geneHapR

https://gitee.com/zhangrenl/genehapr/wikis
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geneHapR is an R package designed for comprehensive gene haplotypic statistics and visualization,...
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### Start geneHapR GUI

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Launches the graphical user interface for geneHapR. This command opens a new interactive environment for analysis.

```R
startGUI.geneHapR()
```

--------------------------------

### Install geneHapR and Dependencies

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Installs necessary Bioconductor and CRAN packages, and geneHapR from CRAN or Gitee.

```R
# a few non-CRAN packages may need install manually
if(! require(BiocManager)) 
    install.packages("BiocManager")
BiocManager::install(c("Biostrings", "GenomicRanges", "muscle", "IRanges", "rtracklayer"))

# install geneHapR from CRAN (stable)
install.packages("geneHapR")

# install geneHapR from gitee (newest but may unstable)
# need devtools package and git software
devtools::install_git("https://gitee.com/zhangrenl/genehapr")  

```

--------------------------------

### Install geneHapR and Dependencies in R

Source: https://gitee.com/zhangrenl/genehapr#vignettes

Installs necessary non-CRAN R packages and the geneHapR package itself using BiocManager and devtools. Ensure devtools and git are installed.

```r
if(! require(BiocManager)) 
    install.packages("BiocManager")
BiocManager::install(c("Biostrings", "GenomicRanges", "muscle", "IRanges", "rtracklayer", "trackViewer"))
install.packages("geneHapR")
devtools::install_git("https://gitee.com/zhangrenl/lolliplot")    # need devtools package and git software
devtools::install_git("https://gitee.com/zhangrenl/genehapr")  # need devtools package and git software
```

--------------------------------

### Install and Run geneHapR Docker Image

Source: https://gitee.com/zhangrenl/genehapr#vignettes

Pulls the latest geneHapR Docker image and runs it interactively. This method is suitable for Linux systems.

```bash
# pull the docker from hub.docker.com
docker pull zhangrenl/genehapr:latest

# run the image
docker run -ti --rm zhangrenl/genehapr bash
```

--------------------------------

### Set Start Codon Coordinate to Zero

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Adjusts the coordinate system to set the start codon (ATG) position to zero for both GFF annotations and haplotype results.

```R
# b) Set the coordinate of the start condon "ATG" as zero
# Annotation
newgff <- gffSetATGas0(gff = gff, hap = hapResult,
                       geneID = "test1G0387",
                       Chr = "scaffold_1",
                       POS = c(4300, 7910))

# Haplotype result
newhapResult <- hapSetATGas0(gff = gff, hap = hapResult,
                             geneID = "test1G0387",
                             Chr = "scaffold_1",
                             POS = c(4300, 7910))

```

--------------------------------

### Filter VCF by Position

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Filter a VCF object by genomic position. Specify the chromosome, start, and end positions.

```r
# Filter VCF by position
vcf_f1 <- filter_vcf(vcf, mode = "POS",
                     Chr = "scaffold_1",
                     start = 4300, end = 5890)
```

--------------------------------

### Filter PLINK Data by Position

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Filter PLINK data (PED/MAP) by genomic position. Specify the chromosome, start, and end positions.

```r
# Filter of p.link data
p.link <- filter_plink.pedmap(p.link, mode = "POS", 
                              Chr = "Chr08", 
                              start = 25947258, end = 25948258)
```

--------------------------------

### Filter VCF by Position and Annotation

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Filter a VCF object by both genomic position and annotation type. Requires specifying chromosome, start, end, GFF object, and type.

```r
# Filter VCF by position and annotation
vcf_f3 <- filter_vcf(vcf, mode = "both",
                     Chr = "scaffold_1",
                     start = 4300, end = 5890,
                     gff = gff,
                     type = "CDS")
```

--------------------------------

### Import VCF Files

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import genetic data from VCF files, including gzipped VCFs. Ensure the file path is correct.

```r
# Import VCF file
vcf <- import_vcf("your_vcf_file_path.vcf")     # Import vcf file
vcf <- import_vcf("your_vcf_file_path.vcf.gz")  # Import gziped vcf file
```

--------------------------------

### Import FASTA Sequences

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import DNA sequences from a FASTA file. The format parameter should be set to 'fasta'.

```r
# Import sequences in FASTA file
seqs <- import_seqs("your_DNA_seq_file_path.fa", format = "fasta")
```

