batch correction #133

HISTORY.md

@@ -4,6 +4,7 @@
 * ADD plot Sample Distribution Histogram
 * ADD paired-ttest option 
 * ENH add option to remove samples in GUI
+* ADD pyComBat Batch correction Behdenna A, Haziza J, Azencot CA and Nordor A. (2020) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431
 
 # 0.4.5
 * FIX loading of Data on Windows

alphastats/DataSet_Preprocess.py

@@ -8,6 +8,7 @@
 from alphastats.utils import ignore_warning
 from sklearn.experimental import enable_iterative_imputer
 import itertools
+from combat.pycombat import pycombat
 
 
 class Preprocess:
@@ -197,17 +198,27 @@ def _log2_transform(self):
         self.mat = np.log2(self.mat + 0.1)
         self.preprocessing_info.update({"Log2-transformed": True})
         print("Data has been log2-transformed.")
+
+    def batch_correction(self, batch:str):
+        """Correct for technical bias/batch effects
+        Behdenna A, Haziza J, Azencot CA and Nordor A. (2020) pyComBat, a Python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. bioRxiv doi: 10.1101/2020.03.17.995431
 
+        Args:
+            batch (str): column name in the metadata describing the different batches
+        """
+        data = self.mat.transpose()
+        series_of_batches = self.metadata.set_index(self.sample).reindex(data.columns.to_list())[batch]
+        self.mat = pycombat(data=data, batch=series_of_batches).transpose()
 
     @ignore_warning(RuntimeWarning)
     def preprocess(
         self,
-        log2_transform=True,
-        remove_contaminations=False,
-        subset=False,
-        normalization=None,
-        imputation=None,
-        remove_samples=None,
+        log2_transform: bool=True,
+        remove_contaminations: bool=False,
+        subset: bool=False,
+        normalization: str=None,
+        imputation: str=None,
+        remove_samples: list=None,
     ):
         """Preprocess Protein data
 

alphastats/gui/pages/03_Preprocessing.py

@@ -12,7 +12,7 @@ def preprocessing():
 
         st.markdown(
             "Before analyzing your data, consider normalizing and imputing your data as well as the removal of contaminants. "
-            + "A more detailed description about the preprocessing methods can be found in the AlphaPeptStats" 
+            + "A more detailed description about the preprocessing methods can be found in the AlphaPeptStats " 
             + "[documentation](https://alphapeptstats.readthedocs.io/en/main/data_preprocessing.html)."
         )
 
@@ -70,6 +70,25 @@ def preprocessing():
                 pd.DataFrame.from_dict(preprocessing, orient="index").astype(str),
                 use_container_width=True,
             )
+
+        st.markdown("#### Batch correction: correct for technical bias")
+
+        with st.form("Batch correction: correct for technical bias"):
+            batch = st.selectbox(
+                "Batch", 
+                options= st.session_state.dataset.metadata.columns.to_list()
+            )
+            submit_batch_correction = st.form_submit_button("Submit")
+
+        if submit_batch_correction:
+            st.session_state.dataset.batch_correction(
+                batch=batch
+            )
+            st.info(
+                "Data has been processed. "
+                + datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S")
+            )
+
 
     with c2:
 

requirements.txt

@@ -18,4 +18,5 @@ numba==0.56.4
 numba-stats==0.5.0
 swifter==1.2.0
 click==8.0.1
-kaleido==0.2.1
+kaleido==0.2.1
+combat==0.3.3

tests/test_DataSet.py

@@ -678,6 +678,13 @@ def test_plot_intensity_sign_001(self):
     def test_plot_samplehistograms(self):
         fig = self.obj.plot_samplehistograms().to_plotly_json()
         self.assertEqual(312, len(fig["data"]))
+
+    def test_batch_correction(self):
+        self.obj.preprocess(subset=True, imputation="knn", normalization="quantile")
+        self.obj.batch_correction(batch="batch_artifical_added")
+        first_value = self.obj.mat.values[0,0]
+        self.assertAlmostEqual(0.0111, first_value, places=2)
+