temp_dir)
data("geneHapR_test")
write.hap(hapResult, file = "test.pheno.txt", sep = "\t")
hap <- import_hap("test.pheno.txt")
hap
setwd(oldDir)

Import MultipleAlignment Result

Description

import sequences algned results

Usage

import_MultipleAlignment(filepath, format = "fasta", type = "DNA")

Arguments

filepath

A character vector (of arbitrary length when reading, of length 1 when writing) containing the paths to the files to read or write. Note that special values like "" or "|cmd" (typically supported by other I/O functions in R) are not supported here. Also filepath cannot be a connection.

format

Either "fasta" (the default), stockholm, or "clustal".

type

one of "DNA" and "Protein"

Value

object of DNAMultipleAlignment

Examples

aliSeqPath <- system.file("extdata", "seqs.fa", package = "geneHapR")

geneSeqs <- import_MultipleAlignment(filepath = aliSeqPath,
                                     format = "fasta",
                                     type = "DNA")
geneSeqs <- import_MultipleAlignment(filepath = aliSeqPath,
                                     format = "fasta",
                                     type = "Protein")

import_plink.pedmap

Description

used for import regular p.link file stored in map and ped format

Usage

import_plink.pedmap(root = root,
                      sep_ped = "\t", sep_map = "\t",
                      pedfile = pedfile, mapfile = mapfile)

Arguments

root

The file name without suffix. This function only support p.link file format stored in "map" and "ped" format, the file names after removed suffix should be same with each other.
sep_ped

a character indicate the separation of ped file
sep_map

a character indicate the separation of map file
pedfile, mapfile

if root is missing then pedfile and mapfile are needed

Value

list, contains map information stored in data.frame and ped information stored in data.frame

Examples

pedfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.ped",
                          package = "geneHapR")
   mapfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.map",
                          package = "geneHapR")
   p.link <- import_plink.pedmap(pedfile = pedfile, mapfile = mapfile,
                                 sep_map = "\t", sep_ped = "\t")
   p.link <- filter_plink.pedmap(p.link, mode = "POS",
                                 Chr = "chr08", start = 25948004,
                                 end = 25949944)
   hapResult <- plink.pedmap2hap(p.link, hapPrefix = "H",
                                 hetero_remove = TRUE,
                                 na_drop = TRUE)

Import Sequences

Description

import DNA sequences in FASTA format

Usage

import_seqs(filepath, format = "fasta")

Arguments

filepath

A character vector containing the path to the DNA sequences file. Reading files in gzip format (which usually have the '.gz' extension) is supported. Note that only DNA supported here.
format

Either "fasta" (the default) or "fastq"

Value

object of DNAStringSet class

Examples

seqPath <- system.file("extdata", "seqs.fa", package = "geneHapR")
geneSeqs <- import_seqs(filepath = seqPath, format = "fasta")

Import VCF from File

Description

import *.vcf structured text format, as well as the compressed ⁠*.vcf.gz⁠ format.

Usage

import_vcf(file = file, ...)

import_vcf(file = file, ...)

Arguments

file

file path of VCF file
...

pass to vcfR::read.vcfR()

Value

vcfR object

Author(s)

Zhangrenl

See Also

vcfR::read.vcfR()

Examples

vcfPath <- system.file("extdata", "var.vcf.gz", package = "geneHapR")
vcf <- import_vcf(file = vcfPath)
vcf

This function produces a pairwise LD plot.

Description

LDheatmap() is used to produce a graphical display, as a heat map, of pairwise linkage disequilibrium (LD) measurements for SNPs. The heat map is a false color image in the upper-left diagonal of a square plot. Optionally, a line parallel to the diagonal of the image indicating the physical or genetic map positions of the SNPs may be added, along with text reporting the total length of the genomic region considered.

