How we generated our prediction for subchallenge 3

library(knitr)
opts_chunk$set(fig.width = 12)
library(DCLEAR)
library(phangorn)
#library(parallel)

This vignettes illustrate how our team prepared a submission for subchallenge 3.

Data processing

The 'csv_file' is a file path of evaluation for subchallenge 3 given from the competition. Change the like with the one you would like to predict.

csv_file <- 'Data/subC3/SubC3_10K_0001_mutation_table.csv'
\dontrun{x <- read.table(csv_file, header = T, sep = ',', colClasses = "character")}

Initical state is '0', interval dropout is '-', point dropout is '' (point dropout was '' from the file, but we will replace it with ''), and mutational outcome states are 'A' to 'Z'.

\dontrun{states <- c('0', '-', '*', LETTERS)}

Read file and save it as 'phyDat' object.

\dontrun{tip_names <- x[, 1]}
\dontrun{x <- x[,-1]}
\dontrun{rownames(x) <- tip_names}
\dontrun{x[ x == '' ] = '*'  ## specified * as point dropout (point missing)} 
\dontrun{x = as.matrix(x)}
\dontrun{x <- x %>% phyDat(type = 'USER', levels = states)}

\dontrun{states2num = 1:length(states)}
\dontrun{names(states2num) = states}

Weight parameters for the prediction

We tried weighted hamming I and II with large number of parameter combinations, and found weighted hamming I with weight below worked fairly well.

InfoW = 1:5
InfoW[1] = 1  ## Score 
InfoW[2] = .9
InfoW[3] = .4
InfoW[4:26] = 3

Generating final submission file for subchallenge 3

\dontrun{aTree2 <- x %>% dist_weighted_hamming(InfoW, FALSE) %>% fastme.ols()}
\dontrun{write.tree(aTree2, "Kwak_Gongsub3_submission.nw")}

Thanks!



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