diff --git a/gseapy/__init__.py b/gseapy/__init__.py
index 11f90a9..da7409d 100644
--- a/gseapy/__init__.py
+++ b/gseapy/__init__.py
@@ -21,7 +21,7 @@ def gsea(
     min_size: int = 15,
     max_size: int = 500,
     permutation_num: int = 1000,
-    weighted_score_type: float = 1.0,
+    weight: float = 1.0,
     permutation_type: str = "phenotype",
     method: str = "signal_to_noise",
     ascending: bool = False,
@@ -32,8 +32,8 @@ def gsea(
     no_plot: bool = False,
     seed: int = 123,
     verbose: bool = False,
-    *arg,
-    **kwarg,
+    *args,
+    **kwargs,
 ) -> GSEA:
     """Run Gene Set Enrichment Analysis.
 
@@ -55,7 +55,7 @@ def gsea(
 
     :param int max_size: Maximum allowed number of genes from gene set also the data set. Default: 500.
 
-    :param float weighted_score_type: Refer to :func:`algorithm.enrichment_score`. Default:1.
+    :param float weight: Refer to :func:`algorithm.enrichment_score`. Default:1.
 
     :param method:  The method used to calculate a correlation or ranking. Default: 'log2_ratio_of_classes'.
                    Others methods are:
@@ -124,9 +124,14 @@ def gsea(
 
 
     """
-    if "processes" in kwarg:
+    if "processes" in kwargs:
         warnings.warn("processes is deprecated; use threads", DeprecationWarning, 2)
-        threads = kwarg["processes"]
+        threads = kwargs["processes"]
+    if "weighted_score_type" in kwargs:
+        warnings.warn(
+            "weighted_score_type is deprecated; use weight", DeprecationWarning, 2
+        )
+        weight = kwargs["weighted_score_type"]
 
     gs = GSEA(
         data,
@@ -136,7 +141,7 @@ def gsea(
         min_size,
         max_size,
         permutation_num,
-        weighted_score_type,
+        weight,
         permutation_type,
         method,
         ascending,
@@ -162,7 +167,7 @@ def ssgsea(
     min_size: int = 15,
     max_size: int = 500,
     permutation_num: Optional[int] = None,
-    weighted_score_type: float = 0.25,
+    weight: float = 0.25,
     ascending: bool = False,
     threads: int = 4,
     figsize: Tuple[float, float] = (6.5, 6),
@@ -195,8 +200,8 @@ def ssgsea(
     :param str correl_norm_type: correlation normalization type. Choose from {'rank', 'symrank', 'zscore'}. Default: rank.
             After ordering genes by sample_norm_method, further data transformed could be applied to get enrichment score.
 
-            when weighted_score_type==0, sample_norm_method and correl_norm_type do not matter;
-            when weighted_score_type > 0, the combination of sample_norm_method and correl_norm_type
+            when weight==0, sample_norm_method and correl_norm_type do not matter;
+            when weight > 0, the combination of sample_norm_method and correl_norm_type
             dictate how the gene expression values in input data are transformed
             to obtain the score -- use this setting with care (the transformations
             can skew scores towards +ve or -ve values)
@@ -216,7 +221,7 @@ def ssgsea(
     :param int permutation_num: For ssGSEA, default is 0.
                                 However, if you try to use ssgsea method to get pval and fdr, set to an interger.
 
-    :param str weighted_score_type: Refer to :func:`algorithm.enrichment_score`. Default:0.25.
+    :param str weight: Refer to :func:`algorithm.enrichment_score`. Default:0.25.
 
     :param bool ascending: Sorting order of rankings. Default: False.
 
@@ -257,6 +262,12 @@ def ssgsea(
     if "processes" in kwargs:
         warnings.warn("processes is deprecated; use threads", DeprecationWarning, 2)
         threads = kwargs["processes"]
+    if "weighted_score_type" in kwargs:
+        warnings.warn(
+            "weighted_score_type is deprecated; use weight", DeprecationWarning, 2
+        )
+        weight = kwargs["weighted_score_type"]
+
     ss = SingleSampleGSEA(
         data=data,
         gene_sets=gene_sets,
@@ -266,7 +277,7 @@ def ssgsea(
         min_size=min_size,
         max_size=max_size,
         permutation_num=permutation_num,
-        weight=weighted_score_type,
+        weight=weight,
         ascending=ascending,
         threads=threads,
         figsize=figsize,
@@ -289,7 +300,7 @@ def prerank(
     min_size: int = 15,
     max_size: int = 500,
     permutation_num: int = 1000,
-    weighted_score_type: float = 1.0,
+    weight: float = 1.0,
     ascending: bool = False,
     threads: int = 4,
     figsize: Tuple[float, float] = (6.5, 6),
@@ -299,7 +310,7 @@ def prerank(
     seed: int = 123,
     verbose: bool = False,
     *arg,
-    **kwarg,
+    **kwargs,
 ) -> Prerank:
     """Run Gene Set Enrichment Analysis with pre-ranked correlation defined by user.
 
