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- </head>
- <body>
- <main>
- <article id="content">
- <header>
- <h1 class="title">Module <code>src.outcomes</code></h1>
- </header>
- <section id="section-intro">
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">import numpy as np
- import pandas as pd
- pd.options.mode.chained_assignment = None # default='warn' - caution: this turns off setting with copy warning
- from viz import *
- def add_rule_based_label(df):
- df['Y_peak_time_frac'] = df['Y_peak_idx'].values / df['lifetime'].values
- df['y_z_score'] = (df['Y_max'].values - df['Y_mean'].values) / df['Y_std'].values
- X_max_around_Y_peak = []
- X_max_after_Y_peak = []
- for i in range(len(df)):
- pt = df['Y_peak_idx'].values[i]
- lt = df['lifetime'].values[i]
- left_bf = np.int(0.2 * lt) + 1 # look at a window with length = 30%*lifetime
- right_bf = np.int(0.1 * lt) + 1
- arr_around = df['X'].iloc[i][max(0, pt - left_bf): min(pt + right_bf, lt)]
- arr_after = df['X'].iloc[i][min(pt + right_bf, lt - 1):]
- X_max_around_Y_peak.append(max(arr_around))
- if len(arr_after) > 0:
- X_max_after_Y_peak.append(max(arr_after))
- else:
- X_max_after_Y_peak.append(max(arr_around))
- df['X_max_around_Y_peak'] = X_max_around_Y_peak
- df['X_max_after_Y_peak'] = X_max_after_Y_peak
- df['X_max_diff'] = df['X_max_around_Y_peak'] - df['X_max_after_Y_peak']
- def rule_based_model(track):
- # three rules:
- # if aux peaks too early -- negative
- # elif:
- # if y_consec_sig or y_conservative_thresh or (cla drops around aux peak, and aux max is greater than
- # mean + 2.6*std), then positive
- # else: negative
- if track['Y_peak_time_frac'] < 0.2:
- return 0
- if track['y_consec_sig'] or track['y_conservative_thresh']:
- return 1
- # if track['X_max_diff'] > 260 and track['y_z_score'] > 2.6:
- # return 1
- if track['X_max_diff'] > 260 and track['Y_max'] > 560:
- return 1
- return 0
- df['y_rule_based'] = np.array([rule_based_model(df.iloc[i]) for i in range(len(df))])
- return df
- def add_outcomes(df, LABELS=None, thresh=3.25, p_thresh=0.05, aux_peak=642.375, aux_thresh=973):
- '''Add binary outcome of whether spike happened and info on whether events were questionable
- '''
- df['y_score'] = df['Y_max'].values - (df['Y_mean'].values + thresh * df['Y_std'].values)
- df['y_thresh'] = (df['y_score'].values > 0).astype(np.int) # Y_max was big
- df['y'] = df['Y_max'] > aux_peak
- # outcomes based on significant p-values
- num_sigs = [np.array(df['Y_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- df['y_num_sig'] = np.array([num_sigs[i].sum() for i in range(df.shape[0])]).astype(np.int)
- df['y_single_sig'] = np.array([num_sigs[i].sum() > 0 for i in range(df.shape[0])]).astype(np.int)
- df['y_double_sig'] = np.array([num_sigs[i].sum() > 1 for i in range(df.shape[0])]).astype(np.int)
- df['y_conservative_thresh'] = (df['Y_max'].values > aux_thresh).astype(np.int)
- y_consec_sig = []
- y_sig_min_diff = []
- for i in range(df.shape[0]):
- idxs_sig = np.where(num_sigs[i] == 1)[0] # indices of significance
- if len(idxs_sig) > 1:
- y_sig_min_diff.append(np.min(np.diff(idxs_sig)))
- else:
- y_sig_min_diff.append(np.nan)
- # find whether there were consecutive sig. indices
- if len(idxs_sig) > 1 and np.min(np.diff(idxs_sig)) == 1:
- y_consec_sig.append(1)
- else:
- y_consec_sig.append(0)
- df['y_consec_sig'] = y_consec_sig
- df['y_sig_min_diff'] = y_sig_min_diff
- df['y_consec_thresh'] = np.logical_or(df['y_consec_sig'], df['y_conservative_thresh'])
- def add_hotspots(df, num_sigs, outcome_def='consec_sig'):
- '''Identify hotspots as any track which over its time course has multiple events
- events must meet the event definition, then for a time not meet it, then meet it again
