def main():
'''
Usage: compute_gr_metrics.py input.tsv > output.tsv
Compute Growth Response metrics for precomputed GR dose-response data in
TSV format.
The input tsv file must meet the following requirements:
- The first row contains column names.
- The 'concentration' column contains the numeric dose of a perturbagen.
- The 'GRvalue' column contains the GR value for that condition.
The input file may also have other "key" columns to distinguish multiple
separate dose-response experiments within the same file.
The GR value may be computed from raw cell counts using the
`add_gr_column.py` script. The columns required as input for that script
will be ignored by this one, and as such the output of `add_gr_column.py`
may be piped directly to the input of this script.
The output tsv will have all key columns in addition to a column for each GR
metric. (See the documentation of gr50.gr_metrics for details)
'''
if '-h' in sys.argv or '--help' in sys.argv:
print textwrap.dedent(main.__doc__)
return
reader = LineReader(fileinput.input(mode='rb'))
data = pd.read_csv(reader, delimiter='\t')
metrics = gr50.gr_metrics(data)
print metrics.to_csv(sep='\t', index=False)
def output_test():
curfilePath = os.path.abspath(__file__)
Dir = os.path.dirname(
os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(curfilePath)))))
Dirfile = os.path.abspath(os.path.join(Dir,
'INPUT/toy_example_input1.tsv'))
df = pd.read_csv(Dirfile, delimiter='\t')
# Read the matlab output.
Dirfile = os.path.abspath(
os.path.join(Dir, 'OUTPUT/matlab_input1_GRmetrics.tsv'))
metrics_matlab = pd.read_csv(Dirfile, delimiter='\t')
# Compute the GR metrics from the data.
gr_values = gr50.compute_gr(df)
metrics_python = gr50.gr_metrics(gr_values)
print(metrics_python.columns)
print(metrics_matlab.columns)
# Determine key columns and sort both dataframes by those columns. Since
# this script is expected to work on arbitrary inputs, we need to "sniff"
# out which columns are the keys (otherwise we'd need that as an input too).
# Both the matlab and python implementations put the keys first and the
# metrics second (starting with GR50) so we will use that to our advantage.
# Of course they don't both sort the rows the same way which is why we need
# to do that here.
first_data_col_index = list(metrics_python).index('GR50')
keys_python = metrics_python.columns[:first_data_col_index]
keys_matlab = metrics_matlab.columns[:first_data_col_index]
assert sorted(keys_python) == sorted(keys_matlab), "Key column mismatch"
keys = list(keys_python)
for df in metrics_python, metrics_matlab:
# Perform the sort.
df.sort_values(keys, inplace=True)
df.reset_index(drop=True, inplace=True)
# Compute log10 of GR50 and drop original column.
df.insert(first_data_col_index, 'log10_GR50', np.log10(df['GR50']))
del df['GR50']
df.insert(first_data_col_index, 'log10_GEC50', np.log10(df['GEC50']))
del df['GEC50']
metrics = [c for c in metrics_python if c not in keys]
errs = ['err_' + m for m in metrics]
# ignore pval and r^2 for now
errs = errs[:-2]
error = metrics_python[keys].copy()
for m, e in zip(metrics, errs):
err = (1 - metrics_python[m] / metrics_matlab[m])
error[e] = err.fillna(0)
rejects = np.isinf(metrics_python.log10_GR50)
rejects2 = np.isinf(metrics_python.log10_GEC50)
# Sanity check to make sure both versions pass/reject the same things.
assert (rejects == np.isinf(metrics_matlab.log10_GR50)).all()
assert (rejects2 == np.isinf(metrics_matlab.log10_GEC50)).all()
# Keep pandas from wrapping.
pd.set_option('display.width', None)
print
print("Records with > 0.1% error between python and matlab values of any "
"metric:")
test1 = error[(abs(error[errs]) > 0.001).any(axis=1)]
print(test1)
print
print("Records with > 10% error between python and matlab values of any "
"metric:")
test2 = error[(abs(error[errs]) > 0.1).any(axis=1)]
print(test2)
# Insert rows at the top with median and IQR of each error column.
median = error[errs].median()
iqr = np.subtract(*np.percentile(error[errs], [75, 25], axis=0))
error.loc[-2] = ['<median>', -1, -1, 'N/A', -1] + list(median)
error.loc[-1] = ['<IQR>', -1, -1, 'N/A', -1] + list(iqr)
error.sort_index(inplace=True)
# Passes if none of the metrics differ by 10%
# Just an example of a passing test.
assert test2.shape[0] == 0
import matplotlib.pyplot as plt
import gr50
# Read the data file.
base_path = path.join(path.dirname(path.abspath(__file__)), '..', '..', '..')
input_path = path.join(base_path, 'OUTPUT', 'toy_example_output.tsv')
df = pd.read_csv(input_path, delimiter='\t')
# Filter down to only a manageable subset of the experiments.
filters = (('time', 72), ('perturbation', 0), ('replicate', 1))
for column, value in filters:
df = df[df[column] == value]
del df[column]
# Compute the GR metrics from the data.
gr_metrics = gr50.gr_metrics(df)
# Produce a trellis plot showing the fitted curves and some of the metrics
# across the different cell lines and drugs.
sns.set(style="ticks")
grid = sns.FacetGrid(df, row="cell_line", col="agent", margin_titles=True)
grid.set(xscale="log")
grid.map(plt.plot, "concentration", "GRvalue", lw=0, marker='o', ms=4)
x_min = df.concentration.min() / 10
x_max = df.concentration.max() * 10
fit_x = np.logspace(np.log10(x_min), np.log10(x_max))
for cell_line, row_axes in zip(grid.row_names, grid.axes):
for agent, ax in zip(grid.col_names, row_axes):
for m in gr_metrics[(gr_metrics.agent == agent) &
(gr_metrics.cell_line == cell_line)].itertuples():
fit_y = gr50.logistic(fit_x, [m.GRinf, np.log10(m.GEC50), m.h_GR])
import matplotlib.pyplot as plt
import gr50
# Read the data file.
base_path = path.join(path.dirname(path.abspath(__file__)), '..', '..', '..')
input_path = path.join(base_path, 'OUTPUT', 'toy_example_output.tsv')
df = pd.read_csv(input_path, delimiter='\t')
# Filter down to only a manageable subset of the experiments.
filters = (('time', 72), ('perturbation', 0), ('replicate', 1))
for column, value in filters:
df = df[df[column] == value]
del df[column]
# Compute the GR metrics from the data.
gr_metrics = gr50.gr_metrics(df)
# Produce a trellis plot showing the fitted curves and some of the metrics
# across the different cell lines and drugs.
sns.set(style="ticks")
grid = sns.FacetGrid(df, row="cell_line", col="agent", margin_titles=True)
grid.set(xscale="log")
grid.map(plt.plot, "concentration", "GRvalue", lw=0, marker='o', ms=4)
x_min = df.concentration.min() / 10
x_max = df.concentration.max() * 10
fit_x = np.logspace(np.log10(x_min), np.log10(x_max))
for cell_line, row_axes in zip(grid.row_names, grid.axes):
for agent, ax in zip(grid.col_names, row_axes):
for m in gr_metrics[(gr_metrics.agent == agent) &
(gr_metrics.cell_line == cell_line)].itertuples():
fit_y = gr50.logistic(fit_x, [m.GRinf, np.log10(m.GEC50), m.h_GR])
