Révision d973538c
| b/doc/Chuffart_NM2_sweave/Chuffart_NM2_sweave.Rnw | ||
|---|---|---|
| 41 | 41 |
config = fromJSON(paste(readLines(json_conf_file), collapse="")) |
| 42 | 42 |
|
| 43 | 43 |
# Read sample file |
| 44 |
all_samples = get_content(config$CSV_SAMPLE_FILE, "cvs", sep=";", head=TRUE, stringsAsFactors=FALSE)
|
|
| 44 |
all_samples = get_content(config$CSV_SAMPLE_FILE, "csv", sep=";", head=TRUE, stringsAsFactors=FALSE)
|
|
| 45 | 45 |
|
| 46 | 46 |
# Remove samples that seems to be eronous for some manipulation efficency reasons e.g. PCR |
| 47 | 47 |
all_samples = all_samples[!(all_samples$id %in% c(33,45,48,55)), ] |
| ... | ... | |
| 705 | 705 |
print(nb_iso/sum(check_iso_snep$snep_index)) |
| 706 | 706 |
|
| 707 | 707 |
|
| 708 |
wpunr_mnase = read.table( paste(config$RESULTS_DIR, "/full/" ,combi[1],"_",combi[2],"_wp_mnase.tab",sep=""), header=TRUE) |
|
| 708 |
wpunr_mnase = read.table( paste(config$RESULTS_DIR, "/full/" ,combi[1],"_",combi[2],"_wpunr_mnase.tab",sep=""), header=TRUE)
|
|
| 709 | 709 |
head(wpunr_mnase) |
| 710 | 710 |
|
| 711 | 711 |
sf = read.table(paste(config$RESULTS_DIR,"/full/size_factors.tab",sep=""), header=TRUE) |
| b/doc/Chuffart_NM2_workdir/src/current/configurator.py | ||
|---|---|---|
| 3 | 3 |
import json |
| 4 | 4 |
|
| 5 | 5 |
CSV_SAMPLE_FILE = None |
| 6 |
"""Path to cvs file that contains sample information."""
|
|
| 6 |
"""Path to csv file that contains sample information."""
|
|
| 7 | 7 |
if __name__ == "__main__": |
| 8 | 8 |
CSV_SAMPLE_FILE = "data/samples.csv" |
| 9 | 9 |
|
| ... | ... | |
| 98 | 98 |
] |
| 99 | 99 |
} |
| 100 | 100 |
|
| 101 |
NEUTRAL_TRANSLATE_CUR = None |
|
| 102 |
"""Desactivate the genome translation facilities""" |
|
| 103 |
if __name__ == "__main__": |
|
| 104 |
NEUTRAL_TRANSLATE_CUR = False |
|
| 105 |
|
|
| 106 |
|
|
| 101 | 107 |
READ_LENGTH = None |
| 102 | 108 |
"""Length of Illumina reads.""" |
| 103 | 109 |
if __name__ == "__main__": |
| b/doc/Chuffart_NM2_workdir/src/current/extract_maps.R | ||
|---|---|---|
| 46 | 46 |
} |
| 47 | 47 |
|
| 48 | 48 |
print("Running bot engine...")
|
| 49 |
run_engine(tasks, extract_maps, debug = substr(Sys.info()[["nodename"]],1,7) == "stainer", rm_starter = TRUE, log_dir = "log", nb_proc=NULL)
|
|
| 49 |
run_engine(tasks, extract_maps, debug = substr(Sys.info()[["nodename"]],1,7) == "stainer", rm_starter = FALSE, log_dir = "log", nb_proc=NULL)
|
|
| 50 | 50 |
|
| 51 | 51 |
|
| 52 | 52 |
|
| ... | ... | |
| 107 | 107 |
for (strain in strains) {
|
| 108 | 108 |
print(paste("Collecting mpas for", strain, "..."))