- Example: two consecutive significant p-values, then non-significant p-value, then 2 more consecutive p-values
- '''
- if outcome_def == 'consec_sig':
- hotspots = np.zeros(df.shape[0]).astype(np.int)
- for i in range(df.shape[0]):
- idxs_sig = np.where(num_sigs[i] == 1)[0] # indices of significance
- if idxs_sig.size < 5:
- hotspots[i] = 0
- else:
- diffs = np.diff(idxs_sig)
- consecs = np.where(diffs == 1)[0] # diffs==1 means there were consecutive sigs
- consec_diffs = np.diff(consecs)
- if consec_diffs.shape[0] > 0 and np.max(
- consec_diffs) > 2: # there were greated than 2 non-consec sigs between the consec sigs
- hotspots[i] = 1
- else:
- hotspots[i] = 0
- df['sig_idxs'] = num_sigs
- df['hotspots'] = hotspots == 1
- return df
- df = add_hotspots(df, num_sigs)
- if LABELS is not None:
- df['y_consec_thresh'][df.pid.isin(LABELS['pos'])] = 1 # add manual pos labels
- df['y_consec_thresh'][df.pid.isin(LABELS['neg'])] = 0 # add manual neg labels
- df['hotspots'][df.pid.isin(LABELS['hotspots'])] = True # add manual hotspot labels
- df = add_rule_based_label(df)
- return df
- def add_sig_mean(df, resp_tracks=['Y']):
- """add response of regression problem: mean auxilin strength among significant observations
- """
- for track in resp_tracks:
- sig_mean = []
- for i in range(len(df)):
- r = df.iloc[i]
- sigs = np.array(r[f'{track}_pvals']) < 0.05
- if sum(sigs)>0:
- sig_mean.append(np.mean(np.array(r[track])[sigs]))
- else:
- sig_mean.append(0)
- df[f'{track}_sig_mean'] = sig_mean
- df[f'{track}_sig_mean_normalized'] = sig_mean
- for cell in set(df['cell_num']):
- cell_idx = np.where(df['cell_num'].values == cell)[0]
- y = df[f'{track}_sig_mean'].values[cell_idx]
- df[f'{track}_sig_mean_normalized'].values[cell_idx] = (y - np.mean(y))/np.std(y)
- return df
- def add_aux_dyn_outcome(df, p_thresh=0.05, clath_thresh=1500, dyn_thresh=2000,
- dyn_cons_thresh=5, clath_sig_frac=0.5, clath_consec_thresh_frac=0.15):
- """add response of regression problem: mean auxilin strength among significant observations
- """
-
- # look for clathrin significance
- num_sigs = [np.array(df['X_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- x_consec_sig = []
- x_frac_sig = []
- lifetime_steps = np.array([len(df['X'].iloc[i]) for i in range(df.shape[0])]) # get lifetimes
- for i in range(df.shape[0]):
- l = lifetime_steps[i]
- sigs = num_sigs[i]
- x_frac_sig.append(np.mean(sigs) >= clath_sig_frac)
- cons = 0
- consec_flag = False
- for j in range(len(sigs)):
- if sigs[j] == 1:
- cons += 1
- else:
- cons = 0
- if cons >= max(l * clath_consec_thresh_frac, 5):
- consec_flag = True
- break
- if consec_flag:
- x_consec_sig.append(1)
- else:
- x_consec_sig.append(0)
-
-
- # outcomes based on significant p-values
- df['clath_conservative_thresh'] = (df['X_max'].values > clath_thresh).astype(np.int)
- df['clath_sig'] = np.logical_and(x_consec_sig, x_frac_sig)
- df['successful'] = np.logical_and(df['y_consec_thresh'], df['clath_conservative_thresh'])
- df['successful_dynamin'] = df['successful']
- df['successful_full'] = np.logical_and(df['clath_sig'], df['successful_dynamin'])
-
-
- # look for dynamin peak
- if 'Z' in df.keys():
- num_sigs = [np.array(df['Z_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- z_consec_sig = []
- for i in range(df.shape[0]):
- sigs = num_sigs[i]
- cons = 0
- consec_flag = False
- for j in range(len(sigs)):
- if sigs[j] == 1:
- cons += 1
- else:
- cons = 0
- if cons >= dyn_cons_thresh:
- consec_flag = True
- break
- if consec_flag:
- z_consec_sig.append(1)
- else:
- z_consec_sig.append(0)
- df['z_consec_sig'] = z_consec_sig
- df['Z_max'] = [np.max(df.iloc[i]['Z']) for i in range(df.shape[0])]
