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<div class="section" id="module-maui.utils">
<span id="maui-utilities"></span><h1>Maui Utilities<a class="headerlink" href="#module-maui.utils" title="Permalink to this headline">¶</a></h1>
<p>The maui.utils model contains utility functions for multi-omics analysis
using maui.</p>
<dl class="function">
<dt id="maui.utils.compute_harrells_c">
<code class="descclassname">maui.utils.</code><code class="descname">compute_harrells_c</code><span class="sig-paren">(</span><em>z, survival, duration_column='duration', observed_column='observed', cox_penalties=[0.1, 1, 10, 100, 1000, 10000], cv_folds=5</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#compute_harrells_c"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.compute_harrells_c" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute’s Harrell’s c-Index for a Cox Proportional Hazards regression modeling
survival by the latent factors in z.</p>
<p>z: pd.DataFrame (n_samples, n_latent factors)
survival: pd.DataFrame of survival information and relevant covariates</p>
<blockquote>
<div>(such as sex, age at diagnosis, or tumor stage)</div></blockquote>
<dl class="docutils">
<dt>duration_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> containing the</dt>
<dd>duration (time between diagnosis and death or last followup)</dd>
<dt>observed_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> containing</dt>
<dd>indicating whether time of death is known</dd>
<dt>cox_penalties: penalty coefficient in Cox PH solver (see <code class="docutils literal notranslate"><span class="pre">lifelines.CoxPHFitter</span></code>)</dt>
<dd>to try. Returns the best c given by the different penalties
(by cross-validation)</dd>
</dl>
<p>cv_folds: number of cross-validation folds to compute C</p>
<dl class="docutils">
<dt>cs: array, Harrell’s c-Index, an auc-like metric for survival prediction accuracy.</dt>
<dd>one value per cv_fold</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.compute_roc">
<code class="descclassname">maui.utils.</code><code class="descname">compute_roc</code><span class="sig-paren">(</span><em>z</em>, <em>y</em>, <em>classifier=LinearSVC(C=0.001</em>, <em>class_weight=None</em>, <em>dual=True</em>, <em>fit_intercept=True</em>, <em>intercept_scaling=1</em>, <em>loss='squared_hinge'</em>, <em>max_iter=1000</em>, <em>multi_class='ovr'</em>, <em>penalty='l2'</em>, <em>random_state=None</em>, <em>tol=0.0001</em>, <em>verbose=0)</em>, <em>cv_folds=10</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#compute_roc"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.compute_roc" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the ROC (false positive rate, true positive rate) using cross-validation.</p>
<p>z: DataFrame (n_samples, n_latent_factors) of latent factor values
y: Series (n_samples,) of ground-truth labels to try to predict
classifier: Classifier object to use, default <code class="docutils literal notranslate"><span class="pre">LinearSVC(C=.001)</span></code></p>
<dl class="docutils">
<dt>roc_curves: dict, one key per class as well as “mean”, each value is a dataframe</dt>
<dd>containing the tpr (true positive rate) and fpr (falce positive rate)
defining that class (or the mean) ROC.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.correlate_factors_and_features">
<code class="descclassname">maui.utils.</code><code class="descname">correlate_factors_and_features</code><span class="sig-paren">(</span><em>z</em>, <em>concatenated_data</em>, <em>pval_threshold=0.001</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#correlate_factors_and_features"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.correlate_factors_and_features" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute pearson correlation of latent factors with input features.</p>
<p>z: (n_samples, n_factors) DataFrame of latent factor values, output of maui model
concatenated_data: (n_samples, n_features) DataFrame of concatenated multi-omics data</p>
<dl class="docutils">
<dt>feature_s: DataFrame (n_features, n_latent_factors)</dt>
<dd>Latent factors representation of the data X.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.estimate_kaplan_meier">