--------------------------------

### Import PLINK PED/MAP Files

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import genotype data from PLINK PED and MAP files. You can specify the map and ped files separately or provide a root name. Delimiter for ped and map files can be specified.

```r
# Import p.link (ped/map) file
plink <- import_plink.pedmap(mapfile = "p_link.map", pedfile = "p_link.ped", 
                             sep_ped = "\t", sep_map = "\t")
plink <- import_plink.pedmap(root = "p_link", sep_ped = "\t", sep_map = "\t")
```

--------------------------------

### Import GFF/GFF3 Annotation

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import genomic annotation data from GFF or GFF3 format files. Specify the file path and format.

```r
# Import annotation in GFF/GFF3 format
gff <- import_gff("your_gff_file_path.gff", format = "GFF")
```

--------------------------------

### Import BED Annotation

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import genomic annotation data from BED4 or BED6 format files. Specify the file path.

```r
# Import annotation in BED4/BED6 format
bed <- import_bed("your_bed_file_path.bed")
```

--------------------------------

### Import Genotype Data from Table/CSV

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Read genotype data from a table file (e.g., TXT) or a CSV file. Specify the file path and whether to use row names and headers.

```r
# Import genotype data in table file
geno <- read.table(file = "geno.txt", row.names = 1, header = TRUE)
geno <- read.csv(file = "geno.csv")
```

--------------------------------

### Import Phenotype/Accession Information

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Import phenotype data or accession group/location information from a text file. Use the import_AccINFO function.

```r
# Importing accession information
# Import phynotype data
pheno <- import_AccINFO("your_pheno_file_path.txt")

# Import accession group/location information
AccINFO <- import_AccINFO("accession_group_file_path.txt")
```

--------------------------------

### Plot Haplotype Table with Annotations

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Visualizes haplotype summary data as a table, including gene model annotations.

```R
# plot haptable with annotations
plotHapTable(hapSummary, hapPrefix = hapPrefix, # input
             angle = 45, # the angle of position coordinates
             displayIndelSize = 4, # max Indel for display
             title = geneID, # title of the figure
             INFO_tag = "ANN", tag_field = 11, geneName = geneID) # add annotations

```

--------------------------------

### Haplotype vs. Phenotype per Site Analysis

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Analyzes phenotype frequencies per site for a specific haplotype and phenotype. Requires 'hap', 'pheno', and 'phenoName'. Filters by minimum frequency.

```R
# hapVsPhenoPerSite(hap = hap, 
#                   pheno = pheno, 
#                   phenoName = names(pheno)[1], # the name of interested phenotype
#                   freq.min = 5)                # minimal number of frequencies
```

--------------------------------

### Multiple Haplotype vs. Phenotype Associations

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Performs and saves haplotype vs. phenotype associations for multiple phenotypes. Configurable output format, resolution, and filename.

```R
hapVsPhenos(hap = hapResult, pheno = pheno,
            hapPrefix = hapPrefix,
            title = geneID, # main title
            compression = "lzw", # see help(hapVsPhenos)
            res = 300, width = 12, height = 12, # unit is "inch"
            outPutSingleFile = TRUE, # only used while filetype is pdf
            filename.surfix = "pdf", # file type should be one of pdf, png, jpg, tiff, bmp
            filename.prefix = geneID)
```

--------------------------------

### Generate and Visualize Haplotype Network

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Constructs a haplotype network based on summary data and associated information, then visualizes it, allowing customization of node size, link appearance, and mutation display.

```R
# Generate haplonet
hapNet <- get_hapNet(hapSummary,
                     AccINFO = AccINFO,
                     groupName = "Type")

# Visualization of haploNet
plotHapNet(hapNet,
           size = "freq",        # circle size
           scale = "log2",       # scale circle with 'log10(size + 1)'
           cex = 0.8,            # 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 = c(-12.5, 7)) # legend position

```

--------------------------------

### Import Haplotype Data

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Imports previously exported haplotype results and summary data.

```R
# import haplotype data
hapResult <- import_hap(file = "GeneID.hapResult")
hapSummary <- import_hap(file = "GeneID.hapSummary")

```

--------------------------------

### Plot Haplotype Table without Annotations

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Visualizes haplotype summary data as a table, without gene model annotations.

```R
# plot haptable without annotations
plotHapTable(hapSummary, hapPrefix = hapPrefix,
             angle = 45,
             displayIndelSize = 0,
             title = geneID)

```

--------------------------------

### Summarize Haplotype Results

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Generates a concise summary of the haplotype identification results.

```R
# Get concise result
hapSummary <- hap_summary(hapResult)

