def main(args):
sims = os.path.sep.join([pathname, '..', 'sims', 'doi_10.1101_025387', args.species])
rdict = {}
corrs = {}
for i in xrange(1, 7):
path = os.path.sep.join([sims, str(i)])
print("reading results from {}".format(path))
tdf = ParsingUtils.readThreeColumnTruth(os.path.sep.join([path, "truth.tsv"]) , "_true")
kdf = ParsingUtils.readKallisto(os.path.sep.join([path, "abundance.tsv"]), "_kallisto")
sdf = ParsingUtils.readSailfish(os.path.sep.join([path, "quant.sf"]), "_sailfish")
df = tdf.join(kdf, rsuffix="_K").join(sdf, rsuffix="_S")
for method in ["sailfish", "kallisto"]:
for ct in ["spearman", "pearson"]:
corrKey = "{}_{}".format(method, ct)
corr = df["TPM_true"].corr(df["TPM_{}".format(method)], method=ct)
if corrKey in corrs:
corrs[corrKey].append(corr)
else:
corrs[corrKey] = [corr]
rdict[i] = df
corrStrs = []
for k, v in corrs.iteritems():
corrStrs.append("{}: {}".format(k, ','.join(["{:0.2}".format(c) for c in v])))
corrStrs = sorted(corrStrs)
print('\n'.join(corrStrs))
relDiffs = {}
for k,v in rdict.iteritems():
rds = AnalysisUtils.relDiff("TPM_true", "TPM_sailfish", v, verbose=False)
rdk = AnalysisUtils.relDiff("TPM_true", "TPM_kallisto", v, verbose=False)
for method, rd in {"sailfish" : rds, "kallisto" : rdk}.iteritems():
for summaryName, summaryFunc in {"median" : AnalysisUtils.getMedian, "mean" : AnalysisUtils.getMean}.iteritems():
signedKey = "{}_{}_{}".format(method, summaryName, "signed")
absKey = "{}_{}_{}".format(method, summaryName, "abs")
if signedKey in relDiffs:
relDiffs[signedKey].append(summaryFunc(rd[0]))
else:
relDiffs[signedKey] = [summaryFunc(rd[0])]
if absKey in relDiffs:
relDiffs[absKey].append(summaryFunc(rd[0].abs()))
else:
relDiffs[absKey] = [summaryFunc(rd[0].abs())]
for signedness in ["signed", "abs"]:
for stat in ["median", "mean"]:
if stat == "median":
print("mean of medians of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.mean(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.mean(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
elif stat == "mean":
print("median of means of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.median(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.median(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
def main():
sims = os.path.sep.join([pathname, '..', 'sims', 'rsem'])
rdict = {}
for i in xrange(1, 21):
path = os.path.sep.join([sims, str(i)])
print("reading results from {}".format(path))
tdf = ParsingUtils.readRSEMTruth(os.path.sep.join([path, "truth.tsv"]) , "_true")
kdf = ParsingUtils.readKallisto(os.path.sep.join([path, "abundance.tsv"]), "_kallisto")
sdf = ParsingUtils.readSailfish(os.path.sep.join([path, "quant.sf"]), "_sailfish")
df = tdf.join(kdf, rsuffix="_K").join(sdf, rsuffix="_S")
rdict[i] = df
relDiffs = {}
for k,v in rdict.iteritems():
rds = AnalysisUtils.relDiff("TPM_true", "TPM_sailfish", v, verbose=False)
rdk = AnalysisUtils.relDiff("TPM_true", "TPM_kallisto", v, verbose=False)
for method, rd in {"sailfish" : rds, "kallisto" : rdk}.iteritems():
for summaryName, summaryFunc in {"median" : AnalysisUtils.getMedian, "mean" : AnalysisUtils.getMean}.iteritems():
signedKey = "{}_{}_{}".format(method, summaryName, "signed")
absKey = "{}_{}_{}".format(method, summaryName, "abs")
if signedKey in relDiffs:
relDiffs[signedKey].append(summaryFunc(rd[0]))
else:
relDiffs[signedKey] = [summaryFunc(rd[0])]
if absKey in relDiffs:
relDiffs[absKey].append(summaryFunc(rd[0].abs()))
else:
relDiffs[absKey] = [summaryFunc(rd[0].abs())]
for signedness in ["signed", "abs"]:
for stat in ["median", "mean"]:
if stat == "median":
print("mean of medians of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.mean(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.mean(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
elif stat == "mean":
print("median of means of {} relative differences is :\n kallisto: {:0.2f}\n sailfish: {:0.2f}\n".format(
signedness, np.median(relDiffs["kallisto_{}_{}".format(stat, signedness)]),
np.median(relDiffs["sailfish_{}_{}".format(stat, signedness)])))
