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nargesr committed Jan 18, 2024
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4 changes: 4 additions & 0 deletions README.md
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Expand Up @@ -41,6 +41,8 @@ git cloning the [Topyfic repository](https://github.com/mortazavilab/Topyfic), g

In general, you need to make three objects (Train, TopModel and Analysis).

![Topyfic workflow](docs/Topyfic_workflow.png)

The Train object can be initialized either from (a) single cell RNA-seq dataset or (b) single cell ATAC-seq or (c) bulk RNA-seq.

Training part can be time-consuming depending on how big your data is, however you can learn each train model per random state in different jobs and then combine all together. Look at [this tutorial](tutorials/make_train_object.ipynb) for mor information.
Expand All @@ -49,6 +51,8 @@ For guidance on using Topyfic to analyze your data look at our more depth-in tut

- [Analysing single cell C2C12 data only using regulatory elements](tutorials/C2C12_TFs_mirhgs_chromreg/C2C12.ipynb): Analysing single cell and single nucleus using C2C12 ENCODE datasets using regulatory elements instead of all genes.
- [Analysing single cell microglia data](tutorials/microglia_all_genes/microglia.ipynb): Analysing single cell microglia data from [Model-AD portal](https://www.model-ad.org/).
- [Analysing ENCODE time course hippocampus data](tutorials/ENCODE_Hipp_parse_10x/analysing.ipynb): Analysing parse single-nucleus RNA-seq data and RNA part of 10x multiome hippocampus data from ENCODE.


If you are using other methods to learn your topics but you are still interested in doing downstream analysis, you can embeded your results in the format describe [here](tutorials/topic_modeling_model.md). Once you have all your files ready you can embed them Topyfic format following the instruction in the same [tutorial](tutorials/topic_modeling_model.md).

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6 changes: 4 additions & 2 deletions Topyfic/analysis.py
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Expand Up @@ -172,7 +172,7 @@ def my_autopct(pct):

def structure_plot(self,
level,
category,
category=None,
topic_order=None,
ascending=None,
metaData=None,
Expand Down Expand Up @@ -217,6 +217,8 @@ def structure_plot(self,
:param file_name: name and path of the plot use for save (default: piechart_topicAvgCell)
:type file_name: str
"""
if category is None:
category = self.cell_participation.obs[level].unique().tolist()
if figsize is None:
figsize = (10 * (len(category) + 1), 10)

Expand Down Expand Up @@ -325,7 +327,7 @@ def structure_plot(self,
axs[0, i].set_title(category[i], fontsize=40)
axs[0, i].set_ylim(0, 1)
axs[0, i].set_xlim(0, a[i])
axs[0, 0].set_ylabel("Topic proportion", fontsize=25)
axs[0, 0].set_ylabel("Topic proportion", fontsize=30)

tissue = tissue[metaData]
tissue = tissue.reindex(tmp.index.tolist())
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10 changes: 4 additions & 6 deletions docs/html/_modules/Topyfic/topic.html
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Expand Up @@ -287,16 +287,14 @@ <h1>Source code for Topyfic.topic</h1><div class="highlight"><pre>
<span class="sd"> :type save: bool</span>
<span class="sd"> &quot;&quot;&quot;</span>

<span class="c1"># check require columns</span>
<span class="n">cols</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gene_information</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">columns</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">{</span><span class="s1">&#39;gene_name&#39;</span><span class="p">,</span> <span class="s1">&#39;gene_id&#39;</span><span class="p">}</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">cols</span><span class="p">):</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Gene information doesn&#39;t contain gene_name and gene_id columns!&quot;</span><span class="p">)</span>

<span class="c1"># Open the file and load the file</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_yaml_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">model_yaml</span> <span class="o">=</span> <span class="n">yaml</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">Loader</span><span class="o">=</span><span class="n">SafeLoader</span><span class="p">)</span>

<span class="k">if</span> <span class="n">topic_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model_yaml</span><span class="p">[</span><span class="s1">&#39;Topic IDs&#39;</span><span class="p">]:</span>
<span class="k">if</span> <span class="n">topic_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">topic_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">id</span>

<span class="k">if</span> <span class="n">topic_id</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model_yaml</span><span class="p">[</span><span class="s1">&#39;Topic file_name(s)&#39;</span><span class="p">]:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="s2">&quot;Topic_id is not in model YAML file!&quot;</span><span class="p">)</span>

<span class="n">topic_yaml</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;Topic ID&#39;</span><span class="p">:</span> <span class="n">topic_id</span><span class="p">,</span>
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55 changes: 43 additions & 12 deletions docs/html/_modules/Topyfic/train.html
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Expand Up @@ -116,6 +116,7 @@ <h1>Source code for Topyfic.train</h1><div class="highlight"><pre>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">repeat</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">LatentDirichletAllocation</span>
<span class="kn">import</span> <span class="nn">h5py</span>