Usage

plot_LDheatmap(
  hap,
  gff,
  Chr,
  start,
  end,
  geneID = NULL,
  distances = "physical",
  LDmeasure = "r",
  title = "Pairwise LD",
  add.map = TRUE,
  map.height = 1,
  colorLegend = TRUE,
  geneMapLocation = 0.15,
  geneMapLabelX = NULL,
  geneMapLabelY = NULL,
  SNP.name = TRUE,
  color = NULL,
  color_gmodel = "grey",
  color_snp = "grey",
  color_snpname = "grey40",
  cex_snpname = 0.8,
  snpmarks_height = NULL,
  newpage = TRUE,
  name = "ldheatmap",
  vp.name = NULL,
  pop = FALSE,
  text = FALSE
)

Arguments

hap

R object of hapSummary or hapResult class.
gff

annotations
Chr, start, end, geneID

chromosome, start and end position, gene ID for extract annotation in target range.
distances

A character string to specify whether the provided map locations are in physical or genetic distances. If distances = "physical" (default), the text describing the total length of the region will be “Physical Length:XXkb” where XX is the length of the region in kilobases. If distances = "genetic", the text will be “Genetic Map Length:YYcM” where YY is the length of the region in centiMorgans. If gdat is an object of class LDheatmap, distances is taken from gdat.
LDmeasure

A character string specifying the measure of LD

  • either allelic correlation r2r^2 or Lewontin's |D′'|; default = "r" for r2r^2; type "D'" for |D′'|. This argument is ignored when the user has already supplied calculated LD measurements through gdat (i.e., when gdat is a matrix of pairwise LD measurements or an object of class "LDheatmap").

title

A character string for the main title of the plot. Default is “Pairwise LD”.
add.map

If TRUE (default) a diagonal line indicating the physical or genetic map positions of the SNPs will be added to the plot, along with text indicating the total length of the genetic region.
map.height

the height of gene map, default is 0.02
colorLegend

If TRUE (default) the color legend is drawn.
geneMapLocation

A numeric value specifying the position of the line parallel to the diagonal of the matrix; the larger the value, the farther it lies from the matrix diagonal. Ignored when add.map = FALSE.
geneMapLabelX

A numeric value specifying the x-coordinate of the text indicating the total length of the genomic region being considered. Ignored when add.map = FALSE.
geneMapLabelY

A numeric value specifying the y-coordinate of the text indicating the total length of the genomic region being considered. Ignored when add.map = FALSE.
SNP.name

a logical vector indicated wherther display SNP names using positions.
color

A range of colors to be used for drawing the heat map. Default is grDevices::colorRampPalette(c("red", "grey"))(30).
color_gmodel, color_snp, color_snpname

the color of gene model and snp and snp names respectively, default as grey80.
cex_snpname

the size of snp names/labels
snpmarks_height

the height of snp marks, if set as NULL, nothing will display on gene model where the heat map is going to be drawn.
newpage

If TRUE (default), the heat map will be drawn on a new page.
name

A character string specifying the name of the LDheatmap graphical object (grob) to be produced.
vp.name

A character string specifying the name of the viewport
pop

If TRUE, the viewport where the heat map is drawn is popped (i.e. removed) from the viewport tree after drawing. Default = FALSE.
text

If TRUE, the LD measurements are printed on each cell.

Details

The input object gdat can be a data frame of genotype objects (a data structure from the genetics package), a SnpMatrix object (a data structure from the snpStats package), or any square matrix with values between 0 and 1 inclusive. LD computation is much faster for SnpMatrix objects than for genotype objects. In the case of a matrix of LD values between 0 and 1, the values above the diagonal will be plotted. In the display of LD, SNPs appear in the order supplied by the user as the horizontal and vertical coordinates are increased and one moves along the off-diagonal line, from the bottom-left to the top-right corner. To achieve this, the conventions of the image() function have been adopted, in which horizontal coordinates correspond to the rows of the matrix and vertical coordinates correspond to columns, and vertical coordinates are indexed in increasing order from bottom to top. See the package vignette LDheatmap for more examples and details of the implementation. Examples of adding “tracks” of genomic annotation above a flipped heatmap are in the package vignette addTracks.

Value

An object of class "LDheatmap" which contains the following components:

LDmatrix

The matrix of pairwise LD measurements plotted in the heat map.

LDheatmapGrob

A grid graphical object (grob) representing the produced heat map.

heatmapVP

The viewport in which the heat map is drawn. See viewport.
genetic.distances

The vector of the supplied physical or genetic map locations, or the vector of equispaced marker distances when no distance vector is supplied.
distances

A character string specifying whether the provided map distances are physical or genetic.

color

The range of colors used for drawing the heat map.