|
| 109 | 109 |
partial_strain_maps = aggregated_intra_strain_nucs[[strain]][[1]] |
| 110 |
tmp_nuc_map = flat_aggregated_intra_strain_nucs(partial_strain_maps, cur_index) |
|
| 110 |
print(strain) |
|
| 111 |
nb_tracks = sum(all_samples$strain == strain & all_samples$marker == "Mnase_Seq") |
|
| 112 |
|
|
| 113 |
tmp_nuc_map = flat_aggregated_intra_strain_nucs(partial_strain_maps, cur_index, nb_tracks) |
|
| 111 | 114 |
|
| 112 | 115 |
wp_indexes = which(tmp_nuc_map$wp == 1) |
| 113 | 116 |
tmp_wp_llr = apply(t(wp_indexes), 2, function(wp_index){
|
| 114 |
tmp_wp = partial_strain_maps[[wp_index]] |
|
| 115 |
res = llr_score_nvecs(list(tmp_wp$nucs[[1]]$original_reads, tmp_wp$nucs[[2]]$original_reads, tmp_wp$nucs[[3]]$original_reads)) |
|
| 117 |
tmp_wp = partial_strain_maps[[wp_index]] |
|
| 118 |
l = lapply(tmp_wp$nucs, function(tmp_wp_nucs){
|
|
| 119 |
tmp_wp_nucs$original_reads |
|
| 120 |
}) |
|
| 121 |
res = llr_score_nvecs(l) |
|
| 116 | 122 |
return(res) |
| 117 | 123 |
}) |
| 118 | 124 |
|
| 119 | 125 |
tmp_dyad_shift = apply(t(wp_indexes), 2, function(wp_index){
|
| 120 | 126 |
tmp_wp = partial_strain_maps[[wp_index]] |
| 121 |
ds = max(c(tmp_wp$nucs[[1]]$center, tmp_wp$nucs[[2]]$center, tmp_wp$nucs[[3]]$center)) - min(c(tmp_wp$nucs[[1]]$center, tmp_wp$nucs[[2]]$center, tmp_wp$nucs[[3]]$center)) |
|
| 127 |
c = sapply(tmp_wp$nucs, function(tmp_wp_nucs){
|
|
| 128 |
tmp_wp_nucs$center |
|
| 129 |
}) |
|
| 130 |
ds = max(c) - min(c) |
|
| 122 | 131 |
return(ds) |
| 123 | 132 |
}) |
| 124 | 133 |
|
| b/doc/Chuffart_NM2_workdir/src/current/headers.R | ||
|---|---|---|
| 5 | 5 |
json_conf_file = "src/current/nucleominer_config.json" |
| 6 | 6 |
config = fromJSON(paste(readLines(json_conf_file), collapse="")) |
| 7 | 7 |
|
| 8 |
all_samples = get_content(config$CSV_SAMPLE_FILE, "cvs", sep=";", head=TRUE, stringsAsFactors=FALSE)
|
|
| 8 |
all_samples = get_content(config$CSV_SAMPLE_FILE, "csv", sep=";", head=TRUE, stringsAsFactors=FALSE)
|
|
| 9 | 9 |
all_samples = all_samples[!(all_samples$id %in% c(33,45,48,55)), ] |
| 10 | 10 |
|
| 11 | 11 |
markers = unique(all_samples$marker) |
| b/doc/sphinx_doc/Makefile | ||
|---|---|---|
| 112 | 112 |
|
| 113 | 113 |
text: |
| 114 | 114 |
$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text |
| 115 |
cat build/text/index.txt build/text/readme.txt build/text/tuto.txt ../../README |
|
| 115 |
cat build/text/index.txt build/text/readme.txt build/text/tuto.txt > ../../README
|
|
| 116 | 116 |
@echo |
| 117 | 117 |
@echo "Build finished. The text files are in $(BUILDDIR)/text." |
| 118 | 118 |
|
| b/doc/sphinx_doc/conf.py | ||
|---|---|---|
| 50 | 50 |
# built documents. |
| 51 | 51 |
# |
| 52 | 52 |
# The short X.Y version. |
| 53 |
version = '2.3.46'
|
|
| 53 |
version = '2.3.47'
|
|
| 54 | 54 |
