LBMC » NucleoMiner

  return(ratio)

### Returns the log likelihood ratio.

}, ex=function(){

  mean1=5; sd1=2; card2 = 250

  mean2=6; sd2=3; card1 = 200

  x1 = rnorm(card1, mean1, sd1)

aggregate_intra_strain_nucs = structure(function(# Aggregate replicated sample's nucleosomes.

samples, ##<< A list of samples. Each sample is a list like \emph{sample = list(id=..., marker=..., strain=..., roi=..., inputs=..., outputs=...)} with \emph{roi = list(name=..., begin=...,  end=..., chr=..., genome=...)}.

coord_max=20000000 ##<< A too big value to be a coord for a nucleosome lower bound.

){

	end_of_tracks = function(tracks) {

}

align_inter_strain_nucs = structure(function(# Aligns nucleosomes between 2 strains.

replicates, ##<< Set of replicates, ideally 3 per strain.

corr_thres=0.5, ##<< Correlation threshold.

config=NULL, ##<< GLOBAL config variable

... ##<< A list of parameters that will be passed to \emph{aggregate_intra_strain_nucs} if needed.

) {

								reads_strain_ref1 = reads_strain_ref1 - rep(diff, length(reads_strain_ref1))

								llr_score = llr_score_nvecs(list(reads_strain_ref1, reads_strain_ref2))

								llr_scores = c(llr_scores, llr_score)

                if (llr_score < llr_thres) {

									tmp_nuc = list()

									# strain_ref1

  return(non_inter_fuzzy)

}

### This function is based on sort of lower bounds to detect regions that intersect. We compare lower bound and upper bound of the porevious item. This function embed a while loop and break break regions list become stable.

regions ##<< The Regions to be aggregated

) {

# }

translate_regions = function(# Translate a list of regions from a strain ref to another.

regions, ##<< Regions to be translated.

combi, ##<< Combination of strains.

cur_index, ##<< The region of interest index.

  return(all_reads)

}

### This function build the design according sample properties.

marker, ##<< The marker to considere.

combi, ##<< The starin combination to considere.

}

plot_dist_samples = function(# Plot the distribution of reads.

strain, ##<< The strain to considere.

marker, ##<< The marker to considere.

res, ##<< Data

  legend("topright", col=(1:length(sample_ids))+1, lty=1:length(sample_ids), legend=cols)

}

) {

	snep_count_table = reads[, rownames(snep_design)]

	cdsFull = newCountDataSet(snep_count_table, snep_design)

combi, ##<< The strain combination involved.

form, ##<< the nuc form involved.

all_samples, ##<< Global list of samples.

config=NULL ##<< GLOBAL config variable

) {

  # PRETREAT

	reads$pvalsGLM = signif(tmp_analyse[[4]], 5)

	snep_design = tmp_analyse[[3]]

  # print(snep_design)

	reads$snep_index = reads$pvalsGLM < thres

	print(paste(sum(reads$snep_index), " SNEPs found for ", length(reads[,1])," nucs and ", fdr*100,"% of FDR.", sep = ""))

  return(reads)

ROM2ARAB = function(# Roman to Arabic pair list.

){list(

  "I" = 1,

  "II" = 2,

})

ARAB2ROM = function(# Arabic to Roman pair list.

){switch_pairlist(ROM2ARAB())}

c2c_extraction = function(# Extract a sub part of the corresponding c2c file

strain1, ##<< the key strain

strain2, ##<< the target strain

config=NULL##<<  GLOBAL config variable

) {

  reverse = (strain1=="RM" & strain2=="BY") | strain1=="YJM"

}

build_replicates = structure(function(# Stage replicates data

roi, ##<< the region that we are interested in.

only_fetch=FALSE, ##<< filter or not inputs.

get_genome=FALSE,##<< Load or not corresponding genome.

    # library(nucleominer)

    #

    # # Read config file

    # config = fromJSON(paste(readLines(json_conf_file), collapse=""))

    # # Read sample file

    # all_samples = get_content(config$CSV_SAMPLE_FILE, "cvs", sep=";", head=TRUE, stringsAsFactors=FALSE)

        tmp_track_prev = tmp_track[-length(tmp_track)]

        tmp_track_next = tmp_track[-1]

        # tmp_track_inter = signif(tmp_track_prev - tmp_track_next) * (abs(tmp_track_prev - tmp_track_next) > 1) * 25

        }

        tmp_x_prev = tmp_x[-length(tmp_x)]

        tmp_x_next = tmp_x[-1]

        need_shift = apply(t(tmp_x_next - tmp_x_prev), 2, function(delta){ delta < 50})

        points(tmp_x, tmp_y, cex=4, pch=16, col="white")

        points(tmp_x, tmp_y, cex=4, lwd=2)

        text(tmp_x, tmp_y, 1:nrow(tf_nucs))

          pos = 2

        } else {

        }

        col_llr = sapply(tmp_llr_inter, function(llr){if (llr < 20 ) return("green") else return("red")})

        text(tmp_x_inter, tmp_y_inter, tmp_llr_inter, cex=1.5, pos=pos, col=col_llr)