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DataStore.py
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DataStore.py
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#!/bin/python
# This file will read in data into an internal data structure, allow analyses to be performed
# and then write the data back out into a format that the target algorithm understands.
import re
class Experiment:
name = ""
file = ""
ratios = {}
type = ""
tfs = []
def __init__(self, name, file, type, tfs=[]):
self.name = name
self.file = file
self.ratios = {}
self.tfs = tfs
self.type = type
class TFList:
tfs = []
file_name = ""
def __init__(self, tflist):
self.tfs = []
if type(tflist) == type(""):
tflist_file = tflist # Because we are dealing with the string to a file
self.file_name = tflist_file
f = open(tflist_file, 'r')
for line in f:
line = line.upper().strip()
line = line.replace(",","") # Just getting rid of any loose , from the csv
if line == "":
continue
self.tfs.append(line)
elif type(tflist) == type([]):
self.tfs = tflist # Just an array, possibly calculated from somewhere and not a file.
self.file_name = "transcription_factor_list.csv"
class MicroarrayData:
input_files = []
experiments = []
gene_list = []
time_mask = []
type = []
timeseries_num = None
def __init__(self, input_file, type, gene_list=None, timeseries_num=None):
if input_file != None:
self.input_files = []
self.input_files.append(input_file.split("/")[len(input_file.split("/"))-1])
else:
self.input_files = []
self.experiments = []
self.type = type
self.timeseries_num = timeseries_num
self.gene_list = gene_list
def average(self):
import math
avgexp = Experiment("Average", "gene_average.csv", "average")
for gene in self.gene_list:
gene_vals = []
for e in self.experiments:
gene_vals.append(e.ratios[gene])
avgexp.ratios[gene] = math.sum(gene_vals) / float(len(gene_vals))
self.experiments = [avgexp]
def median(self):
import numpy
medexp = Experiment("Median", "gene_median.csv", "median")
for gene in self.gene_list:
gene_vals = []
for e in self.experiments:
gene_vals.append(e.ratios[gene])
medexp.ratios[gene] = numpy.median(gene_vals)
self.experiments = [medexp]
def filter(self, filtered_gene_list):
filtered_experiments = []
for e in self.experiments:
f = Experiment(e.name, e.file, e.type)
for gene in e.ratios.keys():
if gene in filtered_gene_list:
f.ratios[gene] = e.ratios[gene]
f.gene_list = filtered_gene_list
filtered_experiments.append(f)
self.experiments = filtered_experiments
self.gene_list = filtered_gene_list
for gene in self.gene_list:
if gene not in filtered_gene_list:
for e in self.experiments:
del e.ratios[gene]
self.gene_list.remove(gene)
def combine(self, x):
if type(x) == type([]):
for e in x:
if set(self.gene_list) == set(e.gene_list):
self.experiments += e.experiments
else:
print "ERROR: Unable to combine datasets with different gene lists"
exit()
else:
if set(self.gene_list) != set(x.gene_list):
print "ERROR: Unable to combine datasets with different gene lists"
exit()
self.experiments += x.experiments
def getTimePoint(self, tp_num):
return self.experiments[tp_num]
def normalize(self):
import numpy
for e in self.experiments:
values = map(float,e.ratios.values())
normvals = (numpy.mean(values) - values) / numpy.std(values)
for i,key in enumerate(e.ratios.keys()):
e.ratios[key] = normvals[i]
def read_input(self, input_file, type):
line_split = re.compile('[\t,]')
def parse_file(input_file, type):
# This function will select which parser to use
self.input_files.append(input_file)
if type == "wildtype" or type == "multifactorial" or type == "knockdown" or type == "overexpression":
read_rowexp(input_file, type)
else:
read_colexp(input_file, type)
def read_colexp(input_file, type):
"""This function is to read simple files that are just the
header with the experiment name and then rows with:
gene_name\texpression value"""
print "Loading " + input_file + " as columns of experiments."
file = open(input_file, 'r')
file = file.readlines()
header = line_split.split(file[0])
names = []
for i, exp_name in enumerate(header):
if exp_name.strip() == "":
continue
exp = Experiment(exp_name.strip(), input_file, type)
self.experiments.append(exp)
for line in file[1:]:
if len(line.strip()) <= 1 or line.strip()[0] == "#":
continue
line = line_split.split(line)
gene_name = line[0].replace('"','')
exp_values = line[1:len(self.experiments)+1]
for i in xrange(len(exp_values)):
exp_values[i] = exp_values[i].strip()
try:
self.experiments[i].ratios[gene_name.upper()] = float(exp_values[i])
except:
print "Warning: Expression value in " + self.experiments[i].file + " on line " + \
str(i) + " will not read in as a float: " + exp_values[i] + "\n"
self.experiments[i].ratios[gene_name.upper()] = exp_values[i]
def read_rowexp(input_file, type):
"""This function reads in files that are columns of genes by rows
of experiments. These are usually wildtype and multifactorial data"""
print "Loading " + input_file + " as rows of experiments."
file = open(input_file, 'r')
file = file.readlines()
header = line_split.split(file.pop().replace('"',''))
gene_list = header
for i, exprow in enumerate(file):
# Instantiate the experiment datatypes
exp = Experiment(string(i), input_file, type)
self.experiments.append(exp)
for j, val in enumerate(exprow.split()):
gene_name = gene_list[j]
try:
self.experiments[i].ratios[gene_name.upper()] = float(exp_values[i])
except:
print "Warning: Expression value in " + self.experiments[i].file + " on line " + str(i) + " will not read in as a float: " + exp_values[i] + "\n"
self.experiments[i].ratios[gene_name.upper()] = exp_values[i]
parse_file(input_file, type)
self.gene_list = []
for e in self.experiments:
self.gene_list = self.gene_list + e.ratios.keys()
self.gene_list = list(set(self.gene_list)) # Setify list