source("R/Posterior.R")
source("R/Stats.R")
source("R/Util.R")
require(Biostrings)
require(Peptides)
require(rstan)
rstan_options(auto_write = TRUE)
require(ggplot2)
d <- read.csv(file = "cells.data.tsv", header = T,
as.is = T, sep = "\t")
d$replicate <- NA
d <- d[, c("replicate" ,"sample", "condition", "cdr3.aa")]
d$cdr3.sequence <- d$cdr3.aa
d$cdr3.aa <- NULL
cdr3.data <- d
# table(cdr3.data$sample, cdr3.data$condition)
# cdr3.data <- cdr3.data[cdr3.data$sample %in% c("11B_S3", "12B_S7",
# "11E_S6", "12E_S8",
# "1N_S15", "3N_S17"), ]
x <- parseCdr3Data(cdr3.data = cdr3.data)
x <- getStanFormattedCdr3Data(parsed.cdr3.data = x)$length
m <- rstan::stan_model(file = "inst/extdata/cdr3_length_s.stan",
auto_write = TRUE)
g <- rstan::sampling(object = m,
data = x,
chains = 2,
cores = 2,
iter = 2000,
warmup = 1000,
control = list(adapt_delta = 0.99,
max_treedepth = 12),
algorithm = "NUTS")
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