def quartile_concentration_ratio(well, channel_num=0, threshold=None, peaks=None, min_events=4000):
if peaks is None:
peaks = accepted_peaks(well)
if len(peaks) < min_events:
return None
if not threshold:
threshold = well.channels[channel_num].statistics.threshold
if not threshold:
return None
quartile_size = len(peaks)/4
first_quartile = peaks[0:quartile_size]
last_quartile = peaks[len(peaks)-quartile_size:]
first_pos, first_neg = cluster_1d(first_quartile, channel_num, threshold)
fq_conc = concentration(len(first_pos), len(first_neg), droplet_vol=well.droplet_volume) # could be nan
last_pos, last_neg = cluster_1d(last_quartile, channel_num, threshold)
lq_conc = concentration(len(last_pos), len(last_neg), droplet_vol=well.droplet_volume) # could be nan
# if conc is nan or zero, we can't compute a real ratio
if math.isnan(fq_conc) or math.isnan(lq_conc) or fq_conc == 0 or lq_conc == 0:
return None
else:
return lq_conc/fq_conc
def fpfn_by_bin(plate_objects, vic_channels, sample_names, bin_func):
bins = set([bin_func(c) for c in vic_channels])
bin_plots = dict([(bin, []) for bin in bins])
bin_wells = defaultdict(list)
# divide into plates
for bin, group in groupinto(vic_channels, bin_func):
# this is a wacky grouping, but for reuse in plate_objects (why did I not pick plate ids again?)
plate_groups = groupinto(group, lambda c: (c.well.plate.file.dirname, c.well.plate.file.basename))
for plate_id, channels in plate_groups:
qplate = plate_objects[plate_id]
positives = [c for c in channels if c.well.well_name in fpfn_positive_well_names]
negatives = [c for c in channels if c.well.well_name in fpfn_negative_well_names]
# compute a threshold which is 1/4 between the positive and negative means for the plate
positive_means = []
negative_means = []
for p in positives:
amps = vic_amplitudes(accepted_peaks(qplate.wells[p.well.well_name]))
positive_means.append((len(amps), np.mean(amps)*len(amps)))
if positive_means:
positive_mean = sum([pm[1] for pm in positive_means])/sum([pm[0] for pm in positive_means])
else:
positive_mean = 32767
for n in negatives:
amps = vic_amplitudes(accepted_peaks(qplate.wells[n.well.well_name]))
negative_means.append((len(amps), np.mean(amps)*len(amps)))
if negative_means:
negative_mean = sum([nm[1] for nm in negative_means])/sum([nm[0] for nm in negative_means])
else:
negative_mean = 0
threshold = ((3*negative_mean)+positive_mean)/4
fps = [c for c in channels if c.well.well_name in fpfn_fp_well_names]
fns = [c for c in channels if c.well.well_name in fpfn_fn_well_names]
fp_counts = []
fn_counts = []
for f in fps:
pos, neg = cluster_1d(accepted_peaks(qplate.wells[f.well.well_name]), 1, threshold)
fp_counts.append((f.well.id, len(pos),
10000*(float(len(pos))/(float(len(pos))+float(len(neg)))),
qplate.wells[f.well.well_name]))
for f in fns:
pos, neg = cluster_1d(accepted_peaks(qplate.wells[f.well.well_name]), 1, threshold)
fn_counts.append((f.well.id, len(neg),
10000*(float(len(neg))/(float(len(pos))+float(len(neg))))))
bin_wells[bin].append((fp_counts, fn_counts, threshold))
return bin_wells
def dnr_by_bin(plate_objects, fam_channels, sample_names, bin_func):
"""
@deprecated - use metrics
"""
bins = set([bin_func(c) for c in fam_channels])
# TODO: do sample names here
bin_plots = dict([(bin, [[0.001,None],[0.01,None],[0.1,None],[1,None],[5,None]]) for bin in bins])
groups = []
groups.extend([(sample_name, groupinto(sample_name_channel_filter(fam_channels, sample_name), bin_func)) for sample_name in sample_names])
for i, (sample, group) in enumerate(groups):
for bin, channels in group:
conc_array = []
for c in channels:
qplate = plate_objects[(c.well.plate.file.dirname, c.well.plate.file.basename)]
well = qplate.wells[c.well.well_name]
