Whoop

README.md

@@ -12,6 +12,7 @@ This work is very much inspired by the methods detailed in [High-throughput, non
 - Loading mass spectrometry files from mzML.
   - Support for polarity switching.
   - MAD-estimated infusion profiling.
+- Assay-wide outlier spectrum detection.
 - Spurious peak elimination.
 - Spectrum export for direct dissemination using Metaboanalyst.
 - Spectral binning.
@@ -117,6 +118,13 @@ If you're only using this pipeline to extract mass spectrum for Metabolanalyst,
 
 That being said, this pipeline contains many of the preprocessing methods found in Metaboanalyst - so it may be easier for you to just use ours.
 
+As a diagnostic measure, the TIC can provide an estimation of factos that may adversely affect the overal intensity count of a run. As a rule, it is common to remove spectrum in which the TIC deviates 2/3 times from the median-absolute deviation. We can do this by calling the ```detect_outliers``` method:
+
+```python
+>>> speclist.detect_outliers(thresh = 2, verbose=True)
+Detected Outliers: outlier_one;outlier_two 
+```
+
 A common first step in the analysis of mass-spectrometry data is to bin the data to a given mass-to-ion value. To do this for all ```Spectrum``` held within our ```SpectrumList``` object, simply apply the ```bin``` method:
 
 ```python

dimepy/spectrumList.py

@@ -48,9 +48,48 @@ def append(self, spectrum: Spectrum):
         else:
             raise ValueError("SpectrumList only accepts Spectrum objects.")
 
+    def detect_outliers(self, threshold: float = 1, verbose: bool = False):
+        """
+        Method to locate and remove outlier spectrum using MAD.
+
+        Arguments:
+            threshold (int): Threshold for MAD outlier detection.
+        """
+
+        def _get_tics() -> Tuple[np.array, np.array]:
+            tics = []
+
+            for spec in self._list:
+                tics.append(np.sum(spec.intensities))
+
+            return np.array(tics)
+
+        def _calculate_mad(tics: np.array) -> float:
+            return np.median(np.abs(tics - np.median(tics)))
+
+        def _get_mask(tics: np.array, mad: float) -> np.array:
+            tics = tics[:, None]
+            median = np.median(tics, axis=0)
+            diff = np.sum((tics - median)**2, axis=-1)
+            diff = np.sqrt(diff)
+
+            med_abs_deviation = np.median(diff)
+
+            modified_z_score = 0.6745 * diff / med_abs_deviation
+
+            return modified_z_score <= threshold
+
+        tics = _get_tics()
+        mad = _calculate_mad(tics)
+        to_keep = _get_mask(tics, mad)
+
+        _list = np.array(self._list)
+
+        if verbose:
+            print("Detected Outliers: %s" %
+                  ";".join([x.identifier for x in _list[~to_keep]]))
 
-    def outlier_detection(self):
-        pass
+        self._list = _list[to_keep].tolist()
 
     def bin(self, bin_width: float = 0.5, statistic: str = "mean"):
         """