brain: add python and R code to local repository.

1 files changed, 205 insertions, 0 deletions

diff --git a/Code/test_network4.py b/Code/test_network4.py
new file mode 100644
index 0000000..44ce492
--- /dev/null
+++ b/Code/test_network4.py
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+# Make tissue specific networks
+
+import os, sys
+from geneid2name import make_gene_name_AGI_map_dict
+
+def get_tfs(fname_lst):
+    d = {}    
+    for fname in fname_lst:
+        f = open(fname)
+        for line in f:
+            line = line.strip()
+            lst = line.split('\t')
+            tf = lst[1].split()[0]
+            if not tf in d:
+                d[tf] = 1
+            else:
+                d[tf] += 1
+        f.close()
+    return d
+
+def get_tissue_from_fname(fname):
+    tissue_lst = [
+        'seedling',
+        'meristem',
+        'flower',
+        'aerial',
+        'shoot',
+        'seed',
+        'leaf',
+        'root',
+        'stem']
+    for x in tissue_lst:
+        if x in fname:
+            return x
+    return 'unknown'
+
+def get_edges_consisting_of_tfs(fname_lst, tf_dict):
+    d = {}
+    for fname in fname_lst:
+        kt = get_tissue_from_fname(fname)
+        d[kt] = {}
+        f = open(fname)
+        for line in f:
+            line = line.strip()
+            lst = line.split('\t')
+            target = lst[0].split()[0].strip()
+            tf     = lst[1].split()[0].strip()
+            k = target + '_' + tf
+            score = float(lst[2])
+            if tf in tf_dict and target in tf_dict:
+                if not k in d[kt]:
+                    d[kt][k] = [(lst[0], lst[1], score)]
+                else:
+                    d[kt][k].append((lst[0], lst[1], score))
+        f.close()
+    return d
+
+def get_degree(fname_lst, tf_dict):
+    d_out = {}
+    d_in  = {}
+    d_all = {}
+    for fname in fname_lst:
+        kt = get_tissue_from_fname(fname)
+        d_out[kt] = {}
+        d_in[kt]  = {}
+        d_all[kt] = {}
+        f = open(fname)
+        for line in f:
+            line = line.strip()
+            lst = line.split('\t')
+            target = lst[0].split()[0].strip()
+            tf     = lst[1].split()[0].strip()
+            if True or tf in tf_dict and target in tf_dict:
+                if not tf in d_out[kt]:
+                    d_out[kt][tf] = 1
+                else:
+                    d_out[kt][tf] += 1
+                    
+                if not target in d_in[kt]:
+                    d_in[kt][target] = 1
+                else:
+                    d_in[kt][target] += 1
+                    
+                if not target in d_all[kt]:
+                    d_all[kt][target] = 1
+                else:
+                    d_all[kt][target] += 1
+                    
+                if not tf in d_all[kt]:
+                    d_all[kt][tf] = 1
+                else:
+                    d_all[kt][tf] += 1
+                
+        f.close()
+    return d_all, d_out, d_in
+    
+
+def simplify(s):
+    result = ''
+    lst = s.split('\t')
+    a = (lst[0].split()[1]).split(';')[0]
+    if a == '.':
+        a = lst[0].split()[0]
+    else:
+        a = lst[0].split()[0] + '_' + (lst[0].split()[1]).split(';')[0]
+    b = (lst[1].split()[1]).split(';')[0]
+    if b == '.':
+        b = lst[1].split()[0]
+    else:
+        b = lst[1].split()[0] + '_' + (lst[1].split()[1]).split(';')[0]
+    return '%s\t%s\t%s' % (a, b, lst[2])
+    
+# main
+GENE_ID_TO_GENE_NAME    = '../Data/information/AGI-to-gene-names_v2.txt'
+agi2name_dict = make_gene_name_AGI_map_dict(GENE_ID_TO_GENE_NAME)
+
+edge_file_lst = [
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.seedling.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.meristem.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.flower.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.aerial.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.shoot.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.seed.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.leaf.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.root.txt.20170629_203729',
+    '/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.stem.txt.20170629_203729'
+]
+
+tf_dict = get_tfs(edge_file_lst)
+
+f = open('result.skeleton.txt', 'w')
+print('Total number of TFs: %d' % (len(tf_dict)))
+d0 = get_edges_consisting_of_tfs(edge_file_lst, tf_dict)
+for kt in d0:  # kt is tissue
+    f.write('##TF skeleton size in %s: %d.\n' % (kt, len(d0[kt])))
+    d = d0[kt]
+    for k in d:
+        lst =  d[k]
+        for x in lst: # {'shoot':{'target_tf':[], }, 'flower':{} }
+            max_score = -9
+            s = ''
+            if abs(x[2]) > max_score:
+                s =  '%s\t%s\t%4.2f' % (x[0], x[1], x[2])
+                max_score = x[2]
+        f.write(simplify(s) + '\n')
+f.close()
+
+# for each TF, get its out-degree and in-degree in each tissue
+dd_all, dd_out, dd_in = get_degree(edge_file_lst, tf_dict)
+f = open('result.out.txt', 'w')
+head_lst = ['TF']
+for k in dd_out:
+    head_lst.append(k)
+f.write('%s\n' %('\t'.join(head_lst)))
+for tf in tf_dict:
+    s = tf
+    name = '.'
+    if tf in agi2name_dict and agi2name_dict[tf] != tf:
+        name = agi2name_dict[tf]
+    s += ' ' + name    
+    for k in dd_out:
+        if tf in dd_out[k]:
+            s += '\t%d' % (dd_out[k][tf])
+        else:
+            s += '\t0'
+    f.write(s + '\n')
+f.close()
+
+f = open('result.in.txt', 'w')
+head_lst = ['TF']
+for k in dd_in:
+    head_lst.append(k)
+f.write('%s\n' %('\t'.join(head_lst)))
+for tf in tf_dict:
+    s = tf
+    name = '.'
+    if tf in agi2name_dict and agi2name_dict[tf] != tf:
+        name = agi2name_dict[tf]
+    s += ' ' + name
+    for k in dd_in:
+        if tf in dd_in[k]:
+            s += '\t%d' % (dd_in[k][tf])
+        else:
+            s += '\t0'
+    f.write(s + '\n')
+f.close()
+
+f = open('result.all.txt', 'w')
+head_lst = ['TF']
+for k in dd_all:
+    head_lst.append(k)
+f.write('%s\n' %('\t'.join(head_lst)))
+for tf in tf_dict:
+    s = tf
+    name = '.'
+    if tf in agi2name_dict and agi2name_dict[tf] != tf:
+        name = agi2name_dict[tf]
+    s += ' ' + name    
+    for k in dd_all:
+        if tf in dd_all[k]:
+            s += '\t%d' % (dd_all[k][tf])
+        else:
+            s += '\t0'
+    f.write(s + '\n')
+f.close()