# TODO: use dynamic threshold or keep 4000?
pos, neg = cluster_1d(accepted_peaks(well), 0, 4000)
conc, clow, chigh = concentration_interval(len(pos), len(neg), droplet_vol=well.droplet_volume)
conc_array.append((max(conc,0.0001), max(clow,0.0001), max(chigh,0.0001)))
if len(conc_array) > 0:
conc_mean = np.mean([ca[0] for ca in conc_array])
bin_plots[bin][i][1] = (conc_mean, conc_array)
return bin_plots
def well_statistics(qlwell, override_thresholds=None):
from pyqlb.nstats import concentration, concentration_interval
from pyqlb.nstats.peaks import cluster_1d
from pyqlb.nstats.well import well_observed_positives_negatives, accepted_peaks
if override_thresholds is None:
override_thresholds = [None]*len(qlwell.channels)
wellui = WellStatisticsUI(channels=[])
for idx, channel in enumerate(qlwell.channels):
if not override_thresholds[idx]:
positives, negatives, unclassified = well_observed_positives_negatives(qlwell, idx)
threshold = channel.statistics.threshold
conc = channel.statistics.concentration
conc_interval = (channel.statistics.concentration,
channel.statistics.concentration_lower_bound,
channel.statistics.concentration_upper_bound)
else:
positives, negatives = cluster_1d(accepted_peaks(qlwell), idx, override_thresholds[idx])
threshold = override_thresholds[idx]
conc = concentration(len(positives), len(negatives), qlwell.droplet_volume)
conc_interval = concentration_interval(len(positives), len(negatives), qlwell.droplet_volume)
channelui = WellChannelStatisticsUI(positives=len(positives),
negatives=len(negatives),
threshold=threshold,
concentration=conc,
concentration_interval=conc_interval)
wellui.channels.append(channelui)
return wellui
def count_positives_in_well(well, channel):
# return count of positives and total events
from pyqlb.nstats.peaks import cluster_1d, width_gated, quality_gated
chn_stats = well.channels[channel].statistics
threshold = chn_stats.threshold
min_width_gate = chn_stats.min_width_gate
max_width_gate = chn_stats.max_width_gate
min_quality_gate = chn_stats.min_quality_gate
accepted_events = accepted_peaks(well)
positives, negatives = cluster_1d(accepted_events, channel, threshold)
return len(positives), len(positives)+len(negatives)
def fam_variation_splits(well, threshold=None):
"""
Returns a 8-tuple: the gaussian parameters (A, mu, sigma) overall,
then for the first half of the amplitudes, then for the second half;
the overall mean, mean of the first half and the
mean of the second half, and the number of peaks on each half.
"""
from scipy.optimize import curve_fit
if threshold is not None:
positives, negatives = cluster_1d(well.peaks, 0, threshold)
peaks = positives
else:
peaks = well.peaks
amps = fam_amplitudes(peaks)
first_half = amps[:len(amps)/2]
second_half = amps[len(amps)/2:]
fbins = amp_bins(first_half, num_bins=257)
fvals, fpos = np.histogram(first_half, bins=fbins)
fcenters = bin_centers(fpos)
sbins = amp_bins(second_half, num_bins=257)
svals, spos = np.histogram(second_half, bins=sbins)
scenters = bin_centers(spos)
abins = amp_bins(amps, num_bins=257)
avals, apos = np.histogram(amps, bins=abins)
acenters = bin_centers(apos)
(gamp1, gmean1, gsigma1), covar = curve_fit(gauss, fcenters, fvals, p0=[max(fvals), np.mean(first_half), fpos[1]-fpos[0]])
(gamp2, gmean2, gsigma2), covar = curve_fit(gauss, scenters, svals, p0=[max(svals), np.mean(second_half), spos[1]-spos[0]])
(gamp, gmean, gsigma), covar = curve_fit(gauss, acenters, avals, p0=[max(avals), np.mean(amps), apos[1]-apos[0]])
return ((gamp, gmean, gsigma),
(gamp1, gmean1, gsigma1),
(gamp2, gmean2, gsigma2),
np.mean(amps),
np.mean(first_half),
np.mean(second_half),
peak_count(fvals, min_peak_val=max(fvals)/3),
peak_count(svals, min_peak_val=max(svals)/3))
def stats_for_qlp_well(well, compute_clusters=False, override_thresholds=None):
"""
Return statistics about a QLWell object read from a QLP file.
The QLWell object should have a populated `peaks` attribute (reading from QLBs won't work)
For parameter explanations and return values, see :func:`stats_for_qlp_well`.