# The full version, including alpha/beta/rc tags. |
| 55 |
release = '2.3.46'
|
|
| 55 |
release = '2.3.47'
|
|
| 56 | 56 |
|
| 57 | 57 |
# The language for content autogenerated by Sphinx. Refer to documentation |
| 58 | 58 |
# for a list of supported languages. |
| b/doc/sphinx_doc/rref.rst | ||
|---|---|---|
| 690 | 690 |
:: |
| 691 | 691 |
|
| 692 | 692 |
filter_tf_inputs(inputs, chr, x_min, x_max, nuc_width = 160, |
| 693 |
only_f = FALSE, only_r = FALSE, filter_for_coverage = FALSE) |
|
| 693 |
only_f = FALSE, only_r = FALSE, filter_for_coverage = FALSE, |
|
| 694 |
USE_DPLYR = TRUE) |
|
| 694 | 695 |
|
| 695 | 696 |
Arguments |
| 696 | 697 |
~~~~~~~~~ |
| ... | ... | |
| 727 | 728 |
|
| 728 | 729 |
Does it filter for plot coverage? |
| 729 | 730 |
|
| 731 |
``USE_DPLYR`` |
|
| 732 |
|
|
| 733 |
Use dplyr lib to filter reads. |
|
| 734 |
|
|
| 730 | 735 |
Value |
| 731 | 736 |
~~~~~ |
| 732 | 737 |
|
| ... | ... | |
| 814 | 819 |
|
| 815 | 820 |
:: |
| 816 | 821 |
|
| 817 |
flat_aggregated_intra_strain_nucs(partial_strain_maps, cur_index) |
|
| 822 |
flat_aggregated_intra_strain_nucs(partial_strain_maps, cur_index, |
|
| 823 |
nb_tracks = 3) |
|
| 818 | 824 |
|
| 819 | 825 |
Arguments |
| 820 | 826 |
~~~~~~~~~ |
| ... | ... | |
| 827 | 833 |
|
| 828 | 834 |
the index of the roi involved |
| 829 | 835 |
|
| 836 |
``nb_tracks`` |
|
| 837 |
|
|
| 838 |
the number of replicates |
|
| 839 |
|
|
| 830 | 840 |
Value |
| 831 | 841 |
~~~~~ |
| 832 | 842 |
|
| ... | ... | |
| 1251 | 1261 |
+---------------+---------------------------------------------------+ |
| 1252 | 1262 |
| Author: | Florent Chuffart | |
| 1253 | 1263 |
+---------------+---------------------------------------------------+ |
| 1254 |
| Version: | 2.3.46 |
|
|
| 1264 |
| Version: | 2.3.47 |
|
|
| 1255 | 1265 |
+---------------+---------------------------------------------------+ |
| 1256 | 1266 |
| License: | CeCILL | |
| 1257 | 1267 |
+---------------+---------------------------------------------------+ |
| 1258 | 1268 |
| Title: | nm | |
| 1259 | 1269 |
+---------------+---------------------------------------------------+ |
| 1260 |
| Depends: | seqinr, plotrix, DESeq, cachecache |
|
|
| 1270 |
| Depends: | seqinr, plotrix, DESeq, cachecache, dplyr |
|
|
| 1261 | 1271 |
+---------------+---------------------------------------------------+ |
| 1262 | 1272 |
|
| 1263 | 1273 |
Author(s) |
| b/src/DESCRIPTION | ||
|---|---|---|
| 1 | 1 |
Package: nucleominer |
| 2 | 2 |
Maintainer: Florent Chuffart <florent.chuffart@ens-lyon.fr> |
| 3 | 3 |
Author: Florent Chuffart |
| 4 |
Version: 2.3.46
|
|
| 4 |
Version: 2.3.47
|
|
| 5 | 5 |
License: CeCILL |
| 6 | 6 |
Title: nm |
| 7 |
Depends: seqinr, plotrix, DESeq, cachecache |
|
| 7 |
Depends: seqinr, plotrix, DESeq, cachecache, dplyr
|
|
| 8 | 8 |