- df['z_thresh'] = df['Z_max'] > dyn_thresh
- df['z_consec_thresh'] = np.logical_and(df['z_consec_sig'], df['z_thresh'])
- df['Y_peak_idx'] = np.nan_to_num(np.array([np.argmax(y) for y in df.Y]))
- df['Z_peak_idx'] = np.nan_to_num(np.array([np.argmax(z) for z in df.Z]))
- df['z_peaked_first'] = df['Z_peak_idx'] < df['Y_peak_idx']
- df['z_peak'] = np.logical_and(df['z_consec_thresh'], df['z_peaked_first'])
-
- # peaks must happen at end of track
- df['z_peak'] = np.logical_and(df['z_peak'], df['Z_peak_idx'] > lifetime_steps / 2)
-
-
- df['successful_dynamin'] = np.logical_or(
- df['successful'],
- np.logical_and(df['clath_conservative_thresh'], df['z_peak'])
- )
- df['successful_full'] = np.logical_and(df['clath_sig'], df['successful_dynamin'])
-
- return df</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-functions">Functions</h2>
- <dl>
- <dt id="src.outcomes.add_aux_dyn_outcome"><code class="name flex">
- <span>def <span class="ident">add_aux_dyn_outcome</span></span>(<span>df, p_thresh=0.05, clath_thresh=1500, dyn_thresh=2000, dyn_cons_thresh=5, clath_sig_frac=0.5, clath_consec_thresh_frac=0.15)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>add response of regression problem: mean auxilin strength among significant observations</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def add_aux_dyn_outcome(df, p_thresh=0.05, clath_thresh=1500, dyn_thresh=2000,
- dyn_cons_thresh=5, clath_sig_frac=0.5, clath_consec_thresh_frac=0.15):
- """add response of regression problem: mean auxilin strength among significant observations
- """
-
- # look for clathrin significance
- num_sigs = [np.array(df['X_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- x_consec_sig = []
- x_frac_sig = []
- lifetime_steps = np.array([len(df['X'].iloc[i]) for i in range(df.shape[0])]) # get lifetimes
- for i in range(df.shape[0]):
- l = lifetime_steps[i]
- sigs = num_sigs[i]
- x_frac_sig.append(np.mean(sigs) >= clath_sig_frac)
- cons = 0
- consec_flag = False
- for j in range(len(sigs)):
- if sigs[j] == 1:
- cons += 1
- else:
- cons = 0
- if cons >= max(l * clath_consec_thresh_frac, 5):
- consec_flag = True
- break
- if consec_flag:
- x_consec_sig.append(1)
- else:
- x_consec_sig.append(0)
-
-
- # outcomes based on significant p-values
- df['clath_conservative_thresh'] = (df['X_max'].values > clath_thresh).astype(np.int)
- df['clath_sig'] = np.logical_and(x_consec_sig, x_frac_sig)
- df['successful'] = np.logical_and(df['y_consec_thresh'], df['clath_conservative_thresh'])
- df['successful_dynamin'] = df['successful']
- df['successful_full'] = np.logical_and(df['clath_sig'], df['successful_dynamin'])
-
-
- # look for dynamin peak
- if 'Z' in df.keys():
- num_sigs = [np.array(df['Z_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- z_consec_sig = []
- for i in range(df.shape[0]):
- sigs = num_sigs[i]
- cons = 0
- consec_flag = False
- for j in range(len(sigs)):
- if sigs[j] == 1:
- cons += 1
- else:
- cons = 0
- if cons >= dyn_cons_thresh:
- consec_flag = True
- break
- if consec_flag:
- z_consec_sig.append(1)
- else:
- z_consec_sig.append(0)
- df['z_consec_sig'] = z_consec_sig
- df['Z_max'] = [np.max(df.iloc[i]['Z']) for i in range(df.shape[0])]
- df['z_thresh'] = df['Z_max'] > dyn_thresh
- df['z_consec_thresh'] = np.logical_and(df['z_consec_sig'], df['z_thresh'])
- df['Y_peak_idx'] = np.nan_to_num(np.array([np.argmax(y) for y in df.Y]))
- df['Z_peak_idx'] = np.nan_to_num(np.array([np.argmax(z) for z in df.Z]))
- df['z_peaked_first'] = df['Z_peak_idx'] < df['Y_peak_idx']
- df['z_peak'] = np.logical_and(df['z_consec_thresh'], df['z_peaked_first'])
-
- # peaks must happen at end of track
