dirpath = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/ThioMacs/Analysis_2013_02/' dirpath_bmdc = 'karmel/Desktop/Projects/GlassLab/Notes_and_Reports/NOD_BALBc/BMDCs/Analysis_2013_03/' dirpath = yzer.get_path(dirpath) dirpath_bmdc = yzer.get_path(dirpath_bmdc) img_dirpath = yzer.get_and_create_path(dirpath, 'bmdc_vs_thiomac') thio = yzer.import_file( yzer.get_filename(dirpath, 'transcript_vectors.txt')) bmdc = yzer.import_file( yzer.get_filename(dirpath_bmdc, 'transcript_vectors.txt')) sets = [] for data in (thio, bmdc): data = data.fillna(0) refseq = yzer.get_refseq(data) # Remove low tag counts #refseq = refseq[refseq['transcript_score'] >= 4] sets.append(refseq) if True: genes = ['Coro1a', 'Vcl', 'Tlr2', 'Clec4e', 'Cxcl2'] vals = [] labels = [] for gene in genes: vals.append(sets[0][sets[0]['gene_names'] == gene] ['balb_nod_notx_1h_fc'].values[0])
data = grapher.normalize(data, 'nod_notx_0h_tag_count', 2.790489) data = grapher.normalize(data, 'diabetic_nod_notx_0h_tag_count', 1.083990) data = grapher.normalize(data, 'slow_diabetic_nod_notx_0h_tag_count', 0.349747) # Vs nod notx data = grapher.normalize(data, 'diabetic_nod_notx_0h_tag_count', 0.483232, suffix='_norm_2') data = grapher.normalize(data, 'slow_diabetic_nod_notx_0h_tag_count', 0.276080, suffix='_norm_2') refseq = grapher.get_refseq(data) xcolname = 'balb_notx_0h_tag_count' ycolname = 'nod_notx_0h_tag_count_norm' # Remove low tag counts refseq = refseq[(refseq[xcolname] > 20) | (refseq[ycolname] > 20)] refseq = refseq[refseq['transcript_score'] > 15] # Remove the strangely large last entry #refseq = refseq[refseq[xcolname] < max(refseq[xcolname])] refseq_up_nond = refseq[refseq['balb_nod_notx_0h_fc'] >= 1] refseq_down_nond = refseq[refseq['balb_nod_notx_0h_fc'] <= -1] refseq_up_d = refseq[refseq['diabetic_balb_nod_notx_0h_fc'] >= 1]