Description: It provides a set of useful functions allowing to perform quantitative analysis of nucleosomal epigenome. |
| b/src/R/nucleominer.R | ||
|---|---|---|
| 153 | 153 |
nuc_width = 160, ##<< Nucleosome width. |
| 154 | 154 |
only_f = FALSE, ##<< Filter only F reads. |
| 155 | 155 |
only_r = FALSE, ##<< Filter only R reads. |
| 156 |
filter_for_coverage = FALSE ##<< Does it filter for plot coverage? |
|
| 156 |
filter_for_coverage = FALSE, ##<< Does it filter for plot coverage? |
|
| 157 |
USE_DPLYR = TRUE ##<< Use dplyr lib to filter reads. |
|
| 157 | 158 |
) {
|
| 158 |
if (only_f) {
|
|
| 159 |
inputs = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,3] == "F" & inputs[,2] <= x_max + nuc_width,] |
|
| 160 |
} else if (only_r) {
|
|
| 161 |
inputs = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,3] == "R" & inputs[,2] <= x_max + nuc_width,] |
|
| 162 |
} else {
|
|
| 163 |
inputs = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,2] <= x_max + nuc_width,] |
|
| 164 |
} |
|
| 159 |
n = names(inputs) |
|
| 160 |
|
|
| 161 |
if (!USE_DPLYR) {
|
|
| 162 |
if (only_f) {
|
|
| 163 |
inputs_out = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,3] == "F" & inputs[,2] <= x_max + nuc_width,] |
|
| 164 |
} else if (only_r) {
|
|
| 165 |
inputs_out = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,3] == "R" & inputs[,2] <= x_max + nuc_width,] |
|
| 166 |
} else {
|
|
| 167 |
inputs_out = inputs[inputs[,1]==chr & inputs[,2] >= x_min - nuc_width & inputs[,2] <= x_max + nuc_width,] |
|
| 168 |
} |
|
| 169 |
} else {
|
|
| 170 |
names(inputs) = c("chr", "pos", "str", "lev")
|
|
| 171 |
if (only_f) {
|
|
| 172 |
inputs_out = filter(inputs, chr == chr, pos >= x_min - nuc_width, str == "F", pos <= x_max + nuc_width) |
|
| 173 |
} else if (only_r) {
|
|
| 174 |
inputs_out = filter(inputs, chr == chr, pos >= x_min - nuc_width, str == "R" & pos <= x_max + nuc_width) |
|
| 175 |
} else {
|
|
| 176 |
inputs_out = filter(inputs, chr == chr, pos >= x_min - nuc_width, pos <= x_max + nuc_width) |
|
| 177 |
} |
|
| 178 |
# if (!filter_for_coverage) {
|
|
| 179 |
# inputs$corrected_inputs_coords = inputs[,2] + nuc_width/2 * sign_from_strand(inputs[,3]) |
|
| 180 |
# inputs = filter(inputs, chr == chr, corrected_inputs_coords >= x_min, corrected_inputs_coords <= x_max) |
|
| 181 |
# inputs$corrected_inputs_coords = NULL |
|
| 182 |
# } |
|
| 183 |
} |
|
| 184 |
|
|
| 165 | 185 |
if (!filter_for_coverage) {
|
| 166 |
corrected_inputs_coords = inputs[,2] + nuc_width/2 * sign_from_strand(inputs[,3])
|
|
| 167 |
inputs = inputs[inputs[,1]==chr & corrected_inputs_coords >= x_min & corrected_inputs_coords <= x_max,]
|
|
| 186 |
corrected_inputs_coords = inputs_out[,2] + nuc_width/2 * sign_from_strand(inputs_out[,3])
|
|
| 187 |
inputs_out = inputs_out[inputs_out[,1]==chr & corrected_inputs_coords >= x_min & corrected_inputs_coords <= x_max,]