- df['z_peak'] = np.logical_and(df['z_peak'], df['Z_peak_idx'] > lifetime_steps / 2)
-
-
- df['successful_dynamin'] = np.logical_or(
- df['successful'],
- np.logical_and(df['clath_conservative_thresh'], df['z_peak'])
- )
- df['successful_full'] = np.logical_and(df['clath_sig'], df['successful_dynamin'])
-
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.outcomes.add_outcomes"><code class="name flex">
- <span>def <span class="ident">add_outcomes</span></span>(<span>df, LABELS=None, thresh=3.25, p_thresh=0.05, aux_peak=642.375, aux_thresh=973)</span>
- </code></dt>
- <dd>
- <section class="desc"><p>Add binary outcome of whether spike happened and info on whether events were questionable</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def add_outcomes(df, LABELS=None, thresh=3.25, p_thresh=0.05, aux_peak=642.375, aux_thresh=973):
- '''Add binary outcome of whether spike happened and info on whether events were questionable
- '''
- df['y_score'] = df['Y_max'].values - (df['Y_mean'].values + thresh * df['Y_std'].values)
- df['y_thresh'] = (df['y_score'].values > 0).astype(np.int) # Y_max was big
- df['y'] = df['Y_max'] > aux_peak
- # outcomes based on significant p-values
- num_sigs = [np.array(df['Y_pvals'].iloc[i]) < p_thresh for i in range(df.shape[0])]
- df['y_num_sig'] = np.array([num_sigs[i].sum() for i in range(df.shape[0])]).astype(np.int)
- df['y_single_sig'] = np.array([num_sigs[i].sum() > 0 for i in range(df.shape[0])]).astype(np.int)
- df['y_double_sig'] = np.array([num_sigs[i].sum() > 1 for i in range(df.shape[0])]).astype(np.int)
- df['y_conservative_thresh'] = (df['Y_max'].values > aux_thresh).astype(np.int)
- y_consec_sig = []
- y_sig_min_diff = []
- for i in range(df.shape[0]):
- idxs_sig = np.where(num_sigs[i] == 1)[0] # indices of significance
- if len(idxs_sig) > 1:
- y_sig_min_diff.append(np.min(np.diff(idxs_sig)))
- else:
- y_sig_min_diff.append(np.nan)
- # find whether there were consecutive sig. indices
- if len(idxs_sig) > 1 and np.min(np.diff(idxs_sig)) == 1:
- y_consec_sig.append(1)
- else:
- y_consec_sig.append(0)
- df['y_consec_sig'] = y_consec_sig
- df['y_sig_min_diff'] = y_sig_min_diff
- df['y_consec_thresh'] = np.logical_or(df['y_consec_sig'], df['y_conservative_thresh'])
- def add_hotspots(df, num_sigs, outcome_def='consec_sig'):
- '''Identify hotspots as any track which over its time course has multiple events
- events must meet the event definition, then for a time not meet it, then meet it again
- Example: two consecutive significant p-values, then non-significant p-value, then 2 more consecutive p-values
- '''
- if outcome_def == 'consec_sig':
- hotspots = np.zeros(df.shape[0]).astype(np.int)
- for i in range(df.shape[0]):
- idxs_sig = np.where(num_sigs[i] == 1)[0] # indices of significance
- if idxs_sig.size < 5:
- hotspots[i] = 0
- else:
- diffs = np.diff(idxs_sig)
- consecs = np.where(diffs == 1)[0] # diffs==1 means there were consecutive sigs
- consec_diffs = np.diff(consecs)
- if consec_diffs.shape[0] > 0 and np.max(
- consec_diffs) > 2: # there were greated than 2 non-consec sigs between the consec sigs
- hotspots[i] = 1
- else:
- hotspots[i] = 0
- df['sig_idxs'] = num_sigs
- df['hotspots'] = hotspots == 1
- return df
- df = add_hotspots(df, num_sigs)
- if LABELS is not None:
- df['y_consec_thresh'][df.pid.isin(LABELS['pos'])] = 1 # add manual pos labels
- df['y_consec_thresh'][df.pid.isin(LABELS['neg'])] = 0 # add manual neg labels
- df['hotspots'][df.pid.isin(LABELS['hotspots'])] = True # add manual hotspot labels
- df = add_rule_based_label(df)
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.outcomes.add_rule_based_label"><code class="name flex">
- <span>def <span class="ident">add_rule_based_label</span></span>(<span>df)</span>