The grob LDheatmapGrob has three grobs as its children (components). They are listed below along with their own children and respectively represent the color image with main title, genetic map and color key of the heat map: "heatMap" - "heatmap", "title"; "geneMap" - "diagonal", "segments", "title", "symbols", "SNPnames"; and "Key" - "colorKey", "title", "labels", "ticks", "box".

Note

The produced heat map can be modified in two ways. First, it is possible to edit interactively the grob components of the heat map, by using the function grid.edit; the function will not work if there is no open graphical device showing the heat map. Alternatively, the user can use the function editGrob and work with the grob LDheatmapGrob returned by LDheatmap. See Examples for usage. LDheatmap() uses Grid, which does not respond to par() settings. Hence modifying par() settings of mfrow and mfcol will not work with LDheatmap(). The Examples section shows how to display multiple heat maps on one plot without the use of par().

References

Shin J-H, Blay S, McNeney B and Graham J (2006). LDheatmap: An R Function for Graphical Display of Pairwise Linkage Disequilibria Between Single Nucleotide Polymorphisms. Journal of Statistical Software, 16 Code Snippet 3

Examples

# Pass LDheatmap a SnpMatrix object
data(geneHapR_test)
plot_LDheatmap(hap = hapResult,
               gff = gff,
               Chr = hapResult[1,2],
               start = 4000, end = 8200)

Generate Haplotype Net Relationshop with Haplotype Result

Description

computes a haplotype network with haplotype summary result

Usage

get_hapNet(hapSummary,
           AccINFO = AccINFO,
           groupName = groupName,
           na.label = "Unknown")

getHapGroup(
  hapSummary,
  AccINFO = AccINFO,
  groupName = groupName,
  na.label = na.label
)

Arguments

hapSummary

object of hapSummary class, generated by hap_summary()
AccINFO

data.frame, specified groups of each accession. Used for pie plot. If missing, pie will not draw in plotHapNet. Or you can supplied a hap_group mattrix with plot(hapNet, pie = hap_group).
groupName

the group name used for pie plot, should be in AccINFO column names, default as the first column name
na.label

the label of NAs

Value

hapNet class

References

Mark P.J. van der Loo (2014) doi:10.32614/RJ-2014-011;

E. Paradis (2010) doi:10.1093/bioinformatics/btp696

See Also

plotHapNet() and hap_summary().

Examples

data("geneHapR_test")
hapSummary <- hap_summary(hapResult)

# calculate haploNet
hapNet <- get_hapNet(hapSummary,
                     AccINFO = AccINFO, # accession types
                     groupName = colnames(AccINFO)[2])

# plot haploNet
plot(hapNet)

# plot haploNet
plotHapNet(hapNet,
           size = "freq",   # circle size
           scale = "log10", # scale circle with 'log10(size + 1)'
           cex = 1, # size of hap symbol
           col.link = 2, # link colors
           link.width = 2, # link widths
           show.mutation = 2, # mutation types one of c(0,1,2,3)
           legend = FALSE) # legend position

plink.pedmap2hap

Description

convert p.link format data into hapResult

Usage

plink.pedmap2hap(
  p.link,
  hapPrefix = "H",
  pad = 3,
  hetero_remove = TRUE,
  na_drop = TRUE
)

Arguments

p.link

list contains p.link information
hapPrefix

prefix of haplotype names
pad

The number length in haplotype names should be extend to.
hetero_remove

whether remove accessions contains hyb-sites
na_drop

whether drop accessions contains missing data ("N", NA)