@@ -316,7 +327,7 @@ def prerank(
 
     :param int max_size: Maximum allowed number of genes from gene set also the data set. Defaults: 500.
 
-    :param str weighted_score_type: Refer to :func:`algorithm.enrichment_score`. Default:1.
+    :param str weight: Refer to :func:`algorithm.enrichment_score`. Default:1.
 
     :param bool ascending: Sorting order of rankings. Default: False.
 
@@ -352,9 +363,16 @@ def prerank(
 
 
     """
-    if "processes" in kwarg:
+    if "processes" in kwargs:
         warnings.warn("processes is deprecated; use threads", DeprecationWarning, 2)
-        threads = kwarg["processes"]
+        threads = kwargs["processes"]
+
+    if "weighted_score_type" in kwargs:
+        warnings.warn(
+            "weighted_score_type is deprecated; use weight", DeprecationWarning, 2
+        )
+        weight = kwargs["weighted_score_type"]
+
     pre = Prerank(
         rnk,
         gene_sets,
@@ -364,7 +382,7 @@ def prerank(
         min_size,
         max_size,
         permutation_num,
-        weighted_score_type,
+        weight,
         ascending,
         threads,
         figsize,
@@ -381,12 +399,14 @@ def prerank(
 def replot(
     indir,
     outdir="GSEA_Replot",
-    weighted_score_type=1,
+    weight=1,
     min_size=3,
     max_size=1000,
     figsize=(6.5, 6),
     format="pdf",
     verbose=False,
+    *args,
+    **kwargs,
 ):
     """The main function to reproduce GSEA desktop outputs.
 
@@ -394,7 +414,7 @@ def replot(
 
     :param outdir: Output directory.
 
-    :param float weighted_score_type: weighted score type. choose from {0,1,1.5,2}. Default: 1.
+    :param float weight: weighted score type. choose from {0,1,1.5,2}. Default: 1.
 
     :param list figsize: Matplotlib output figure figsize. Default: [6.5,6].
 
@@ -411,9 +431,13 @@ def replot(
     :return: Generate new figures with selected figure format. Default: 'pdf'.
 
     """
-    rep = Replot(
-        indir, outdir, weighted_score_type, min_size, max_size, figsize, format, verbose
-    )
+    if "weighted_score_type" in kwargs:
+        warnings.warn(
+            "weighted_score_type is deprecated; use weight", DeprecationWarning, 2
+        )
+        weight = kwargs["weighted_score_type"]
+
+    rep = Replot(indir, outdir, weight, min_size, max_size, figsize, format, verbose)
     rep.run()
 
     return
@@ -634,16 +658,16 @@ def gsva(
     gene_sets: Union[List[str], str, Dict[str, str]],
     outdir: Optional[str] = None,
     kcdf: Optional[str] = "Gaussian",
-    weighted_score_type: float = 0.25,
+    weight: float = 1.0,
     mx_diff: bool = True,
     abs_rnk: bool = False,
     min_size: int = 15,
     max_size: int = 1000,
-    threads: int = 1,
+    threads: int = 4,
     seed: int = 123,
     verbose: bool = False,
     **kwargs,
-):
+) -> GSVA:
     """Run Gene Set Enrichment Analysis with single sample GSEA tool
 
     :param data: Expression table, pd.Series, pd.DataFrame, GCT file
@@ -652,22 +676,22 @@ def gsva(
 
     :param outdir: Results output directory. If None, nothing will write to disk.
 
-    :param str kcdf: "Gaussian", "Possion" or None. 
+    :param str kcdf: "Gaussian", "Possion" or None.
                      The non-parametric estimation of the cumulative distribution function
 
-    :param str weighted_score_type: tau of gsva. Default: 1.
+    :param str weight: tau of gsva. Default: 1.
 
-    :param bool mx_diff: Offers two approaches to calculate the enrichment statistic (ES) from the KS random walk statistic. 
-                         If True (default), ES is calculated as the difference 
-                         between the maximum positive and negative random walk deviations. 
+    :param bool mx_diff: Offers two approaches to calculate the enrichment statistic (ES) from the KS random walk statistic.
+                         If True (default), ES is calculated as the difference
+                         between the maximum positive and negative random walk deviations.
                          If False, ES is calculated as the maximum positive to 0.
 
