import scipy
import numpy
import statsmodels.stats.multitest
import time
import sys
import collections
import base
import pytransit
import pytransit.transit_tools as transit_tools
import pytransit.tnseq_tools as tnseq_tools
import pytransit.norm_tools as norm_tools
############# GUI ELEMENTS ##################
short_name = "anova"
long_name = "anova"
short_desc = "Perform Anova analysis"
long_desc = """Perform Anova analysis"""
EOL = "\n"
transposons = ["", ""]
columns = []
[docs]class AnovaAnalysis(base.TransitAnalysis):
def __init__(self):
base.TransitAnalysis.__init__(self, short_name, long_name, short_desc, long_desc, transposons, AnovaMethod)
[docs]def main():
print("ANOVA example")
[docs]class AnovaMethod(base.MultiConditionMethod):
"""
anova
"""
def __init__(self, combined_wig, metadata, annotation, normalization, output_file, ignored_conditions=set()):
base.MultiConditionMethod.__init__(self, short_name, long_name, short_desc, long_desc, combined_wig, metadata, annotation, output_file, normalization=normalization)
self.ignored_conditions = ignored_conditions
[docs] @classmethod
def fromargs(self, rawargs):
(args, kwargs) = transit_tools.cleanargs(rawargs)
if (kwargs.get('-help', False) or kwargs.get('h', False)):
print(AnovaMethod.usage_string())
sys.exit(0)
combined_wig = args[0]
annotation = args[1]
metadata = args[2]
output_file = args[3]
normalization = kwargs.get("n", "TTR")
ignored_conditions = set(kwargs.get("-ignore-conditions", "Unknown").split(","))
return self(combined_wig, metadata, annotation, normalization, output_file, ignored_conditions)
[docs] def wigs_to_conditions(self, conditionsByFile, filenamesInCombWig):
"""
Returns list of conditions corresponding to given wigfiles.
({FileName: Condition}, [FileName]) -> [Condition]
Condition :: [String]
"""
return [conditionsByFile.get(f, "Unknown") for f in filenamesInCombWig]
[docs] def means_by_condition_for_gene(self, sites, conditions, data):
"""
Returns a dictionary of {Condition: Mean} for each condition.
([Site], [Condition]) -> {Condition: Number}
Site :: Number
Condition :: String
"""
wigsByConditions = collections.defaultdict(lambda: [])
for i, c in enumerate(conditions):
wigsByConditions[c].append(i)
return { c: numpy.mean(data[wigIndex][:, sites]) for (c, wigIndex) in wigsByConditions.items() }
[docs] def filter_by_conditions_blacklist(self, data, conditions, ignored_conditions):
"""
Filters out wigfiles, with ignored conditions.
([[Wigdata]], [Condition]) -> Tuple([[Wigdata]], [Condition])
"""
d_filtered, cond_filtered = [], [];
for i, c in enumerate(conditions):
if c not in ignored_conditions:
d_filtered.append(data[i])
cond_filtered.append(conditions[i])
return (numpy.array(d_filtered), numpy.array(cond_filtered))
[docs] def means_by_rv(self, data, RvSiteindexesMap, genes, conditions):
"""
Returns Dictionary of mean values by condition
([[Wigdata]], {Rv: SiteIndex}, [Gene], [Condition]) -> {Rv: {Condition: Number}}
Wigdata :: [Number]
SiteIndex :: Number
Gene :: {start, end, rv, gene, strand}
Condition :: String
"""
MeansByRv = {}
for gene in genes:
Rv = gene["rv"]
if len(RvSiteindexesMap[gene["rv"]]) > 0: # skip genes with no TA sites
MeansByRv[Rv] = self.means_by_condition_for_gene(RvSiteindexesMap[Rv], conditions, data)
return MeansByRv
[docs] def group_by_condition(self, wigList, conditions):
"""
Returns array of datasets, where each dataset corresponds to one condition.