```

--------------------------------

### Haplotype Statistics and Export

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Calculates haplotype statistics, removes hybrid sites, handles missing data, and exports the results.

```R
# haplotype statistics
hapResult <- vcf2hap(vcf, hapPrefix = hapPrefix,
                    hetero_remove = TRUE, # remove hybrid sites
                    na_drop = TRUE) # ignore site containing missing data
hapSummary <- hap_summary(hapResult, hapPrefix = hapPrefix)

# export haplotype data
write.hap(hapResult, file = "GeneID.hapResult")
write.hap(hapSummary, file = "GeneID.hapSummary")

```

--------------------------------

### Calculate and Plot Site Effects

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Calculates and plots site-specific effects on phenotypes. Requires 'hapResult', 'pheno', 'gff', and chromosome/region details. Customizable display options.

```R
# EFF <- siteEFF(hapResult, pheno)
# plotEFF(EFF, gff = gff,
#         Chr = Chr, start = start, end = end,
#         showType = c("five_prime_UTR", "CDS", "three_prime_UTR"), # see help(plotEFF)
#         ylab = "siteEFF", cex = 0.5,
#         legend.cex = 0.8, # legend size
#         legend.ncol = 2, # legend columns, usful for multi-transcripts
#         main = geneID, # main title
#         CDS.height = 1, # controls the height of CDS, heights of others will be half of that
#         markMutants = TRUE, # mark mutants by short lines
#         mutants.col = 1, mutants.type = 1, # parameters for appearance of mutants
#         pch = 20) # points type
```

--------------------------------

### Haplotype Identification from VCF

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Identifies haplotypes from a VCF file. Ensures heterozygotes are removed and NA values are dropped.

```R
# Identification of haplotyping from vcf
hapResult <- vcf2hap(vcf, hapPrefix = "H",
                     hetero_remove = TRUE, na_drop = TRUE)

```

--------------------------------

### Plot Haplotype Table

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Generates a table visualization of haplotype variants. Can include additional information tags and customize display options.

```R
# Plot variants details
plotHapTable(hapSummary)

# Plot variants details with additional information
plotHapTable(hapSummary,
             hapPrefix = "H",
             INFO_tag = c("CDSChange", "PrChange"),
             tag_name = c("CDS", "Pr"),
             displayIndelSize = 1, 
             angle = 45,
             replaceMultiAllele = TRUE,
             ALLELE.color = "grey90")

```

--------------------------------

### Plot Haplotype Distribution on China Map

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Visualizes the geographical distribution of specified haplotypes on a map of China, using provided coordinates and map data.

```R
# Geo distribution
# 地图获取 （json格式）
# http://datav.aliyun.com/portal/school/atlas/area_selector
# 格式转换（json转shp）
# https://mygeodata.cloud/converter/json-to-shp
# 文件解压后有多个文件都需要放到同一个文件夹内
library(mapdata)
library(maptools)
# import GEO data (china)
chinaGEO <- readShapePoly("/data/zhangrenliang/Hap/chinaGEO/china-polygon.shp")
map(chinaGEO)

AccINFO$Longitude <- as.numeric(AccINFO$Longitude)
AccINFO$Latitude <- as.numeric(AccINFO$Latitude)
# 中国陆地
hapDistribution(hapResult, AccINFO = AccINFO,
                database = chinaGEO, # mapdata or charater eg. china, world, 
                regions = ".", # see help(hapDistribution)
                hapNames = c("H001", "H002", "H003"), # plotted haplotypes
                symbolSize = 0.12, # circle size
                LON.col = "Longitude", LAT.col = "Latitude", # chich col contains longitude or latitude
                legend = "bottomleft", cex.legend = 1, # legend position and legend size
                xlim = c(72, 138), ylim = c(17,54), # region size
                lwd.pie = 0.2, lwd = 1.5, # line width of pies and maps
                main = geneID) # main title
# 中国南海

```

--------------------------------

### Phenotype Comparison with Haplotypes

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Compares phenotypes against haplotype data to identify associations. Generates plots for p-values and violin plots of trait distribution per haplotype. Requires minimum number of accessions per haplotype.

```R
# Single phenotype comparison
results <- hapVsPheno(hapResult,
                      hapPrefix = "H",
                      title = "This is title",
                      mergeFigs = FALSE,
                      pheno = pheno,
                      phenoName = "GrainWeight.2021",
                      minAcc = 3)
plot(results$fig_pvalue)
plot(results$fig_Violin)

```

--------------------------------

### Haplotype Identification from Sequences

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Identifies haplotypes from sequence data. Allows for a maximum proportion of gaps per sequence and removes heterozygotes and NA values.

```R
# Identification of haplotyping from sequences
hapResult <- seqs2hap(seqs, hapPrefix = "H",
                      Ref = names(seqs)[1], maxGapsPerSeq = 0.25,
                      hetero_remove = TRUE, na_drop = TRUE)

