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]