<code class="descclassname">maui.utils.</code><code class="descname">estimate_kaplan_meier</code><span class="sig-paren">(</span><em>y</em>, <em>survival</em>, <em>duration_column='duration'</em>, <em>observed_column='observed'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#estimate_kaplan_meier"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.estimate_kaplan_meier" title="Permalink to this definition">¶</a></dt>
<dd><p>Estimate survival curves for groups defined in y based on survival data in <code class="docutils literal notranslate"><span class="pre">survival</span></code></p>
<dl class="docutils">
<dt>y: pd.Series, groups (clusters, subtypes). the index is</dt>
<dd>the sample names</dd>
<dt>survival: pd.DataFrame with the same index as y, with columns for</dt>
<dd>the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.</dd>
</dl>
<p>duration_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> with the duration
observed_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> with True/False values</p>
<blockquote>
<div>for whether death was observed or not</div></blockquote>
<dl class="docutils">
<dt>km_estimates: pd.DataFrame, index is the timeline, columns are survival</dt>
<dd>functions (estimated by Kaplan-Meier) for each class, as
defined in <code class="docutils literal notranslate"><span class="pre">y</span></code>.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.filter_factors_by_r2">
<code class="descclassname">maui.utils.</code><code class="descname">filter_factors_by_r2</code><span class="sig-paren">(</span><em>z</em>, <em>x</em>, <em>threshold=0.02</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#filter_factors_by_r2"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.filter_factors_by_r2" title="Permalink to this definition">¶</a></dt>
<dd><p>Filter latent factors by the R^2 of a linear model predicting features x
from latent factors z.</p>
<p>z: (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x: (n_samples, n_features) DataFrame of concatenated multi-omics data</p>
<dl class="docutils">
<dt>z_filtered: (n_samples, n_factors) DataFrame of latent factor values,</dt>
<dd>with only those columns from the input <cite>z</cite> which have an R^2
above the threshold when using that column as an input
to a linear model predicting <cite>x</cite>.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.map_factors_to_feaures_using_linear_models">
<code class="descclassname">maui.utils.</code><code class="descname">map_factors_to_feaures_using_linear_models</code><span class="sig-paren">(</span><em>z</em>, <em>x</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#map_factors_to_feaures_using_linear_models"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.map_factors_to_feaures_using_linear_models" title="Permalink to this definition">¶</a></dt>
<dd><p>Get feature <-> latent factors mapping from linear models.
Runs one univariate (multi-output) linear model per latent factor in <cite>z</cite>,
predicting the values of the features <cite>x</cite>, in order to get weights
between inputs and outputs.</p>
<p>z: (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
x: (n_samples, n_features) DataFrame of concatenated multi-omics data</p>
<dl class="docutils">
<dt>W: (n_features, n_latent_factors) DataFrame</dt>
<dd>w_{ij} is the coefficient associated with feature <cite>i</cite> in a linear model
predicting it from latent factor <cite>j</cite>.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.merge_factors">
<code class="descclassname">maui.utils.</code><code class="descname">merge_factors</code><span class="sig-paren">(</span><em>z</em>, <em>l=None</em>, <em>threshold=0.17</em>, <em>merge_fn=<function mean></em>, <em>metric='correlation'</em>, <em>linkage='single'</em>, <em>plot_dendro=True</em>, <em>plot_dendro_ax=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#merge_factors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.merge_factors" title="Permalink to this definition">¶</a></dt>
<dd><p>Merge latent factors in <cite>z</cite> which form clusters, as defined by hierarchical
clustering where a cluster is formed by cutting at a pre-set threshold, i.e.