|
|
| 168 | 188 |
} |
| 169 |
return(inputs) |
|
| 189 |
|
|
| 190 |
names(inputs_out) = n |
|
| 191 |
return(inputs_out) |
|
| 170 | 192 |
### Returns filtred inputs. |
| 171 | 193 |
} |
| 172 | 194 |
|
| ... | ... | |
| 205 | 227 |
} |
| 206 | 228 |
return(TRUE) |
| 207 | 229 |
} |
| 208 |
store_cluster = function(clusters, new_cluster, nb_nucs_in_cluster,nuc_from_track,nb_tracks, min_nuc_center, max_nuc_center) {
|
|
| 230 |
store_cluster = function(clusters, new_cluster, nb_nucs_in_cluster, nuc_from_track, nb_tracks, min_nuc_center, max_nuc_center) {
|
|
| 209 | 231 |
if ( nb_nucs_in_cluster==nb_tracks & sum(nuc_from_track)==nb_tracks) {
|
| 210 | 232 |
new_cluster$wp = TRUE |
| 211 | 233 |
center = (new_cluster$lower_bound + new_cluster$upper_bound) / 2 |
| ... | ... | |
| 248 | 270 |
} |
| 249 | 271 |
i = i+1 |
| 250 | 272 |
} |
| 273 |
nb_tracks = length(tf_outs) |
|
| 251 | 274 |
# print(track_readers) |
| 252 | 275 |
new_cluster = NULL |
| 253 | 276 |
nb_nucs_in_cluster = 0 |
| 254 | 277 |
nuc_from_track = c() |
| 255 |
for (i in 1:length(tf_outs)){
|
|
| 278 |
for (i in 1:nb_tracks){
|
|
| 256 | 279 |
nuc_from_track[i] = FALSE |
| 257 | 280 |
} |
| 258 | 281 |
# Start clustering |
| ... | ... | |
| 297 | 320 |
} else {
|
| 298 | 321 |
if (!is.null(new_cluster)) {
|
| 299 | 322 |
# store old cluster |
| 300 |
clusters = store_cluster(clusters, new_cluster, nb_nucs_in_cluster,nuc_from_track,length(tf_outs),min_nuc_center, max_nuc_center)
|
|
| 323 |
clusters = store_cluster(clusters, new_cluster, nb_nucs_in_cluster, nuc_from_track, nb_tracks, min_nuc_center, max_nuc_center)
|
|
| 301 | 324 |
} |
| 302 | 325 |
# Reinit current cluster composition stuff |
| 303 | 326 |
nb_nucs_in_cluster = 0 |
| 304 | 327 |
nuc_from_track = c() |
| 305 |
for (i in 1:length(tf_outs)){
|
|
| 328 |
for (i in 1:nb_tracks){
|
|
| 306 | 329 |
nuc_from_track[i] = FALSE |
| 307 | 330 |
} |
| 308 | 331 |
# create new cluster |
| ... | ... | |
| 331 | 354 |
# store last cluster |
| 332 | 355 |
if (!is.null(new_cluster)) {
|
| 333 | 356 |
# store old cluster |
| 334 |
clusters = store_cluster(clusters, new_cluster, nb_nucs_in_cluster,nuc_from_track,length(tf_outs),min_nuc_center, max_nuc_center)
|
|
| 357 |
clusters = store_cluster(clusters, new_cluster, nb_nucs_in_cluster, nuc_from_track, nb_tracks, min_nuc_center, max_nuc_center)
|
|
| 335 | 358 |
} |
| 336 | 359 |
return(list(clusters, llr_scores)) |
| 337 | 360 |
### Returns a list of clusterized nucleosomes, and all computed llr scores. |
| ... | ... | |
| 364 | 387 |
flat_aggregated_intra_strain_nucs = function(# to flat aggregate_intra_strain_nucs function output |
| 365 | 388 |
### This function builds a dataframe of all clusters obtain from aggregate_intra_strain_nucs function. |
| 366 | 389 |
