minor

gseapy/base.py

@@ -164,9 +164,8 @@ def _load_ranking(self, rnk: Union[pd.DataFrame, pd.Series, str]) -> pd.Series:
         if rank_metric.select_dtypes(np.number).shape[1] > 1:
             return rank_metric
         # sort ranking values from high to low
-        rank_metric.sort_values(
-            by=rank_metric.columns[1], ascending=self.ascending, inplace=True
-        )
+        rnk_cols = rank_metric.columns
+        rank_metric.sort_values(by=rnk_cols[1], ascending=self.ascending, inplace=True)
         # drop na values
         if rank_metric.isnull().any(axis=1).sum() > 0:
             self._logger.warning(
@@ -177,16 +176,23 @@ def _load_ranking(self, rnk: Union[pd.DataFrame, pd.Series, str]) -> pd.Series:
             self._logger.debug("NAs list:\n" + NAs.to_string())
             rank_metric.dropna(how="any", inplace=True)
         # drop duplicate IDs, keep the first
-        if rank_metric.duplicated(subset=rank_metric.columns[0]).sum() > 0:
-            self._logger.warning(
-                "Input gene rankings contains duplicated IDs, Only use the duplicated ID with highest value!"
+        if rank_metric.duplicated(subset=rnk_cols[0]).sum() > 0:
+            self._logger.info("Input gene rankings contains duplicated IDs")
+            mask = rank_metric.duplicated(subset=rnk_cols[0]).duplicated(keep=False)
+            dups = (
+                rank_metric[mask]
+                .groupby(rnk_cols[0])
+                .cumcount()
+                .map(lambda c: "_" + str(c) if c else "")
             )
-            # print out duplicated IDs.
-            dups = rank_metric[rank_metric.duplicated(subset=rank_metric.columns[0])]
-            self._logger.debug("Dups list:\n" + dups.to_string())
-            rank_metric.drop_duplicates(
-                subset=rank_metric.columns[0], inplace=True, keep="first"
+            rank_metric.loc[mask, rnk_cols[0]] = (
+                rank_metric.loc[mask, rnk_cols[0]] + dups
             )
+            # dups = rank_metric[rank_metric.duplicated(subset=rnk_cols[0])]
+            # self._logger.debug("Dups list:\n" + dups.to_string())
+            # rank_metric.drop_duplicates(
+            #     subset=rank_metric.columns[0], inplace=True, keep="first"
+            # )
         # reset ranking index, because you have sort values and drop duplicates.
         rank_metric.reset_index(drop=True, inplace=True)
         rank_metric.columns = ["gene_name", "prerank"]
@@ -609,7 +615,7 @@ def to_df(
             res_df["NES"].abs().sort_values(ascending=False).index
         ).reset_index(drop=True)
         res_df.drop(dc, axis=1, inplace=True)
-        
+
         if self._outdir is not None:
             out = os.path.join(
                 self.outdir,
@@ -751,7 +757,7 @@ def plot(
         ofname: savefig
         """
         # if hasattr(self, "results"):
-        if self.module == "ssgsea":
+        if self.module in ["ssgsea", "gsva"]:
             raise NotImplementedError("not for ssgsea")
         keys = list(self._results.keys())
         if len(keys) > 1:

gseapy/gsea.py

@@ -97,13 +97,6 @@ def load_data(self, cls_vec: List[str]) -> Tuple[pd.DataFrame, Dict]:
         else:
             raise Exception("Error parsing gene expression DataFrame!")
 
-        # drop duplicated gene names
-        if exprs.iloc[:, 0].duplicated().sum() > 0:
-            self._logger.warning(
-                "Dropping duplicated gene names, only keep the first values"
-            )
-            # drop duplicate gene_names.
-            exprs.drop_duplicates(subset=exprs.columns[0], inplace=True)
         if exprs.isnull().any().sum() > 0:
             self._logger.warning("Input data contains NA, filled NA with 0")
             exprs.dropna(how="all", inplace=True)  # drop rows with all NAs
@@ -113,6 +106,12 @@ def load_data(self, cls_vec: List[str]) -> Tuple[pd.DataFrame, Dict]:
         # select numberic columns
         df = exprs.select_dtypes(include=[np.number])
 
+        if exprs.index.duplicated().sum() > 0:
+            self._logger.warning(
+                "Found duplicated gene names, values averaged by gene names!"
+            )
+            exprs = exprs.groupby(level=0).mean()
+
         # in case the description column is numeric
         if len(cls_vec) == (df.shape[1] - 1):
             df = df.iloc[:, 1:]
@@ -581,16 +580,16 @@ def load_data(self) -> pd.DataFrame:
             rank_metric = rank_metric.select_dtypes(include=[np.number])
         else:
             raise Exception("Error parsing gene ranking values!")
-        if rank_metric.index.duplicated().sum() > 0:
-            self._logger.warning(
-                "Dropping duplicated gene names, values averaged by gene names!"
-            )
-            rank_metric = rank_metric.loc[rank_metric.index.dropna()]
-            rank_metric = rank_metric.groupby(level=0).mean()
+
         if rank_metric.isnull().any().sum() > 0:
             self._logger.warning("Input data contains NA, filled NA with 0")
             rank_metric = rank_metric.fillna(0)
 
+        if rank_metric.index.duplicated().sum() > 0:
+            self._logger.warning(
+                "Found duplicated gene names, values averaged by gene names!"
+            )
+            rank_metric = rank_metric.groupby(level=0).mean()
         return rank_metric
 
     def norm_samples(self, dat: pd.DataFrame) -> pd.DataFrame:
@@ -679,7 +678,6 @@ def runSamplesPermu(
         return
 
 
-
 class Replot(GSEAbase):
     """To reproduce GSEA desktop output results."""
 

gseapy/gsva.py

@@ -47,10 +47,10 @@ def __init__(
         self.ranking = None
         self.permutation_num = 0
         self._noplot = True
-        if kcdf == "Gaussian":
+        if kcdf in ["Gaussian", "gaussian"]:
             self.kernel = True
             self.rnaseq = False
-        elif kcdf == "Poisson":
+        elif kcdf in ["Poisson", "poisson"]:
             self.kernel = True
             self.rnaseq = True
         else:
@@ -106,15 +106,16 @@ def load_data(self) -> pd.DataFrame:
             rank_metric = rank_metric.select_dtypes(include=[np.number])
         else:
             raise Exception("Error parsing gene ranking values!")
+
+        if rank_metric.isnull().any().sum() > 0:
+            self._logger.warning("Input data contains NA, filled NA with 0")
+            rank_metric = rank_metric.fillna(0)
+
         if rank_metric.index.duplicated().sum() > 0:
             self._logger.warning(
-                "Dropping duplicated gene names, values averaged by gene names!"
+                "Found duplicated gene names, values averaged by gene names!"
             )
-            rank_metric = rank_metric.loc[rank_metric.index.dropna()]
             rank_metric = rank_metric.groupby(level=0).mean()
-        if rank_metric.isnull().any().sum() > 0:
-            self._logger.warning("Input data contains NA, filled NA with 0")
-            rank_metric = rank_metric.fillna(0)
 
         return rank_metric
 
@@ -125,13 +126,7 @@ def run(self):
         # load data
         df = self.load_data()
         if self.rnaseq:
-            self._logger.debug(
-                "Poisson kernel selected. round input values to intergers!"
-            )
-            df = df.astype(int)
-            self._logger.debug(
-                "Poisson kernel selected. convert negative values to 0 !"
-            )
+            self._logger.info("Poisson kernel selected. Clip negative values to 0 !")
             df = df.clip(lower=0)
 
         self.data = df