Value

object of hapSummary class

Examples

pedfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.ped",
                          package = "geneHapR")
   mapfile <- system.file("extdata",
                          "snp3kvars-CHR8-25947258-25951166-plink.map",
                          package = "geneHapR")
   p.link <- import_plink.pedmap(pedfile = pedfile, mapfile = mapfile,
                                 sep_map = "\t", sep_ped = "\t")
   p.link <- filter_plink.pedmap(p.link, mode = "POS",
                                 Chr = "chr08", start = 25948004,
                                 end = 25949944)
   hapResult <- plink.pedmap2hap(p.link, hapPrefix = "H",
                                 hetero_remove = TRUE,
                                 na_drop = TRUE)

plotEFF

Description

plotEFF

Usage

plotEFF(
  siteEFF,
  gff = gff,
  Chr = Chr,
  start = start,
  end = end,
  showType = c("five_prime_UTR", "CDS", "three_prime_UTR"),
  CDS.height = CDS.height,
  cex = 0.1,
  col = c("red", "yellow"),
  pch = 20,
  main = main,
  legend.cex = 0.8,
  gene.legend = TRUE,
  markMutants = TRUE,
  mutants.col = 1,
  mutants.type = 1,
  y = c("pvalue", "effect"),
  ylab = ylab,
  legendtitle = legendtitle,
  par.restore = TRUE
)

Arguments

siteEFF

matrix, column name are pheno names and row name are site position
gff

gff annotation
Chr

the chromosome name
start

start position
end

end position
showType

character vector, eg.: "CDS", "five_prime_UTR", "three_prime_UTR"
CDS.height

numeric indicate the height of CDS in gene model, range: ⁠[0,1]⁠
cex

a numeric control the size of point
col

vector specified the color bar
pch

vector controls points type, see par()
main

main title
legend.cex

a numeric control the legend size
gene.legend

whether add legend for gene model
markMutants

whether mark mutants on gene model, default as TRUE
mutants.col

color of lines which mark mutants
mutants.type

a vector of line types
y, ylab, legendtitle

y: indicate either pvalue or effect should be used as y axix, ylab,legendtitle:,character, if missing, the value will be decide by y.
par.restore

default as TRUE, wether restore the origin par after ploted EFF.

Value

No return value, called for side effects

Examples

data("geneHapR_test")

# calculate site functional effect
# siteEFF <- siteEFF(hapResult, pheno, names(pheno))
# plotEFF(siteEFF, gff = gff, Chr = "scaffold_1")

plotHapNet

Description

plotHapNet

Usage

plotHapNet(
  hapNet,
  size = "freq",
  scale = 1,
  cex = 0.8,
  cex.legend = 0.6,
  col.link = 1,
  link.width = 1,
  show.mutation = 2,
  backGround = backGround,
  hapGroup = hapGroup,
  legend = FALSE,
  show_size_legend = TRUE,
  show_color_legend = TRUE,
  pie.lim = c(0.5, 2),
  main = main,
  labels = TRUE,
  legend_version = 0,
  labels.cex = 0.8,
  labels.col = "blue",
  labels.adj = NULL,
  labels.font = 2,
  ...
)

Arguments

hapNet

an object of class "haploNet"
size

a numeric vector giving the diameter of the circles representing the haplotypes: this is in the same unit than the links and eventually recycled.
scale

a numeric indicate the ratio of the scale of the links representing the number of steps on the scale of the circles representing the haplotypes or a character one of c('log10', 'log2') indicate the scale method by log10(size) or log2(size), respectively. Default as 1
cex

character expansion factor relative to current par("cex")
cex.legend

same as cex, but for text in legend
col.link

a character vector specifying the colours of the links; eventually recycled.
link.width

a numeric vector giving the width of the links; eventually recycled.
show.mutation

an integer value:

if 0, nothing is drawn on the links;

if 1, the mutations are shown with small segments on the links;

if 2, they are shown with small dots;

if 3, the number of mutations are printed on the links.
backGround

a color vector with length equal to number of Accession types
hapGroup

a matrix used to draw pie charts for each haplotype; its number of rows must be equal to the number of haplotypes
legend

a logical specifying whether to draw the legend, or a vector of length two giving the coordinates where to draw the legend; FALSE by default. If TRUE, the user is asked to click where to draw the legend.
show_size_legend, show_color_legend

wether show size or color legend
pie.lim

A numeric vector define the maximum and minmum pie size, which will be avoid the pie to samll or too large
main

The main title (on top) using font, size (character expansion) and color par(c("font.main", "cex.main", "col.main")).
labels

a logical specifying whether to identify the haplotypes with their labels (default as TRUE)
legend_version

the size legened style, default as 0
labels.cex

the size of labels
labels.col

the labels color
labels.adj

a named list contains two length vectors defining the adjustment of labels. The names should be exactly matched with the haplotype names. default as NULL.
labels.font

the font of labels, default as 2
...