<span class="kn">from</span> <span class="nn">Topyfic.topModel</span> <span class="kn">import</span> <span class="n">TopModel</span>

Expand Down Expand Up @@ -301,23 +302,53 @@ <h1>Source code for Topyfic.train</h1><div class="highlight"><pre>

<span class="k">return</span> <span class="n">all_components</span><span class="p">,</span> <span class="n">all_exp_dirichlet_component</span><span class="p">,</span> <span class="n">all_others</span></div>

<div class="viewcode-block" id="Train.save_train"><a class="viewcode-back" href="../../api.html#Topyfic.train.Train.save_train">[docs]</a> <span class="k">def</span> <span class="nf">save_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">):</span>
<div class="viewcode-block" id="Train.save_train"><a class="viewcode-back" href="../../api.html#Topyfic.train.Train.save_train">[docs]</a> <span class="k">def</span> <span class="nf">save_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="n">file_format</span><span class="o">=</span><span class="s1">&#39;pickle&#39;</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="sd"> save Train class as a pickle file</span>

<span class="sd"> :param name: name of the pickle file (default is train_Train.name)</span>
<span class="sd"> :type name: str</span>
<span class="sd"> :param save_path: directory you want to use to save pickle file (default is saving near script)</span>
<span class="sd"> :type save_path: str</span>
<span class="sd"> save Train class as a pickle file</span>

<span class="sd"> :param name: name of the pickle file (default is train_Train.name)</span>
<span class="sd"> :type name: str</span>
<span class="sd"> :param save_path: directory you want to use to save pickle file (default is saving near script)</span>
<span class="sd"> :type save_path: str</span>
<span class="sd"> :param file_format: format of the file you want to save (option: pickle (default), HDF5)</span>
<span class="sd"> :type file_format: str</span>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="k">if</span> <span class="n">file_format</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;pickle&#39;</span><span class="p">,</span> <span class="s1">&#39;HDF5&#39;</span><span class="p">]:</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">file_format</span><span class="si">}</span><span class="s2"> is not correct! It should be &#39;pickle&#39; or &#39;HDF5&#39;.&quot;</span><span class="p">)</span>
<span class="k">if</span> <span class="n">name</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;train_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving train class as </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.p&quot;</span><span class="p">)</span>

<span class="n">picklefile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">save_path</span><span class="si">}{</span><span class="n">name</span><span class="si">}</span><span class="s2">.p&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">picklefile</span><span class="p">)</span>
<span class="n">picklefile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div></div>
<span class="k">if</span> <span class="n">file_format</span> <span class="o">==</span> <span class="s2">&quot;pickle&quot;</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving train as </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.p&quot;</span><span class="p">)</span>

<span class="n">picklefile</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">save_path</span><span class="si">}{</span><span class="n">name</span><span class="si">}</span><span class="s2">.p&quot;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">picklefile</span><span class="p">)</span>
<span class="n">picklefile</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="k">if</span> <span class="n">file_format</span> <span class="o">==</span> <span class="s2">&quot;HDF5&quot;</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Saving train as </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.h5&quot;</span><span class="p">)</span>

<span class="n">f</span> <span class="o">=</span> <span class="n">h5py</span><span class="o">.</span><span class="n">File</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">.h5&quot;</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span>

<span class="c1"># models</span>
<span class="n">models</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="s2">&quot;models&quot;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">)):</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">create_group</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;components_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">components_</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;exp_dirichlet_component_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">exp_dirichlet_component_</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;n_batch_iter_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">n_batch_iter_</span><span class="p">)</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;n_features_in_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">n_features_in_</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;n_iter_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">n_iter_</span><span class="p">)</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;bound_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">bound_</span><span class="p">)</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;doc_topic_prior_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">doc_topic_prior_</span><span class="p">)</span>
<span class="n">model</span><span class="p">[</span><span class="s1">&#39;topic_word_prior_&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">top_models</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">topic_word_prior_</span><span class="p">)</span>

<span class="n">f</span><span class="p">[</span><span class="s1">&#39;name&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">string_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
<span class="n">f</span><span class="p">[</span><span class="s1">&#39;k&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
<span class="n">f</span><span class="p">[</span><span class="s1">&#39;n_runs&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">int_</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_runs</span><span class="p">)</span>
<span class="n">f</span><span class="p">[</span><span class="s1">&#39;random_state_range&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state_range</span><span class="p">))</span>

<span class="n">f</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div></div>
</pre></div>

</div>
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