```

--------------------------------

### Import and Filter VCF Data

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Imports VCF data and filters it based on genomic position and feature type (e.g., CDS).

```R
# library package
library(geneHapR)

# set working directory
setwd("./zhangrenliang/Hap/geneHapR")


# import annotations and phenotypes and accession informations
gff <- import_gff("gff/Yugu1.gff3")
pheno <- import_AccINFO("pheno/S_DroughtPEGTreatment.txt")
AccINFO <- import_AccINFO("pheno/AccINFO.txt", na.strings = "NA")

# set of basic parameters
vcf_file <- "vcf/cleanvcf/Chr8/Seita.8G036300_2350931_2353396_-_3k_final.vcf.gz"
geneID <- "Seita.8G036300_CDS"
Chr <- "scaffold_8"
start <- 2350931
end <- 2353396
hapPrefix <- "H"

# import and filter VCF file
vcf <- import_vcf(vcf_file)
vcf <- filter_vcf(vcf, gff,
                 mode = "both", # one of both, POS, Type
                 start = start, end = end, Chr = Chr, # POS parameters
                 type = "CDS", cusTyp = c("CDS")) # Type parameters

```

--------------------------------

### Haplotype Identification from Table (Data Frame)

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Identifies haplotypes from a data frame containing genotype data. Heterozygotes are removed and NA values are dropped.

```R
# Identification of haplotyping from table (data.frame)
hapResult <- table2hap(gt.geno, hapPrefix = "H",
                       hetero_remove = TRUE, na_drop = TRUE)

```

--------------------------------

### Display Variants on Gene Model

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Visualizes genetic variants on a gene model, showing different feature types like CDS and UTRs.

```R
# display variants on gene model
displayVarOnGeneModel(gff = gff, hapSummary = hapSummary,
                      startPOS = start-10,
                      endPOS = end+10,
                      CDS_h = 0.05, fiveUTR_h = 0.25, threeUTR_h = 0.25, # gene model parameters
                      cex = 0.8) # size of variants

```

--------------------------------

### Haplotype vs. Phenotype Association

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Analyzes the association between haplotypes and phenotypes. Requires 'hapResult', 'pheno', and 'hapPrefix' to be defined.

```R
hapVsPheno(hap = hapResult, pheno = pheno, hapPrefix = hapPrefix,
           title = geneID, # main title
           minAcc = 4,  # min number accession for pvalue calculation
           mergeFigs = FALSE)
```

--------------------------------

### Filter VCF by Annotation Type

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Filter a VCF object by annotation type (e.g., CDS, UTR). Requires a GFF object and the desired type.

```r
# Filter VCF by annotation
vcf_f2 <- filter_vcf(vcf, mode = "type",
                     gff = gff,
                     type = "CDS")
```

--------------------------------

### Plot LD Heatmap

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Generates a Linkage Disequilibrium (LD) heatmap. Requires 'hapResult', 'gff', and chromosome details.

```R
plot_LDheatmap(hap = hapResult,
               gff = gff,
               Chr = hapResult[1,2],
               start = 4000, end = 8200)
```

--------------------------------

### Plot LD-Heatmap

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Generates a Linkage Disequilibrium (LD) heatmap using haplotype data, GFF annotations, and genomic coordinates. Includes options for color legend and map display.

```R
plot_LDheatmap(hap = hapResult,
               colorLegend = TRUE,
               add.map = TRUE,
               gff = gff, 
               start = start, 
               end = end,
               Chr = hapResult[1,2])

```

--------------------------------

### Generate and Plot Haplotype Network

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Creates a haplotype network based on accession types and plots it using specified parameters for links and labels.

```R
# haplotype net visualization
unique(AccINFO$Type)
hapnet <- get_hapNet(hapSummary, AccINFO = AccINFO,
                    groupName = "Type", # column contains accession types
                    na.label = "Unknown") # missing data replaced text

plotHapNet(hapnet, scale = "log2",
           show.mutation = 2, # mutants type, one of 0,1,2,3
           col.link = 2, link.width = 2, # links colours and widths
           main = geneID, # main title
           labels = T, # plot labels or not
           # legend = F) # do not add legend
           legend = c(12,0), cex.legend = 0.6) # legend posizion and legend size

