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ChaozhongLiu committed Nov 19, 2023
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17 changes: 11 additions & 6 deletions docs/_build/html/_modules/treasmo/core.html
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Expand Up @@ -262,7 +262,7 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; </span>
<span class="sd"> Function to calculate Moran&#39;s I for all the features in multiome data</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>

<span class="sd"> mudata: MuData</span>
Expand Down Expand Up @@ -351,7 +351,7 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; </span>
<span class="sd"> Function to calculate the global L index (mean of correlation strength index) for all the pairs in multiome data</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>

<span class="sd"> mudata: MuData</span>
Expand All @@ -362,15 +362,18 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>

<span class="sd"> pairsDf: pandas.DataFrame</span>
<span class="sd"> gene-peak pair DataFrame containing pairs to be calculated</span>

<span class="sd"> self-prepared or called from treasmo.tl.peaks_within_distance / TFBS_match</span>

<span class="sd"> permutations: int</span>
<span class="sd"> Number of permutations for significance test.</span>

<span class="sd"> Default is 0, meaning no significance test</span>
<span class="sd"> 999 is a good choice for most cases, but it might take a long time (hours) to finish depending on the number of pairs</span>
<span class="sd"> </span>
<span class="sd"> percent: float</span>
<span class="sd"> percentage of cells to shuffle during permutation.</span>
<span class="sd"> </span>
<span class="sd"> For most of the time, default 0.1 is already a good choice.</span>
<span class="sd"> </span>
<span class="sd"> seed: int</span>
Expand All @@ -384,8 +387,10 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>
<span class="sd"> --------------</span>
<span class="sd"> DataFrame</span>
<span class="sd"> pairsDf added with extra columns:</span>
<span class="sd"> Global L results</span>
<span class="sd"> QC metrics (feature sparsity)</span>

<span class="sd"> Global L results</span>

<span class="sd"> QC metrics (feature sparsity)</span>

<span class="sd"> &quot;&quot;&quot;</span>

Expand Down Expand Up @@ -462,7 +467,7 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; </span>
<span class="sd"> Function to calculate the single-cell gene-peak correlation strength index for all the pairs in multiome data</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>

<span class="sd"> mudata: MuData</span>
Expand Down Expand Up @@ -563,7 +568,7 @@ <h1>Source code for treasmo.core</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function to calculate the Pearson correlation between genes and peaks</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>

<span class="sd"> mudata: MuData</span>
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41 changes: 23 additions & 18 deletions docs/_build/html/_modules/treasmo/ds.html
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Expand Up @@ -242,7 +242,6 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="kn">import</span> <span class="nn">anndata</span> <span class="k">as</span> <span class="nn">ad</span>
<span class="kn">import</span> <span class="nn">time</span>

<span class="kn">import</span> <span class="nn">treasmo.core</span> <span class="k">as</span> <span class="nn">core</span>
<span class="kn">from</span> <span class="nn">minisom</span> <span class="kn">import</span> <span class="n">MiniSom</span>


Expand Down Expand Up @@ -281,8 +280,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="sd"> Function to discover regulatory gene-peak markers in all groups</span>
<span class="sd"> Target group correlation strength is compared with all remaining groups by t-test.</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
Expand Down Expand Up @@ -355,8 +354,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function to compare regulatory gene-peak pairs between two group by t-test.</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
Expand Down Expand Up @@ -533,8 +532,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function to filter markers from statistical test results by sparsity, correlation difference, and p-value</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> statDf: DataFrame</span>
<span class="sd"> Differentially regulated pairs statistical test results</span>
<span class="sd"> </span>
Expand All @@ -558,7 +557,7 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="sd"> DataFrame</span>
<span class="sd"> Filtered marker list with the same columns as stat_df</span>

<span class="sd"> if plot==True, also return volcano plot</span>
<span class="sd"> if plot==True, also return volcano plot</span>

<span class="sd"> &quot;&quot;&quot;</span>
<span class="n">stat_df</span> <span class="o">=</span> <span class="n">statDf</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
Expand Down Expand Up @@ -599,8 +598,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> One-to-one comparison of gene-peak correlation among groups in the trajectory path by t-test.</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
Expand Down Expand Up @@ -680,10 +679,11 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Helper function to generate bined data along trajectory.</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>

<span class="sd"> It must have correlation strength index calculated.</span>
<span class="sd"> </span>
<span class="sd"> ident: str</span>
Expand All @@ -706,12 +706,14 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="sd"> </span>
<span class="sd"> fitted: int, default is None</span>
<span class="sd"> if an int, return GaussianProcessRegressor fitted data with ``fitted`` bins.</span>

<span class="sd"> if None, return only bined raw data</span>

<span class="sd"> Returns</span>
<span class="sd"> ---------</span>
<span class="sd"> DataFrame</span>
<span class="sd"> bined raw data</span>

<span class="sd"> Optional: GaussianProcessRegressor fitted data</span>

<span class="sd"> &quot;&quot;&quot;</span>
Expand Down Expand Up @@ -765,9 +767,9 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function to calculate bined cell type proportion along trajectory</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
<span class="sd"> ident: str</span>
Expand Down Expand Up @@ -824,7 +826,7 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Detect highly variable gene-peak pairs along the trajectory by correlation strength range (max-min) and variance</span>

