Révision ec2936ea

b/doc/sphinx_doc/conf.py
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# built documents.
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#
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# The short X.Y version.
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version = '2.3.42'
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version = '2.3.43'
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# The full version, including alpha/beta/rc tags.
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release = '2.3.42'
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release = '2.3.43'
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# The language for content autogenerated by Sphinx. Refer to documentation
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# for a list of supported languages.
b/doc/sphinx_doc/rref.rst
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~~~~~~~~~~~
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This function filters TemplateFilter outputs according, not only genome
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area observerved properties, but also correlation and overlap threshold.
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area observerved properties, but also correlation and overlapping
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threshold.
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Usage
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~~~~~
......
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+---------------+---------------------------------------------------+
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| Author:       | Florent Chuffart                                  |
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+---------------+---------------------------------------------------+
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| Version:      | 2.3.42                                            |
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| Version:      | 2.3.43                                            |
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+---------------+---------------------------------------------------+
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| License:      | CeCILL                                            |
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+---------------+---------------------------------------------------+
b/src/DESCRIPTION
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Package: nucleominer
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Maintainer: Florent Chuffart <florent.chuffart@ens-lyon.fr>
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Author: Florent Chuffart
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Version: 2.3.42
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Version: 2.3.43
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License: CeCILL 
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Title: nm
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Depends: seqinr, plotrix, DESeq, cachecache
b/src/R/nucleominer.R
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filter_tf_outputs = function(# Filter TemplateFilter outputs
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### This function filters TemplateFilter outputs according, not only genome area observerved properties, but also correlation and overlap threshold.
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### This function filters TemplateFilter outputs according, not only genome area observerved properties, but also correlation and overlapping threshold.
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tf_outputs, ##<< TemplateFilter outputs.
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chr, ##<< Chromosome observed, here chr is an integer.
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x_min, ##<< Coordinate of the first bp observed.
......
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  tf_outs = list()
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	i = 1
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  for (sample in samples) {
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		# print(sample$roi$chr)
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		# print(min_nuc_center)
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		# print(max_nuc_center)
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		# print(sample$outputs)
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		# tf_outs[[i]] = filter_tf_outputs(sample$outputs, sample$roi$chr, min_nuc_center, max_nuc_center)
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		# print(tf_outs[[i]])
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		tf_outs[[i]] = sample$outputs
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		tf_outs[[i]] = tf_outs[[i]][order(tf_outs[[i]]$center),]
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    indexes[i] = 1
......
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								llr_score = llr_score_nvecs(list(reads_strain_ref1, reads_strain_ref2))
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								llr_scores = c(llr_scores, llr_score)
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								# Filtering on LOD Score
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								if (llr_score < llr_thres) {
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                # if (llr_score < llr_thres) {
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								if (TRUE) {
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									tmp_nuc = list()
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									# strain_ref1
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									tmp_nuc[[paste("chr_", strain_ref1, sep="")]] = chr
......
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									tmp_nuc[[paste("sd_", strain_ref1, sep="")]] = signif(sd(reads_strain_ref1),5)
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									tmp_nuc[[paste("nb_reads_", strain_ref1, sep="")]] = length(reads_strain_ref1)
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									tmp_nuc[[paste("index_nuc_", strain_ref1, sep="")]] = index_nuc_strain_ref1
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									# tmp_nuc[[paste("corr1_", strain_ref1, sep="")]] = signif(nuc_strain_ref1$nucs[[1]]$corr,5)
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									# tmp_nuc[[paste("corr2_", strain_ref1, sep="")]] = signif(nuc_strain_ref1$nucs[[2]]$corr,5)
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									# tmp_nuc[[paste("corr3_", strain_ref1, sep="")]] = signif(nuc_strain_ref1$nucs[[3]]$corr,5)
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									# strain_ref2
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									tmp_nuc[[paste("chr_", strain_ref2, sep="")]] = roi_strain_ref2$chr
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									tmp_nuc[[paste("lower_bound_", strain_ref2, sep="")]] = nuc_strain_ref2$lower_bound
......
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									tmp_nuc[[paste("sd_", strain_ref2, sep="")]] = signif(sd(reads_strain_ref2),5)
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									tmp_nuc[[paste("nb_reads_", strain_ref2, sep="")]] = length(reads_strain_ref2)
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									tmp_nuc[[paste("index_nuc_", strain_ref2, sep="")]] = index_nuc_strain_ref2
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									# tmp_nuc[[paste("corr1_", strain_ref2, sep="")]] = signif(nuc_strain_ref2$nucs[[1]]$corr,5)
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									# tmp_nuc[[paste("corr2_", strain_ref2, sep="")]] = signif(nuc_strain_ref2$nucs[[2]]$corr,5)
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									# tmp_nuc[[paste("corr3_", strain_ref2, sep="")]] = signif(nuc_strain_ref2$nucs[[3]]$corr,5)
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									# common
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									tmp_nuc[["llr_score"]] = signif(llr_score,5)
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									# print(tmp_nuc)
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									common_nuc = dfadd(common_nuc, tmp_nuc)
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								}
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                }
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							}
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						}
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					}
......
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        new_tmp_y[index_odd] = tmp_y
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        new_tmp_x[index_even] = tmp_x_inter
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        new_tmp_y[index_even] = tmp_y_inter
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        lines(new_tmp_x , new_tmp_y, lw=2)
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        lines(new_tmp_x , new_tmp_y, lwd=2)
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        points(tmp_x, tmp_y, cex=4, pch=16, col="white")
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        points(tmp_x, tmp_y, cex=4, lw=2)
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        points(tmp_x, tmp_y, cex=4, lwd=2)
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        text(tmp_x, tmp_y, 1:nrow(tf_nucs))
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        if (is.null(config$LEGEND_LOD_POS)) {
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          pos = 2
......
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      common_nuc_results = list()
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      common_nuc_results[[paste(combi[1], combi[2], sep="_")]] = aligned_inter_strain_nucs
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      unrs = get_unrs(combi, roi, cur_index, wp_maps, fuzzy_maps, common_nuc_results, config = config) 
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      rect(sign(x_min[[1]]) * unrs$lower_bound + shift[[1]], y_min[[1]], sign(x_min[[1]]) * unrs$upper_bound + shift[[1]], y_max[[2]], border=4, lw=10, col=adjustcolor(4, alpha.f = 0.05))        
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      rect(sign(x_min[[1]]) * unrs$lower_bound + shift[[1]], y_min[[1]], sign(x_min[[1]]) * unrs$upper_bound + shift[[1]], y_max[[2]], border=4, lwd=10, col=adjustcolor(4, alpha.f = 0.05))        
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    }
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	}

Formats disponibles : Unified diff