other parameters will pass to plot function

Details

Additional parameters control the network features: labels.cex = 1, labels.font = 2, link.color = "black", link.type = 1, link.type.alt = 2, link.width = 1, link.width.alt = 1, altlinks = TRUE, threshold = c(1,2), haplotype.inner.color = "white", haplotype.outer.color = "black", mutations.cex = 1, mutations.font = 1, mutations.frame.background = "#0000FF4D", mutations.frame.border = "black", mutations.text.color = 1, mutations.arrow.color = "black", mutations.arrow.type = "triangle", mutations.sequence.color = "#BFBFBF4D", mutations.sequence.end = "round", mutations.sequence.length = 0.3, mutations.sequence.width = 5, pie.inner.segments.color = "black", pie.colors.function = rainbow, scale.ratio = 1, show.mutation = 2

The alter links could be eliminated by set the 'threshold' to 0 or set 'altlinks' as FALSE.

Value

No return value

See Also

hap_summary() and get_hapNet().

Examples

data("geneHapR_test")
hapSummary <- hap_summary(hapResult)

# calculate haploNet
hapNet <- get_hapNet(hapSummary,
                     AccINFO = AccINFO, # accession types
                     groupName = colnames(AccINFO)[2])

# plot haploNet
plot(hapNet)

# plot haploNet
plotHapNet(hapNet,
           size = "freq",   # circle size
           scale = "log10", # scale circle with 'log10(size + 1)'
           cex = 1, # size of hap symbol
           col.link = 2, # link colors
           link.width = 2, # link widths
           show.mutation = 2, # mutation types one of c(0,1,2,3)
           legend = FALSE) # legend position

plotHapTable

Description

display hap result as a table-like figure

Usage

plotHapTable(hapSummary,
             hapPrefix = "H",
             title = "",
             geneName = geneName,
             INFO_tag = NULL,
             tag_split = tag_split,
             tag_field = tag_field,
             tag_name = tag_name,
             displayIndelSize = 0, angle = c(0,45,90),
             replaceMultiAllele = TRUE,
             ALLELE.color = "grey90")

Arguments

hapSummary

object of hapSummary class
hapPrefix

prefix of haplotype names. Default as "H"
title

the main title of the final figure
geneName

character, will be used for filter INFO filed of ANN
INFO_tag

The annotations in the INFO field are represented as tag-value pairs, where the tag and value are separated by an equal sign, ie "=", and pairs are separated by colons, ie ";". For more information please see details.
tag_split

usually, the value of tag-value contains one information. However, if a tag contains more than one fields, eg "ANN", then tag_split is needed. When INFO_tag was set as "ANN" or "SNPEFF", tag_split will be set as "|" by default, see details.
tag_field

integer, if a tag-value contains more than one fields, user need to specified which field should be display. If tag_field set as 0, the whole contents will be displayed. Default as 0.
tag_name

tag name is displayed in Hap figure. If tag_name is missing, will take the value of INFO_tag.
displayIndelSize

display indels with max size of displayIndelSize, If set as 0, all indels will convert into "i*" of which "i" represents "indel".
angle

the angle of coordinates, should be one of 0, 45 and 90
replaceMultiAllele

whether to replace MultiAllele with "T*", default as TRUE.
ALLELE.color

the color of ALLELE row, default as "grey90"

Details

In VCF files, the INFO field are represented as tag-value pairs, where the tag and value are separated by an equal sign, ie "=", and pairs are separated by colons, ie ";".

If hapSummarys were generated from sequences, INFO row is null. If hapSummarys were generated from VCF, INFO was take from the INFO column in the source VCF file. Some tag-values may contains more than one value separated by "|", eg.: "ANN" or "snpEFF" added by 'snpeff' or other software. For those fields we need specified value of tag_field = "ANN" and tag_split = "[\|]", it's suggest specified the value of tag_name for display in figure.