<span class="sd"> Arguments</span>
<span class="sd"> Parameters</span>
<span class="sd"> --------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
Expand Down Expand Up @@ -892,8 +894,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Quantify regulatory dynamics along the trajectory for a single gene and its regulatory elements.</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
Expand Down Expand Up @@ -977,8 +979,8 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Function to cluster gene-peak modules by Self-Organizing Map along the trajectory</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> mudata: MuData</span>
<span class="sd"> single-cell multi-omics data saved as MuData object</span>
<span class="sd"> </span>
Expand All @@ -996,8 +998,11 @@ <h1>Source code for treasmo.ds</h1><div class="highlight"><pre>
<span class="sd"> </span>
<span class="sd"> fitted: int</span>
<span class="sd"> number of bins to divide the trajectory into for GaussianProcessRegressor fitted data</span>

<span class="sd"> ``bins`` sets bined data for later plotting;</span>

<span class="sd"> ``fitted`` sets bined data for clustering;</span>
<span class="sd"> </span>
<span class="sd"> It is recommended to keep the two the same</span>
<span class="sd"> </span>
<span class="sd"> num_iteration: int</span>
Expand Down
31 changes: 15 additions & 16 deletions docs/_build/html/_modules/treasmo/lee_vec.html
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Expand Up @@ -247,12 +247,12 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="kn">import</span> <span class="nn">scipy.stats</span>
<span class="kn">import</span> <span class="nn">psutil</span>

<div class="viewcode-block" id="get_memory"><a class="viewcode-back" href="../../api.html#treasmo.lee_vec.get_memory">[docs]</a><span class="k">def</span> <span class="nf">get_memory</span><span class="p">():</span>
<span class="k">def</span> <span class="nf">get_memory</span><span class="p">():</span>
<span class="c1"># Get the virtual memory usage details</span>
<span class="n">memory</span> <span class="o">=</span> <span class="n">psutil</span><span class="o">.</span><span class="n">virtual_memory</span><span class="p">()</span>
<span class="c1"># Calculate the free memory in MB</span>
<span class="n">free_memory_mb</span> <span class="o">=</span> <span class="p">(</span><span class="n">memory</span><span class="o">.</span><span class="n">available</span> <span class="o">/</span> <span class="mf">1024.0</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1024.0</span>
<span class="k">return</span> <span class="n">free_memory_mb</span></div>
<span class="k">return</span> <span class="n">free_memory_mb</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="sd">def get_memory():</span>
<span class="sd"> with open(&#39;/proc/meminfo&#39;, &#39;r&#39;) as mem:</span>
Expand All @@ -277,8 +277,8 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Initialize a spatial pearson correlation estimator</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> connectivity: scipy.sparse matrix object</span>
<span class="sd"> the connectivity structure describing the relationships</span>
<span class="sd"> between observed units. Will be row-standardized. </span>
Expand All @@ -288,7 +288,7 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="sd"> if &lt; 1, no permutational inference will be conducted. </span>

<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> ------------</span>
<span class="sd"> association_: numpy.ndarray (p,)</span>
<span class="sd"> array containg the estimated Lee spatial pearson correlation</span>
<span class="sd"> coefficients for all gene-peak pairs</span>
Expand Down Expand Up @@ -320,8 +320,8 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> bivariate spatial pearson&#39;s R based on Eq. 18 of :cite:`Lee2001`.</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> X: numpy.ndarray [n x p]</span>
<span class="sd"> array containing continuous data</span>
<span class="sd"> Y: numpy.ndarray [n x p]</span>
Expand All @@ -339,11 +339,11 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>


<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> ------------</span>
<span class="sd"> the fitted estimator.</span>

<span class="sd"> Notes</span>
<span class="sd"> -----</span>
<span class="sd"> ------------</span>
<span class="sd"> Technical details and derivations can be found in :cite:`Lee2001`.</span>

<span class="sd"> &quot;&quot;&quot;</span>
Expand Down Expand Up @@ -408,8 +408,7 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>


<div class="viewcode-block" id="Spatial_Pearson_Local"><a class="viewcode-back" href="../../api.html#treasmo.lee_vec.Spatial_Pearson_Local">[docs]</a><span class="k">class</span> <span class="nc">Spatial_Pearson_Local</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Local Spatial Pearson Correlation Statistic;</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; </span>
<span class="sd"> Single-cell gene-peak correlation strength index.</span>

<span class="sd"> Adapted and vectorized from esda library</span>
Expand All @@ -420,8 +419,8 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> Initialize a spatial local pearson estimator</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> connectivity: scipy.sparse matrix object</span>
<span class="sd"> the connectivity structure describing the relationships</span>
<span class="sd"> between observed units. Will be row-standardized. </span>
Expand All @@ -432,7 +431,7 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>


<span class="sd"> Attributes</span>
<span class="sd"> ----------</span>
<span class="sd"> ------------</span>
<span class="sd"> associations_: numpy.ndarray (n_samples,)</span>
<span class="sd"> array containg the estimated Lee spatial pearson correlation</span>
<span class="sd"> coefficients, where element [0,1] is the spatial correlation</span>
Expand Down Expand Up @@ -461,8 +460,8 @@ <h1>Source code for treasmo.lee_vec</h1><div class="highlight"><pre>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> bivariate local pearson&#39;s R based on Eq. 22 in Lee (2001)</span>

<span class="sd"> Arguments</span>
<span class="sd"> ---------</span>
<span class="sd"> Parameters</span>
<span class="sd"> ------------</span>
<span class="sd"> X: numpy.ndarray [n x p]</span>
<span class="sd"> array containing continuous data</span>
<span class="sd"> Y: numpy.ndarray [n x p]</span>
Expand Down
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