WH: WH In DCLEAR: Distance Based Cell Lineage Reconstruction

Description

implementation of weighted hamming algorithm

Usage

 1 WH(x, InfoW, dropout = FALSE)

Arguments

 x Sequence object of 'phyDat' type. InfoW Weight vector for the calculation of weighted hamming distance dropout Different weighting strategy is taken to consider interval dropout with dropout = 'TRUE'. Default is, dropout = 'FALSE'.

Value

Calculated distance matrix of input sequences. The result is a 'dist' class object.

Il-Youp Kwak

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 set.seed(1) library(phangorn) mu_d1 = c( 30, 20, 10, 5, 5, 1, 0.01, 0.001) mu_d1 = mu_d1/sum(mu_d1) simn = 10 # number of cell samples m = 10 ## number of targets sD = sim_seqdata(sim_n = simn, m = m, mu_d = 0.03, d = 12, n_s = length(mu_d1), outcome_prob = mu_d1, p_d = 0.005 ) ## RF score with hamming distance D_h = dist.hamming(sD\$seqs) tree_h= NJ(D_h) RF.dist(tree_h, sD\$tree, normalize = TRUE) ## RF score with weighted hamming InfoW = -log(mu_d1) InfoW[1:2] = 1 InfoW[3:7] = 4.5 D_wh = WH(sD\$seqs, InfoW) tree_wh= NJ(D_wh) RF.dist(tree_wh, sD\$tree, normalize = TRUE) ## RF score with weighted hamming, cosidering dropout situation nfoW = -log(mu_d1) InfoW = 1 InfoW = 12 InfoW[3:7] = 3 D_wh2 = WH(sD\$seqs, InfoW, dropout=TRUE) tree_wh2= NJ(D_wh2) RF.dist(tree_wh2, sD\$tree, normalize = TRUE)

DCLEAR documentation built on Sept. 5, 2021, 5:21 p.m.