'snpeff', a toolbox for genetic variant annotation and functional effect prediction, will add annotations to INFO filed in VCF file under a tag named as "ANN". The annotations contains several fields separated by "|". eg.:

  1. Allele

  2. Annotation

  3. Annotation_Impact

  4. Gene_Name

  5. Gene_ID

  6. Feature_Type

  7. Feature_ID

  8. Transcript_BioType

  9. Rank

  10. HGVS.c

  11. HGVS.p

  12. cDNA.pos/cDNA.length ... ...

However, the INFO in hapResults may missing annotations that we need. In this case, we can custom INFOs in hapSummarys with addINFO(). Once the needed annotations were included in hap results, we can display them with plotHapTable() by specify the value of INFO_tag.

Value

ggplot2 object

See Also

addINFO()

Examples

data("geneHapR_test")
plotHapTable(hapResult)

plotHapTable2

Description

plot the hapResult in table like style using grid system. This function is under development and may not stable. Some parameters may deleted or renamed in future.

Usage

plotHapTable2(
  hapSummary,
  show_indel_size = 1,
  replaceMultiAllele = TRUE,
  angle = 0,
  show_INFO = FALSE,
  INFO_split = c(";", ",", "\\|"),
  INFO_tag = "ANN",
  geneID = NA,
  tag_field = -1,
  title = "",
  gff = NULL,
  show_chr_name = TRUE,
  Chr = NULL,
  start = NULL,
  end = NULL,
  model_rect_col = "black",
  model_rect_fill = "grey80",
  model_line_col = "black",
  model_anno_txt = NULL,
  model_anno_col = "black",
  model_anno_cex = 1,
  table_txt_col = "black",
  table_txt_cex = 1,
  model_anno_pos = c(-1, -1),
  model_anno_adj = c(0, 1),
  gene_model_height = 0.2,
  space_height = 0.1,
  table_height = NULL,
  CDS_height = 0.3,
  link_line_type = 3,
  headrows = 1,
  equal_col_width = FALSE,
  head_anno = 1,
  col_annots = 0,
  row_labels = 1,
  row_annots = 1,
  table_line_col = "white",
  annot_for_each_transcrips = TRUE,
  labels_fill = "white",
  annot_fill = "grey90",
  head_fill = NULL,
  cell_fill = NULL,
  style = gpar(fontfamily = "sans", fontface = 1, cex = 0.7),
  footbar = "",
  ...
)

Arguments

hapSummary

the hapSummary or hapResult object
show_indel_size

the Indel length longer will be replaced by "i1,i2,i3,..."
replaceMultiAllele

replace multi-allele title by 'T1,T2,...' or not
angle

the angle of number positions
show_INFO

show annotation field or not, default as FALSE
INFO_split, INFO_tag, geneID, tag_field

used to set annotation in haplotype table. And the geneID was used to fileter annotation in INFO field.
title

title of plot
gff

gff or bed annotation
show_chr_name

show chromosome name at left-top cell or not
Chr, start, end

which range should be plotted in gene model
model_rect_col, model_rect_fill, model_line_col

a string specified the color for line/rectangle in gene model
table_txt_col, table_txt_cex

controls the color and size of texts in genotype table
model_anno_pos, model_anno_adj, model_anno_cex, model_anno_col, model_anno_txt

the position (x,y), just (hjust, vjust), color, size and content of annotation text in gene model
gene_model_height, table_height, space_height

the plotting range height of gene model, table and spacer
CDS_height

a numeric vector specified the height of CDS, and the height of utr is half of that, only useful when gff is provided,
link_line_type

the type of link lines for mutations in gene model and genotype table
equal_col_width

a bool or numeric vector specified whether column with should be equal
col_annots, head_anno, headrows, row_annots, row_labels

the column or row number of annotation or labels or heads
table_line_col

the line color in genotype table
annot_for_each_transcrips

mark the strand and trancripts name for each gene modle
annot_fill, head_fill, labels_fill

the fill color of annotation, head and label row or columns
cell_fill

a color vector or function or named vector specified cell fill color
style

see help(gpar)
footbar

the foot notes
...