- </code></dt>
- <dd>
- <section class="desc"></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def add_rule_based_label(df):
- df['Y_peak_time_frac'] = df['Y_peak_idx'].values / df['lifetime'].values
- df['y_z_score'] = (df['Y_max'].values - df['Y_mean'].values) / df['Y_std'].values
- X_max_around_Y_peak = []
- X_max_after_Y_peak = []
- for i in range(len(df)):
- pt = df['Y_peak_idx'].values[i]
- lt = df['lifetime'].values[i]
- left_bf = np.int(0.2 * lt) + 1 # look at a window with length = 30%*lifetime
- right_bf = np.int(0.1 * lt) + 1
- arr_around = df['X'].iloc[i][max(0, pt - left_bf): min(pt + right_bf, lt)]
- arr_after = df['X'].iloc[i][min(pt + right_bf, lt - 1):]
- X_max_around_Y_peak.append(max(arr_around))
- if len(arr_after) > 0:
- X_max_after_Y_peak.append(max(arr_after))
- else:
- X_max_after_Y_peak.append(max(arr_around))
- df['X_max_around_Y_peak'] = X_max_around_Y_peak
- df['X_max_after_Y_peak'] = X_max_after_Y_peak
- df['X_max_diff'] = df['X_max_around_Y_peak'] - df['X_max_after_Y_peak']
- def rule_based_model(track):
- # three rules:
- # if aux peaks too early -- negative
- # elif:
- # if y_consec_sig or y_conservative_thresh or (cla drops around aux peak, and aux max is greater than
- # mean + 2.6*std), then positive
- # else: negative
- if track['Y_peak_time_frac'] < 0.2:
- return 0
- if track['y_consec_sig'] or track['y_conservative_thresh']:
- return 1
- # if track['X_max_diff'] > 260 and track['y_z_score'] > 2.6:
- # return 1
- if track['X_max_diff'] > 260 and track['Y_max'] > 560:
- return 1
- return 0
- df['y_rule_based'] = np.array([rule_based_model(df.iloc[i]) for i in range(len(df))])
- return df</code></pre>
- </details>
- </dd>
- <dt id="src.outcomes.add_sig_mean"><code class="name flex">
- <span>def <span class="ident">add_sig_mean</span></span>(<span>df, resp_tracks=['Y'])</span>
- </code></dt>
- <dd>
- <section class="desc"><p>add response of regression problem: mean auxilin strength among significant observations</p></section>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def add_sig_mean(df, resp_tracks=['Y']):
- """add response of regression problem: mean auxilin strength among significant observations
- """
- for track in resp_tracks:
- sig_mean = []
- for i in range(len(df)):
- r = df.iloc[i]
- sigs = np.array(r[f'{track}_pvals']) < 0.05
- if sum(sigs)>0:
- sig_mean.append(np.mean(np.array(r[track])[sigs]))
- else:
- sig_mean.append(0)
- df[f'{track}_sig_mean'] = sig_mean
- df[f'{track}_sig_mean_normalized'] = sig_mean
- for cell in set(df['cell_num']):
- cell_idx = np.where(df['cell_num'].values == cell)[0]
- y = df[f'{track}_sig_mean'].values[cell_idx]
- df[f'{track}_sig_mean_normalized'].values[cell_idx] = (y - np.mean(y))/np.std(y)
- return df</code></pre>
- </details>
- </dd>
- </dl>
- </section>
- <section>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index</h1>
- <div class="toc">
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- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="src" href="index.html">src</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-functions">Functions</a></h3>
- <ul class="">
- <li><code><a title="src.outcomes.add_aux_dyn_outcome" href="#src.outcomes.add_aux_dyn_outcome">add_aux_dyn_outcome</a></code></li>
- <li><code><a title="src.outcomes.add_outcomes" href="#src.outcomes.add_outcomes">add_outcomes</a></code></li>
- <li><code><a title="src.outcomes.add_rule_based_label" href="#src.outcomes.add_rule_based_label">add_rule_based_label</a></code></li>
- <li><code><a title="src.outcomes.add_sig_mean" href="#src.outcomes.add_sig_mean">add_sig_mean</a></code></li>
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