## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE)
## ---- eval=FALSE--------------------------------------------------------------
# library(BC32BarSeq)
## ---- eval=FALSE--------------------------------------------------------------
# initBC()
## ---- eval=FALSE--------------------------------------------------------------
# pat <- 'CTANNCAGNNCTTNNCGANNCTANNCTTNNGGANNCTANNCAGNNCTTNNCGANNCTANNCTTNNGGANNCTANNCAGNN' # matching pattern (BC32)
# restriction <- 'CTCGAG' # The restriction sequence flanking the barcode pattern at the 3'end
# idx_mis <- 1 # number of mismatch allowed in index
# base_q <- 20 # minimum average base quality score in first 90 nucleotides
# bb_mis <- 1 # number of mismatch allowed in barcode backbone sequence
# indels <- 1 # total edit distance resulting from indels that are tolerated for barcode matching (try to keep this low)
# dl <- 12 # threshold for Damerau-Levenshtein distance in step pooling similar sequences
# min.count <- 10 # in the final merged table, replace barcodes with occurence below min.count (considered as noise) with 0
## ---- eval=FALSE--------------------------------------------------------------
# sampname <- read.delim('sampname.txt', # sampname provides a list of sample names, matching sequencing files and multiplexing index
# header = F,
# stringsAsFactor = F)
# colnames(sampname) <- c('sample', 'file', 'index')
## ---- eval=FALSE--------------------------------------------------------------
# bc_data <- generateSummaries(pat = pat,
# restriction = restriction,
# sampname = sampname,
# base_q = base_q,
# idx_mis = idx_mis,
# bb_mis = bb_mis,
# indels = indels)
#
# save(bc_data, 'bc_data.Rdata') # Keep this object, it is required for downstream processing!
## ---- eval=FALSE--------------------------------------------------------------
# invited_idx <- read.delim('invited_idx.txt', header = F, stringsAsFactor = F) # format: column 1 -> sequences, column 2 -> sample name
# invited_bc <- read.delim('invited_bc.txt', header = F, stringsAsFactor = F) # format: column 1 -> sequences
#
# addInvitedSeq(pat = pat,
# bc_data = bc_data,
# dir = getwd(),
# invited_idx = invited_idx,
# invited_bc = invited_bc)
## ---- eval=FALSE--------------------------------------------------------------
# poolBC(sampname = sampname,
# dir = getwd(),
# dl = dl)
#
# poolingStatsPlot(dir = getwd()) # export plots
## ---- eval=FALSE--------------------------------------------------------------
# counts <- mergeSummaries(sampname = sampname,
# dir = getwd(),
# min.count = min.count,
# cleancol = F)
#
# freq <- countsToFreq(counts = counts)
#
# cpm <- countsToCPM(counts = counts)
#
# write.table(counts, 'merged_summary_pooled_count.txt', sep='\t', row.names = F)
# write.table(freq, 'merged_summary_pooled_freq.txt', sep='\t', row.names = F)
# write.table(cpm, 'merged_summary_pooled_cpm.txt', sep='\t', row.names = F)
#
# # export session info for reproducibility
# writeLines(capture.output(sessionInfo()), 'sessionInfo.txt')
## ---- eval=FALSE--------------------------------------------------------------
# exploreBC(getwd())
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