param not used

Examples

#
data(geneHapR_test)
plotHapTable2(hapResult)
plotHapTable2(hapResult, gff = gff)

Generate Hap Results from Seqs

Description

generate hapResults from aligned and trimed sequences

Usage

seqs2hap(
  seqs,
  Ref = names(seqs)[1],
  hetero_remove = TRUE,
  na_drop = TRUE,
  maxGapsPerSeq = 0.25,
  hapPrefix = "H",
  pad = 3,
  chrName = "Chr0",
  ...
)

trimSeqs(seqs,
         minFlankFraction = 0.1)

Arguments

seqs

object of DNAStringSet or DNAMultipleAlignment class
Ref

the name of reference sequences. Default as the name of the first sequence
hetero_remove

whether remove accessions contains hybrid site or not. Default as TRUE
na_drop

whether drop sequeces contain "N" Default as TRUE.
maxGapsPerSeq

value in ⁠[0, 1]⁠ that indicates the maximum fraction of gaps allowed in each seq after alignment (default as 0.25). Seqs with gap percent exceed that will be dropped
hapPrefix

prefix of hap names. Default as "H"
pad

The number length in haplotype names should be extend to.
chrName

the Name should be used for haplotype
...

Parameters not used.
minFlankFraction

A value in ⁠[0, 1]⁠ that indicates the minimum fraction needed to call a gap in the consensus string (default as 0.1).

Value

object of hapResult class

Examples

data("geneHapR_test")
seqs
seqs <- trimSeqs(seqs,
         minFlankFraction = 0.1)
seqs
hapResult <- seqs2hap(seqs,
                      Ref = names(seqs)[1],
                      hetero_remove = TRUE, na_drop = TRUE,
                      maxGapsPerSeq = 0.25,
                      hapPrefix = "H")

Set Position of ATG as Zero

Description

Set position of ATG as zero in hap result and gff annotation. The upstream was negative while the gene range and downstream was positive.

Usage

gffSetATGas0(gff = gff, hap = hap,
             geneID = geneID,
             Chr = Chr, POS = POS)

hapSetATGas0(gff = gff, hap = hap,
             geneID = geneID,
             Chr = Chr, POS = POS)

Arguments

gff

gene annotations
hap

object of hapResult or hapSummary class
geneID

geneID
Chr

Chromsome name
POS

vector consist with start and end position

Details

Filter hap result and gff annotation according to provided information. And then set position of ATG as zero in hap result and gff annotation. The upstream was negative while the gene range and downstream was positive.

Notice: the position of "ATG" after modified was 0, 1 and 2 separately. The site in hap result exceed the selected range will be dropped.

Value

gffSetATGas0: filtered gff with position of ATG was as zero

hapSetATGas0: hap results with position of ATG was set as zero

See Also

displayVarOnGeneModel()

Examples

# load example dataset
data("geneHapR_test")


# set position of ATG as zero in gff
newgff <- gffSetATGas0(gff = gff, hap = hapResult,
                       geneID = "test1G0387",
                       Chr = "scaffold_1",
                       POS = c(4300, 7910))


data("geneHapR_test")

# set position of ATG as zero in hap results
newhapResult <- hapSetATGas0(gff = gff, hap = hapResult,
                             geneID = "test1G0387",
                             Chr = "scaffold_1",
                             POS = c(4300, 7910))

Calculation of Sites Effective

Description

Calculation of Sites Effective

Usage

siteEFF(hap, pheno, phenoNames, quality = FALSE, method = "auto",
        p.adj = "none")

Arguments

hap

object of "hapResult" class
pheno

phenotype data, with column names as pheno name and row name as accessions.
phenoNames

pheno names used for analysis, if missing, will use all pheno names in pheno
quality

bool type, indicate whther the type of phenos are quality or quantitative. Length of quality could be 1 or equal with length of phenoNames. Default as FALSE
method

character or character vector with length equal with phenoNames indicate which method should be performed towards each phenotype. Should be one of "t.test", "chi.test", "wilcox.test" and "auto". Default as "auto", see details.
p.adj

character, indicate correction method. Could be "BH", "BY", "none"

Details

The site EFF was determinate by the phenotype difference between each site geno-type.

The p was calculated with statistical analysis method as designated by the parameter method. If method set as "auto", then chi.test will be selected for quantity phenotype, eg.: color; for quantity phynotype, eg.: height, with at least 30 observations per geno-type and fit Gaussian distribution t.test will be performed, otherwise wilcox.test will be performed.

