def read_V1_data(data_file, learn_options):
if data_file is None:
data_file = "../data/07-23 Data for Jennifer.xlsx"
human_data = pandas.read_excel(data_file, sheetname=0, index_col=[0, 1])
mouse_data = pandas.read_excel(data_file, sheetname=1, index_col=[0, 1])
Xdf, Y = combine_organisms(human_data, mouse_data)
# get position within each gene, then join and re-order
# note that 11 missing guides we were told to ignore
annotations = pandas.read_csv(r"..\data\AML_EL4_PercentRank_0725.txt", delimiter='\t', index_col=[0, 4])
annotations.index.names = Xdf.index.names
gene_position = pandas.merge(Xdf, annotations, how="inner", left_index=True, right_index=True)
gene_position = util.impute_gene_position(gene_position)
gene_position = gene_position[['Amino Acid Cut position', 'Nucleotide cut position', 'Percent Peptide']]
Y = Y.loc[gene_position.index]
Xdf = Xdf.loc[gene_position.index]
Y['test'] = 1 # for bookeeping to keep consistent with V2 which uses this for "extra pairs"
target_genes = Y['Target gene'].unique()
Y.index.names = ['Sequence', 'Target gene']
assert Xdf.index.equals(Y.index), "The index of Xdf is different from the index of Y (this can cause inconsistencies/random performance later on)"
if learn_options is not None and learn_options["flipV1target"]:
print "************************************************************************"
print "*****************MATCHING DOENCH CODE (DEBUG MODE)**********************"
print "************************************************************************"
# normally it is: Y['average threshold'] = Y['average rank'] > 0.8, where 1s are good guides, 0s are not
Y['average threshold'] = Y['average rank'] < 0.2 # 1s are bad guides
print "press c to continue"
import ipdb
ipdb.set_trace()
return annotations, gene_position, target_genes, Xdf, Y
def read_V2_data(data_file, learn_options=None, verbose=True):
if data_file is None:
data_file = "../data/11-15-2014 DeepXPR results_processed.xlsx"
# to compare
# import predict as pr; a1, g1, t1, X1, Y1 = pr.data_setup()
# a1.index.names
data = pandas.read_excel(data_file, sheetname="ResultsFiltered", skiprows=range(0, 6+1), index_col=[0, 4])
# grab data relevant to each of three drugs, which exludes some genes
# note gene MED12 has two drugs, all others have at most one
Xdf = pandas.DataFrame()
# This comes from the "Pairs" tab in their excel sheet,
# note HPRT/HPRT1 are same thing, and also PLX_2uM/PLcX_2uM
known_pairs = {'AZD_200nM': ['CCDC101', 'MED12', 'TADA2B', 'TADA1'],
'6TG_2ug/mL': ['HPRT1'],
'PLX_2uM': ['CUL3', 'NF1', 'NF2', 'MED12']}
drugs_to_genes = {'AZD_200nM': ['CCDC101', 'MED12', 'TADA2B', 'TADA1'],
'6TG_2ug/mL': ['HPRT1'],
'PLX_2uM': ['CUL3', 'NF1', 'NF2', 'MED12']}
if learn_options is not None:
assert not (learn_options['extra pairs'] and learn_options['all pairs']), "extra pairs and all pairs options (in learn_options) can't be active simultaneously."
if learn_options['extra pairs']:
drugs_to_genes['AZD_200nM'].extend(['CUL3', 'NF1', 'NF2'])
elif learn_options['all pairs']:
drugs_to_genes['AZD_200nM'].extend(['HPRT1', 'CUL3', 'NF1', 'NF2'])
drugs_to_genes['PLX_2uM'].extend(['HPRT1', 'CCDC101', 'TADA2B', 'TADA1'])
drugs_to_genes['6TG_2ug/mL'].extend(['CCDC101', 'MED12', 'TADA2B', 'TADA1', 'CUL3', 'NF1', 'NF2'])
count = 0
for drug in drugs_to_genes.keys():
genes = drugs_to_genes[drug]
for g in genes:
Xtmp = data.copy().xs(g, level='Target gene', drop_level=False)
Xtmp['drug'] = drug
Xtmp['score'] = Xtmp[drug].copy() # grab the drug results that are relevant for this gene
if g in known_pairs[drug]:
Xtmp['test'] = 1.
else:
Xtmp['test'] = 0.