([[Wigdata]], [Condition]) -> [[DataForCondition]]
Wigdata :: [Number]
Condition :: String
DataForCondition :: [Number]
"""
countsByCondition = collections.defaultdict(lambda: [])
for i, c in enumerate(conditions):
countsByCondition[c].append(wigList[i])
return [numpy.array(v).flatten() for v in countsByCondition.values()]
[docs] def run_anova(self, data, genes, MeansByRv, RvSiteindexesMap, conditions):
"""
Runs Anova (grouping data by condition) and returns p and q values
([[Wigdata]], [Gene], {Rv: {Condition: Mean}}, {Rv: [SiteIndex]}, [Condition]) -> Tuple([Number], [Number])
Wigdata :: [Number]
Gene :: {start, end, rv, gene, strand}
Mean :: Number
SiteIndex: Integer
Condition :: String
"""
count = 0
self.progress_range(len(genes))
pvals,Rvs = [],[]
for gene in genes:
count += 1
Rv = gene["rv"]
if Rv in MeansByRv:
countsvec = self.group_by_condition(map(lambda wigData: wigData[RvSiteindexesMap[Rv]], data), conditions)
stat,pval = scipy.stats.f_oneway(*countsvec)
pvals.append(pval)
Rvs.append(Rv)
# Update progress
text = "Running Anova Method... %5.1f%%" % (100.0*count/len(genes))
self.progress_update(text, count)
pvals = numpy.array(pvals)
mask = numpy.isfinite(pvals)
qvals = numpy.full(pvals.shape, numpy.nan)
qvals[mask] = statsmodels.stats.multitest.fdrcorrection(pvals[mask])[1] # BH, alpha=0.05
p,q = {},{}
for i,rv in enumerate(Rvs):
p[rv],q[rv] = pvals[i],qvals[i]
return (p, q)
[docs] def Run(self):
self.transit_message("Starting Anova analysis")
start_time = time.time()
self.transit_message("Getting Data")
(sites, data, filenamesInCombWig) = tnseq_tools.read_combined_wig(self.combined_wig)
self.transit_message("Normalizing using: %s" % self.normalization)
(data, factors) = norm_tools.normalize_data(data, self.normalization)
conditions = self.wigs_to_conditions(
self.read_samples_metadata(self.metadata),
filenamesInCombWig)
data, conditions = self.filter_by_conditions_blacklist(data, conditions, self.ignored_conditions)
genes = tnseq_tools.read_genes(self.annotation_path)
TASiteindexMap = {TA: i for i, TA in enumerate(sites)}
RvSiteindexesMap = tnseq_tools.rv_siteindexes_map(genes, TASiteindexMap)
MeansByRv = self.means_by_rv(data, RvSiteindexesMap, genes, conditions)
self.transit_message("Running Anova")
pvals,qvals = self.run_anova(data, genes, MeansByRv, RvSiteindexesMap, conditions)
self.transit_message("Adding File: %s" % (self.output))
file = open(self.output,"w")
conditionsList = list(set(conditions))
vals = "Rv Gene TAs".split() + conditionsList + "pval padj".split()
file.write('\t'.join(vals)+EOL)
for gene in genes:
Rv = gene["rv"]
if Rv in MeansByRv:
vals = ([Rv, gene["gene"], str(len(RvSiteindexesMap[Rv]))] +
["%0.1f" % MeansByRv[Rv][c] for c in conditionsList] +
["%f" % x for x in [pvals[Rv], qvals[Rv]]])
file.write('\t'.join(vals)+EOL)
file.close()
self.transit_message("Finished Anova analysis")
[docs] @classmethod
def usage_string(self):
return """python %s anova <combined wig file> <annotation .prot_table> <samples_metadata file> <output file> [Optional Arguments]
Optional Arguments:
-n <string> := Normalization method. Default: -n TTR
--ignore-conditions <cond1,cond2> := Comma seperated list of conditions to ignore, for the analysis. Default --ignore-conditions Unknown
""" % (sys.argv[0])
if __name__ == "__main__":
main()