Value

a list containing two matrix names as "p" and "EFF", with column name are pheno names and row name are site position. The matrix names as "p" contains all p-value. The matrix named as "EFF" contains scaled difference between each geno-types per site.

Examples

data("geneHapR_test")

# calculate site functional effect
# siteEFF <- siteEFF(hapResult, pheno, names(pheno))
# plotEFF(siteEFF, gff = gff, Chr = "scaffold_1")

sites comparison

Description

Used for all allele effect compare once

Usage

compareAllSites(
  hap,
  pheno,
  phenoName = names(pheno)[1],
  hetero_remove = TRUE,
  title = "",
  file = file
)

Arguments

hap

object of hapResult class
pheno

a data.frame contains phenotypes
phenoName

the name of used phenotype
hetero_remove

removing the heter-sites or not, default as TRUE
title

the title of the figure
file

if provieds a file path the comparing results will saved to file.

table2hap

Description

convert variants stored in table format into hapResult

Usage

table2hap(x, hapPrefix = "H", pad = 3, hetero_remove = TRUE, na_drop = TRUE)

Arguments

x

a data.frame contains variants information. The first file column are fix as Chrome name, position, reference nuclieotide, alter nuclieotide and INFO. Accession genotype should be in followed columns. "-" will be treated as Indel. "." and "N" will be treated as missing data. Heterozygotes should be "A/T", "AAA/A"
hapPrefix

prefix of haplotype names
pad

The number length in haplotype names should be extend to.
hetero_remove

whether remove accessions contains hyb-sites, Character not A T C G
na_drop

whether drop accessions contains missing data ("N", "NA", ".")

Value

object of hapSummary class

Examples

data("geneHapR_test")
   hapResult <- table2hap(gt.geno, hapPrefix = "H",
                          hetero_remove = TRUE,
                          na_drop = TRUE)

Generat Haps from VCF

Description

Generate hapResult from vcfR object A simple filter by position was provided in this function, however it's prefer to filter VCF (vcfR object) through filter_vcf().

Usage

vcf2hap(
  vcf,
  hapPrefix = "H",
  filter_Chr = FALSE,
  filter_POS = FALSE,
  pad = 3,
  hetero_remove = TRUE,
  na_drop = TRUE,
  ...
)

Arguments

vcf

vcfR object imported by import_vcf()
hapPrefix

prefix of hap names, default as "H"
filter_Chr

not used
filter_POS

not used
pad

The number length in haplotype names should be extend to.
hetero_remove

whether remove accessions contains hybrid site or not. Default as TRUE
na_drop

whether remove accessions contains unknown allele site or not Default as TRUE.
...

Parameters not used

Value

object of hapResult class

Author(s)

Zhangrenl

See Also

extract genotype from vcf: vcfR::extract_gt_tidy(), import vcf files: import_vcf() (preferred) and vcfR::read.vcfR(), filter vcf according position and annotations: filter_vcf()

Examples

data("geneHapR_test")
hapResult <- vcf2hap(vcf)

Save Haplotype Results to Disk

Description

This function will write hap result into a txt file.

Usage

write.hap(x, file = file, sep = "\t", pheno = pheno, phenoName = phenoName)

Arguments

x

objec of hapResult or hapSummary class
file

file path, where to save the hap result/summary
sep

the field separator string. Values within each row of x are separated by this string. Default as "⁠\t⁠"
pheno, phenoName

the phenotype data.frame, only used for export hapResult object.

Details

The hap result and hap summary result have common features. The common features of these two types are: First four rows contains extra information: CHR, POS, INFO and ALLELE Hap names were in the first column. The differences are: Hap summary result have a freq column while hap result not. Rows represent haplotypes in hap summary result, while rows represent accessions in hap result. In addtion, the accessions of each haplotype in hap summary result were separated by ";".

Value

No return value

Examples

oriDir <- getwd()
 temp_dir <- tempdir()
 if(! dir.exists(temp_dir))
   dir.create(temp_dir)
 setwd(temp_dir)
data("geneHapR_test")
write.hap(hapResult, file = "hapResult.txt")
setwd(oriDir)