count = count + Xtmp.shape[0]
Xdf = pandas.concat([Xdf, Xtmp], axis=0)
if verbose:
print "Loaded %d samples for gene %s \ttotal number of samples: %d" % (Xtmp.shape[0], g, count)
# create new index that includes the drug
Xdf = Xdf.set_index('drug', append=True)
Y = pandas.DataFrame(Xdf.pop("score"))
Y.columns.names = ["score"]
test_gene = pandas.DataFrame(Xdf.pop('test'))
target = pandas.DataFrame(Xdf.index.get_level_values('Target gene').values, index=Y.index, columns=["Target gene"])
Y = pandas.concat((Y, target, test_gene), axis=1)
target_genes = Y['Target gene'].unique()
gene_position = Xdf[["Percent Peptide", "Amino Acid Cut position"]].copy()
# convert to ranks for each (gene, drug combo)
# flip = True
y_rank = pandas.DataFrame()
y_threshold = pandas.DataFrame()
y_quant = pandas.DataFrame()
for drug in drugs_to_genes.keys():
gene_list = drugs_to_genes[drug]
for gene in gene_list:
ytmp = pandas.DataFrame(Y.xs((gene, drug), level=["Target gene", "drug"], drop_level=False)['score'])
y_ranktmp, y_rank_raw, y_thresholdtmp, y_quanttmp = util.get_ranks(ytmp, thresh=0.8, prefix="score_drug_gene", flip=False)
# np.unique(y_rank.values-y_rank_raw.values)
y_rank = pandas.concat((y_rank, y_ranktmp), axis=0)
y_threshold = pandas.concat((y_threshold, y_thresholdtmp), axis=0)
y_quant = pandas.concat((y_quant, y_quanttmp), axis=0)
yall = pandas.concat((y_rank, y_threshold, y_quant), axis=1)
Y = pandas.merge(Y, yall, how='inner', left_index=True, right_index=True)
# convert also by drug only, irrespective of gene
y_rank = pandas.DataFrame()
y_threshold = pandas.DataFrame()
y_quant = pandas.DataFrame()
for drug in drugs_to_genes.keys():
ytmp = pandas.DataFrame(Y.xs(drug, level="drug", drop_level=False)['score'])
y_ranktmp, y_rank_raw, y_thresholdtmp, y_quanttmp = util.get_ranks(ytmp, thresh=0.8, prefix="score_drug", flip=False)
# np.unique(y_rank.values-y_rank_raw.values)
y_rank = pandas.concat((y_rank, y_ranktmp), axis=0)
y_threshold = pandas.concat((y_threshold, y_thresholdtmp), axis=0)
y_quant = pandas.concat((y_quant, y_quanttmp), axis=0)
yall = pandas.concat((y_rank, y_threshold, y_quant), axis=1)
Y = pandas.merge(Y, yall, how='inner', left_index=True, right_index=True)
PLOT = False
if PLOT:
# to better understand, try plotting something like:
labels = ["score", "score_drug_gene_rank", "score_drug_rank", "score_drug_gene_threshold", "score_drug_threshold"]
gene_position = util.impute_gene_position(gene_position)
if learn_options is not None and learn_options["weighted"] == "variance":
print "computing weights from replicate variance..."
# compute the variance across replicates so can use it as a weight
data = pandas.read_excel(data_file, sheetname="Normalized", skiprows=range(0, 6+1), index_col=[0, 4])
data.index.names = ["Sequence", "Target gene"]
experiments = {}
experiments['AZD_200nM'] = ['Deep 25', 'Deep 27', 'Deep 29 ', 'Deep 31']
experiments['6TG_2ug/mL'] = ['Deep 33', 'Deep 35', 'Deep 37', 'Deep 39']
experiments['PLX_2uM'] = ['Deep 49', 'Deep 51', 'Deep 53', 'Deep 55']
variance = None
for drug in drugs_to_genes.keys():
data_tmp = data.iloc[data.index.get_level_values('Target gene').isin(drugs_to_genes[drug])][experiments[drug]]
data_tmp["drug"] = drug
data_tmp = data_tmp.set_index('drug', append=True)
data_tmp["variance"] = np.var(data_tmp.values, axis=1)
if variance is None:
variance = data_tmp["variance"].copy()
else:
variance = pandas.concat((variance, data_tmp["variance"]), axis=0)
orig_index = Y.index.copy()
Y = pandas.merge(Y, pandas.DataFrame(variance), how="inner", left_index=True, right_index=True)
Y = Y.ix[orig_index]
print "done."
# Make sure to keep this check last in this function
assert Xdf.index.equals(Y.index), "The index of Xdf is different from the index of Y (this can cause inconsistencies/random performance later on)"
return Xdf, drugs_to_genes, target_genes, Y, gene_position