merge factors if their distance to one-another is below <cite>threshold</cite>.</p>
<p>z: (n_samples, n_factors) DataFrame of latent factor values, output of a maui model
metric: Distance metric to merge factors by, one which is supported by</p>
<blockquote>
<div><code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.spatial.distance.pdist()</span></code></div></blockquote>
<dl class="docutils">
<dt>linkage: The kind of linkage to form hierarchical clustering, one which is</dt>
<dd>supported by <code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.cluster.hierarchy.linkage()</span></code></dd>
<dt>l: As an alternative to supplying <cite>metric</cite> and <cite>linkage</cite>, supply a</dt>
<dd>linkage matrix of your own choice, such as one computed by
<code class="xref py py-func docutils literal notranslate"><span class="pre">scipy.cluster.hierarchy.linkage()</span></code></dd>
<dt>threshold: The distance threshold. latent factors with similarity below the</dt>
<dd>threshold will be merged to form single latent facator</dd>
<dt>merge_fn: A function which will be used to merge latent factors. The default</dt>
<dd>is <code class="xref py py-func docutils literal notranslate"><span class="pre">numpy.mean()</span></code>, i.e. the newly formed (merged) latent factor
will be the mean of the merged ones. Supply any function here which
has the same interface, i.e. takes a matrix and an axis.</dd>
<dt>plot_dendro: Boolean. If True, the function will plot a dendrogram showing</dt>
<dd>which latent factors are merged and the threshold.</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="maui.utils.multivariate_logrank_test">
<code class="descclassname">maui.utils.</code><code class="descname">multivariate_logrank_test</code><span class="sig-paren">(</span><em>y</em>, <em>survival</em>, <em>duration_column='duration'</em>, <em>observed_column='observed'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#multivariate_logrank_test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.multivariate_logrank_test" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the multivariate log-rank test for differential survival
among the groups defined by <code class="docutils literal notranslate"><span class="pre">y</span></code> in the survival data in <code class="docutils literal notranslate"><span class="pre">survival</span></code>,
under the null-hypothesis that all groups have the same survival function
(i.e. test whether at least one group has different survival rates)</p>
<dl class="docutils">
<dt>y: pd.Series, groups (clusters, subtypes). the index is</dt>
<dd>the sample names</dd>
<dt>survival: pd.DataFrame with the same index as y, with columns for</dt>
<dd>the duration (survival time for each patient) and whether
or not the death was observed. If the death was not
observed (sensored), the duration is the time of the last
followup.</dd>
</dl>
<p>duration_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> with the duration
observed_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> with True/False values</p>
<blockquote>
<div>for whether death was observed or not</div></blockquote>
<p>test_statistic: the test statistic (chi-square)
p_value: the associated p_value</p>
</dd></dl>
<dl class="function">
<dt id="maui.utils.scale">
<code class="descclassname">maui.utils.</code><code class="descname">scale</code><span class="sig-paren">(</span><em>df</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.scale" title="Permalink to this definition">¶</a></dt>
<dd><p>Scale and center data</p>
<p>df: pd.DataFrame (n_features, n_samples) non-scaled data</p>
<p>scaled: pd.DataFrame (n_features, n_samples) scaled data</p>
</dd></dl>
<dl class="function">
<dt id="maui.utils.select_clinical_factors">
<code class="descclassname">maui.utils.</code><code class="descname">select_clinical_factors</code><span class="sig-paren">(</span><em>z</em>, <em>survival</em>, <em>duration_column='duration'</em>, <em>observed_column='observed'</em>, <em>alpha=0.05</em>, <em>cox_penalizer=0</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/maui/utils.html#select_clinical_factors"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#maui.utils.select_clinical_factors" title="Permalink to this definition">¶</a></dt>
<dd><p>Select latent factors which are predictive of survival. This is
accomplished by fitting a Cox Proportional Hazards (CPH) model to each
latent factor, while controlling for known covariates, and only keeping
those latent factors whose coefficient in the CPH is nonzero (adjusted
p-value < alpha).</p>
<dl class="docutils">
<dt>survival: pd.DataFrame of survival information and relevant covariates</dt>
<dd>(such as sex, age at diagnosis, or tumor stage)</dd>
<dt>duration_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> containing the</dt>
<dd>duration (time between diagnosis and death or last followup)</dd>
<dt>observed_column: the name of the column in <code class="docutils literal notranslate"><span class="pre">survival</span></code> containing</dt>
<dd>indicating whether time of death is known</dd>
<dt>alpha: threshold for p-value of CPH coefficients to call a latent</dt>
<dd>factor clinically relevant (p < alpha)</dd>
</dl>
<p>cox_penalizer: penalty coefficient in Cox PH solver (see <code class="docutils literal notranslate"><span class="pre">lifelines.CoxPHFitter</span></code>)</p>
<dl class="docutils">
<dt>z_clinical: pd.DataFrame, subset of the latent factors which have been</dt>
<dd>determined to have clinical value (are individually predictive
of survival, controlling for covariates)</dd>
</dl>
</dd></dl>
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Datasets
Models