"""
from pyqlb.nstats.peaks import cluster_1d, channel_amplitudes
from pyqlb.nstats.well import accepted_peaks, above_min_amplitude_peaks, well_channel_sp_values, well_cluster_peaks
from pyqlb.nstats.well import well_observed_positives_negatives, well_s2d_values, getClusters
from pyqlb.nstats.well import high_flier_droplets, low_flier_droplets, singleRain_droplets, doubleRain_droplets, diagonal_scatter
from numpy import mean as np_mean, std as np_std
if not override_thresholds:
override_thresholds = (None, None)
statistics = well_statistics(well, override_thresholds=override_thresholds)
accepted = len(accepted_peaks(well))
num_above_min = len(above_min_amplitude_peaks(well))
if num_above_min > 0 and accepted > 0:
if well.sum_amplitude_bins:
peaksets, boundaries, amps = revb_polydisperse_peaks(well, 0, threshold=override_thresholds[0])
poly_peaks = sum([len(p) for p in peaksets])
statistics[0].revb_polydispersity_pct = 100*float(poly_peaks)/num_above_min
else:
peaksets, boundaries, width_gates = polydisperse_peaks(well, 0, threshold=override_thresholds[0])
poly_peaks = sum([len(p) for p in peaksets])
statistics[0].revb_polydispersity_pct = 100*float(poly_peaks)/num_above_min
else:
statistics[0].revb_polydispersity_pct = 0
s, p_plus, p, p_minus = well_channel_sp_values(well, 0, override_threshold=override_thresholds[0])
statistics[0].s_value = s
statistics[0].p_plus = p_plus
statistics[0].p_plus_drops = int(p_plus*accepted) if p_plus is not None else None
statistics[0].p = p
statistics[0].p_drops = int(p*accepted) if p is not None else None
statistics[0].p_minus = p_minus
statistics[0].p_minus_drops = int(p_minus*accepted) if p_minus is not None else None
if num_above_min > 0 and accepted > 0:
if well.sum_amplitude_bins:
peaksets, boundaries, amps = revb_polydisperse_peaks(well, 1, threshold=override_thresholds[1])
poly_peaks = sum([len(p) for p in peaksets])
statistics[1].revb_polydispersity_pct = 100*float(poly_peaks)/num_above_min
else:
peaksets, boundaries, width_gates = polydisperse_peaks(well, 1, threshold=override_thresholds[1])
poly_peaks = sum([len(p) for p in peaksets])
statistics[1].revb_polydispersity_pct = 100*float(poly_peaks)/num_above_min
else:
statistics[1].revb_polydispersity_pct = 0
s, p_plus, p, p_minus = well_channel_sp_values(well, 1, override_threshold=override_thresholds[1])
statistics[1].s_value = s
statistics[1].p_plus = p_plus
statistics[1].p_plus_drops = int(p_plus*accepted) if p_plus is not None else None
statistics[1].p = p
statistics[1].p_drops = int(p*accepted) if p is not None else None
statistics[1].p_minus = p_minus
statistics[1].p_minus_drops = int(p_minus*accepted) if p_minus is not None else None
## compute s2d plots
s2d_vals = well_s2d_values( well, thresholds=override_thresholds)
statistics[0].s2d_value = s2d_vals[0] if s2d_vals is not None else None
statistics[1].s2d_value = s2d_vals[1] if s2d_vals is not None else None
## compute extra cluster metrics
clusters = getClusters( well, override_thresholds )
dscatter = diagonal_scatter( clusters )
statistics.diagonal_scatter = dscatter[1] if dscatter is not None else None
statistics.diagonal_scatter_pct = dscatter[2] *100 if dscatter is not None else None
for channel in [0,1]:
high_fliers = high_flier_droplets( clusters, channel )
statistics[channel].high_flier_value = high_fliers[1] if high_fliers is not None else None
statistics[channel].high_flier_pct = high_fliers[2] * 100 if high_fliers is not None else None
low_fliers = low_flier_droplets( clusters, channel )
statistics[channel].low_flier_value = low_fliers[1] if low_fliers is not None else None
statistics[channel].low_flier_pct = low_fliers[2] * 100 if low_fliers is not None else None
singleRain = singleRain_droplets( clusters, channel )
statistics[channel].single_rain_value = singleRain[1] if singleRain is not None else None
statistics[channel].single_rain_pct = singleRain[2] * 100 if singleRain is not None else None
doubleRain = doubleRain_droplets( clusters, channel )
statistics[channel].double_rain_value = doubleRain[1] if doubleRain is not None else None
statistics[channel].double_rain_pct = doubleRain[2] * 100 if doubleRain is not None else None
if compute_clusters:
clusters = well_cluster_peaks(well, override_thresholds)
else:
clusters = {'positive_peaks': {'positive_peaks': [], 'negative_peaks': []},
'negative_peaks': {'positive_peaks': [], 'negative_peaks': []}}
# cheap hack
statistics.alg_version = "%s.%s/%s.%s" % (well.statistics.peak_alg_major_version,
well.statistics.peak_alg_minor_version,
well.statistics.quant_alg_major_version,
well.statistics.quant_alg_minor_version)
statistics.ref_copy_num = well.ref_copy_num
statistics[0].decision_tree = well.channels[0].decision_tree_verbose
statistics[1].decision_tree = well.channels[1].decision_tree_verbose
# end cheap hack
# SNR
for chan in (0,1):
if override_thresholds[chan]:
# TODO add this to pyqlb.nstats.well instead
pos, neg = cluster_1d(accepted_peaks(well), chan, override_thresholds[chan])
else:
pos, neg, unknown = well_observed_positives_negatives(well, chan)
for attr, coll in (('positive_snr', pos),('negative_snr',neg)):
if len(pos) > 0:
amps = channel_amplitudes(coll, chan)
amp_mean = np_mean(amps)
amp_std = np_std(amps)
if amp_std > 0:
setattr(statistics[chan], attr, amp_mean/amp_std)
else:
setattr(statistics[chan], attr, 10000)
else:
setattr(statistics[chan], attr, 0)
for channel in [0,1]:
means,stds = total_events_amplitude_vals(well,channel)
statistics[channel].total_events_amplitude_mean = means if means is not None else None
statistics[channel].total_events_amplitude_stdev = stds if stds is not None else None
return statistics, clusters