partial_strain_maps, ##<< the output of aggregate_intra_strain_nucs function |
| 367 |
cur_index ##<< the index of the roi involved |
|
| 390 |
cur_index, ##<< the index of the roi involved |
|
| 391 |
nb_tracks=3 ##<< the number of replicates |
|
| 368 | 392 |
) {
|
| 369 | 393 |
if (length(partial_strain_maps) == 0 ){
|
| 370 | 394 |
print(paste("Empty partial_strain_maps for roi", cur_index, "ands current strain." ))
|
| ... | ... | |
| 386 | 410 |
tmp_nuc_as_list[["nb_reads"]] = length(all_original_reads) |
| 387 | 411 |
tmp_nuc_as_list[["nb_nucs"]] = length(tmp_nuc$nucs) |
| 388 | 412 |
if (tmp_nuc$wp) {
|
| 389 |
tmp_nuc_as_list[["llr_1"]] = signif(tmp_nuc$nucs[[2]]$llr_score,5) |
|
| 390 |
tmp_nuc_as_list[["llr_2"]] = signif(tmp_nuc$nucs[[3]]$llr_score,5) |
|
| 413 |
for (i in 1:(nb_tracks-1)) {
|
|
| 414 |
tmp_nuc_as_list[[paste("llr", i, sep="_")]] = signif(tmp_nuc$nucs[[i + 1]]$llr_score,5)
|
|
| 415 |
} |
|
| 391 | 416 |
} else {
|
| 392 |
tmp_nuc_as_list[["llr_1"]] = NA |
|
| 393 |
tmp_nuc_as_list[["llr_2"]] = NA |
|
| 417 |
for (i in 1:(nb_tracks-1)) {
|
|
| 418 |
tmp_nuc_as_list[[paste("llr", i, sep="_")]] = NA
|
|
| 419 |
} |
|
| 394 | 420 |
} |
| 395 | 421 |
return(tmp_nuc_as_list) |
| 396 | 422 |
}) |
| ... | ... | |
| 1141 | 1167 |
config=NULL, ##<< GLOBAL config variable |
| 1142 | 1168 |
big_cur=NULL ##<< A largest region than roi use to filter c2c if it is needed. |
| 1143 | 1169 |
) {
|
| 1144 |
strain1 = roi$strain_ref |
|
| 1145 |
if (strain1 == strain2) {
|
|
| 1146 |
roi$length = roi$end - roi$begin + sign(roi$end - roi$begin) * 1 |
|
| 1170 |
strain1 = roi$strain_ref |
|
| 1171 |
# Do something or nothing? |
|
| 1172 |
if (is.null(config$NEUTRAL_TRANSLATE_CUR)) {
|
|
| 1173 |
config$NEUTRAL_TRANSLATE_CUR = FALSE |
|
| 1174 |
} |
|
| 1175 |
if (strain1 == strain2 | config$NEUTRAL_TRANSLATE_CUR) {
|
|
| 1176 |
roi$strain_ref = strain2 |
|
| 1177 |
roi$length = roi$end - roi$begin + sign(roi$end - roi$begin) |
|
| 1147 | 1178 |
return(roi) |
| 1148 | 1179 |
} |
| 1149 |
|
|
| 1180 |
|
|
| 1150 | 1181 |
# Extract c2c file |
| 1151 | 1182 |
if (!is.null(big_cur)) {
|
| 1152 | 1183 |
# Dealing with big_cur |
| ... | ... | |
| 1964 | 1995 |
if (plot_wp_nucs | plot_fuzzy_nucs | plot_common_nucs ) {
|
| 1965 | 1996 |
replicates_wp_nucs[[replicate_rank]] = wp_nucs |
| 1966 | 1997 |
strain = samples[[1]]$strain |
| 1967 |
wp_maps[[strain]] = flat_aggregated_intra_strain_nucs(wp_nucs, "foo") |
|
| 1998 |
wp_maps[[strain]] = flat_aggregated_intra_strain_nucs(wp_nucs, "foo", nb_tracks)
|
|
| 1968 | 1999 |
fuzzy_maps[[strain]] = get_intra_strain_fuzzy(wp_maps[[strain]], as.list(samples[[1]]$roi), samples[[1]]$strain, config=config) |
| 1969 | 2000 |
|
| 1970 | 2001 |
if (plot_fuzzy_nucs) {
|
Formats disponibles : Unified diff