```

--------------------------------

### Add Information to Haplotype Results

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Adds custom information tags to the haplotype results. Can replace existing tags if specified.

```R
# a) Add information
# Assume there were three sites in hapResult
hapResult <- addINFO(hapResult,
                     tag = "PrChange",
                     values = c("C->D", "V->R", "G->N"),
                     replace = TRUE) # replace the old INFO or not

```

--------------------------------

### Display Variants on Gene Model

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Visualizes variants on a gene schematic diagram using GFF and haplotype summary data within a specified genomic region.

```R
displayVarOnGeneModel(gff, hapSummary,
                      start = 9154380, end = 9152730)

```

--------------------------------

### Haplotype Identification from PLINK PED/MAP

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Identifies haplotypes from PLINK PED/MAP files. Heterozygotes are removed and NA values are dropped.

```R
# Identification of haplotyping from p.link (ped/map)
hapResult <- plink.pedmap2hap(p.link, hapPrefix = "H",
                              hetero_remove = TRUE, na_drop = TRUE)

```

--------------------------------

### Plot Haplotype Distribution

Source: https://gitee.com/zhangrenl/genehapr/wikis/An_example_in_Setaria_italica

Plots the geographical distribution of haplotypes. Ensure 'hapResult' and 'AccINFO' are loaded and 'chinaGEO' database is available.

```R
par(new = T)
hapDistribution(hapResult, AccINFO = AccINFO, database = chinaGEO,
                hapNames = c("H001", "H002", "H003", "H004"), symbolSize = 0.1,
                LON.col = "Longitude", LAT.col = "Latitude",
                legend = F, cex.legend = 1,lwd.pie = 0.1, lwd = 2,
                xlim = c(105, 125), ylim = c(2,25))
par(new = T)
plot(c(106,14), type = "n", xaxt = "n", yaxt = "n",
     xlab = "", ylab = "",
     frame.plot = T)
```

--------------------------------

### Filter Large VCF by Multiple Genes/Ranges

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Extract multiple genes or ranges from a large VCF file and save them to separate output VCF files. Supports specifying multiple chromosomes and position lists. Existing files will be overridden if override is TRUE.

```r
# b) Extract multi genes/ranges from large vcf
filterLargeVCF(VCFin = "Ori.vcf.gz",          # surfix should be .vcf.gz or .vcf
               VCFout = c("filtered1.vcf.gz", # surfix should be .vcf.gz or .vcf
                          "filtered2.vcf.gz", 
                          "filtered3.vcf.gz"),
               Chr = c("scaffold_8",
                       "scaffold_8",
                       "scaffold_7"),
               POS = list(c(19802,24501), 
                          c(27341,28949),
                          c(38469,40344)),
               override = TRUE) # If TRUE, existed file will be override without warning
```

--------------------------------

### Haplotype Identification from Hapmap

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Identifies haplotypes from Hapmap formatted data. Heterozygotes are removed and NA values are dropped.

```R
# Identification of haplotyping from Hapmap
hapResult <- hmp2hap(gt.geno, hapPrefix = "H",
                     hetero_remove = TRUE, na_drop = TRUE)

```

--------------------------------

### Filter Large VCF by Gene/Range

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Extract a specific gene or range from a large VCF file and save it to a new VCF file. The override option allows overwriting existing files.

```r
# a) Extract a single gene/range from a large vcf
# New VCF file will be saved to disk
filterLargeVCF(VCFin = "Ori.vcf.gz",
               VCFout = "filtered.vcf.gz",
               Chr = "scaffold_8",
               POS = c(19802,24501),
               override = TRUE)
```

--------------------------------

### Filter Haplotype Results

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Adjust hapResult or hapSummary objects by removing specific positions, accessions, or haplotypes. Can also filter haplotypes based on minimum frequency.

```r
hap <- filter_hap(hapSummary,
                  rm.mode = c("position", "accession", "haplotype", "freq"),
                  position.rm = c(4879, 4950),      # positions to be removed
                  accession.rm = c("C1", "C9"),     # accessions to be removed
                  haplotype.rm = c("H009", "H008"), # haplotypes to be removed
                  freq.min = 5)  # haplotypes with accession less than freq.min will be removed
```

--------------------------------

### Haplotype Geographical Distribution

Source: https://gitee.com/zhangrenl/genehapr/wikis/Introduction.md

Displays the geographical distribution of specified haplotypes on a world map, using accession information including longitude and latitude. Requires mapdata and maptools libraries.

```R
# library(mapdata)
# library(maptools)
hapDistribution(hapResult,
                database = "world",
                AccINFO = AccINFO,
                LON.col = "longitude",
                LAT.col = "latitude", 
                hapNames = c("H001", "H002", "H003"), 
                legend = TRUE)

```
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