ghap.blockstats | R Documentation |
Generate HapBlock summary statistics from pre-computed HapAlleles statistics.
ghap.blockstats(hapstats, ncores = 1, verbose = TRUE)
hapstats |
A data.frame containing HapAllele statistics, as generated by the |
ncores |
A numeric value specifying the number of cores to be used in parallel computing (default = 1). |
verbose |
A logical value specfying whether log messages should be printed (default = TRUE). |
For each HapBlock, the function counts the number of unique HapAlleles and computes the expected heterozygosity 1 - ∑ {p_{i}}^2 , where p_{i} is the frequency of HapAllele i. Please notice that when HapAlleles are prunned out by frequency the block statistics can retrieve high expected heterozygosity for blocks with small number of HapAlleles.
A data frame with columns:
BLOCK |
Block alias. |
CHR |
Chromosome name. |
BP1 |
Block start position. |
BP2 |
Block end position. |
EXP.H |
Block expected heterozygosity. |
N.ALLELES |
Number of HapAlleles per block. |
Yuri Tani Utsunomiya <ytutsunomiya@gmail.com>
# #### DO NOT RUN IF NOT NECESSARY ### # # # Copy phase data in the current working directory # exfiles <- ghap.makefile(dataset = "example", # format = "phase", # verbose = TRUE) # file.copy(from = exfiles, to = "./") # # # Load data # phase <- ghap.loadphase("example") # # # Generate blocks of 5 markers # blocks <- ghap.blockgen(phase, windowsize = 5, # slide = 5, unit = "marker") # # # Haplotyping # ghap.haplotyping(phase = phase, blocks = blocks, outfile = "example", # binary = T, ncores = 1) # # # Load haplotype genotypes using prefix # haplo <- ghap.loadhaplo("example") # # # Subset # ids <- which(haplo$pop == "Pure1") # haplo <- ghap.subset(haplo, ids = ids, # variants = haplo$allele.in, # index = TRUE) # # # Compute haplotype statistics # hapstats <- ghap.hapstats(haplo) # # ### RUN ### # # # Compute block statistics # blockstats <- ghap.blockstats(hapstats)
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