-    :param bool abs_rnk: Flag used only when mx_diff=True. 
-                         If False (default), the enrichment statistic (ES) is calculated taking the magnitude difference 
-                         between the largest positive and negative random walk deviations. 
-                         If True, feature sets with features enriched on either extreme (high or low) 
+    :param bool abs_rnk: Flag used only when mx_diff=True.
+                         If False (default), the enrichment statistic (ES) is calculated taking the magnitude difference
+                         between the largest positive and negative random walk deviations.
+                         If True, feature sets with features enriched on either extreme (high or low)
                          will be regarded as 'highly' activated.
-    
+
     see GSVA doc: https://rdrr.io/bioc/GSVA/man/gsva.html
 
     :param int min_size: Minimum allowed number of genes from gene set also the data set. Default: 15.
@@ -689,7 +713,7 @@ def gsva(
         gene_sets=gene_sets,
         outdir=outdir,
         kcdf=kcdf,
-        weight=weighted_score_type,
+        weight=weight,
         mx_diff=mx_diff,
         abs_rnk=abs_rnk,
         min_size=min_size,
diff --git a/gseapy/base.py b/gseapy/base.py
index ce2e38d..2227acf 100644
--- a/gseapy/base.py
+++ b/gseapy/base.py
@@ -614,7 +614,7 @@ def to_df(
         self.res2d = res_df.reindex(
             res_df["NES"].abs().sort_values(ascending=False).index
         ).reset_index(drop=True)
-        res_df.drop(dc, axis=1, inplace=True)
+        self.res2d.drop(dc, axis=1, inplace=True)
 
         if self._outdir is not None:
             out = os.path.join(
diff --git a/gseapy/gsva.py b/gseapy/gsva.py
index 4f9eb87..419f9a5 100644
--- a/gseapy/gsva.py
+++ b/gseapy/gsva.py
@@ -121,14 +121,25 @@ def load_data(self) -> pd.DataFrame:
 
     def run(self):
         """run entry"""
+        assert self.min_size <= self.max_size
+        if self._outdir:
+            mkdirs(self.outdir)
 
         self._logger.info("Parsing data files for GSVA.............................")
         # load data
         df = self.load_data()
-        if self.rnaseq:
-            self._logger.info("Poisson kernel selected. Clip negative values to 0 !")
-            df = df.clip(lower=0)
-
+        # kernel
+        if self.kernel:
+            if self.rnaseq:
+                self._logger.info(
+                    "Estimating ECDFs with Poisson kernels. Clip negative values to 0 !"
+                )
+                df = df.clip(lower=0)
+            else:
+                self._logger.info("Estimating ECDFs with Gaussian kernels.")
+        else:
+            self._logger.info("Estimating ECDFs with directly.")
+        # save data
         self.data = df
         # normalized samples, and rank
         # filtering out gene sets and build gene sets dictionary
@@ -139,11 +150,7 @@ def run(self):
         )
         # start analysis
         self._logger.info("Start to run GSVA...Might take a while................")
-
-        assert self.min_size <= self.max_size
-        if self._outdir:
-            mkdirs(self.outdir)
-
+        # run
         gsum = gsva_rs(
             df.index.values.tolist(),
             df.values.tolist(),
@@ -158,5 +165,5 @@ def run(self):
             self._threads,
         )
         self.to_df(gsum.summaries, gmt, df, gsum.indices)
-        self.ranking = [rnk[ind] for rnk, ind in zip(gsum.rankings, gsum.indices)]
+        self.ranking = gsum.rankings
         self._logger.info("Done")
diff --git a/src/gsva.rs b/src/gsva.rs
index 9fd5a96..3292c89 100644
--- a/src/gsva.rs
+++ b/src/gsva.rs
@@ -1,7 +1,7 @@
 #![allow(dead_code, unused)]
 
+use crate::stats::{GSEAResult, GSEASummary};
 use crate::utils::{DynamicEnum, Statistic};
-use crate::stats::{GSEASummary, GSEAResult};
 use rayon::prelude::*;
 use statrs::distribution::{ContinuousCDF, DiscreteCDF, Normal, Poisson};
 use std::collections::HashMap;
@@ -87,17 +87,19 @@ impl GSVA {
         }
     }
     /// row: input are gene values of all samples.
+    /// The empirical CDF is usually formally defined as
+    /// CDF(x) = "number of samples <= x" / "number of samples"
     fn apply_ecdf(&self, row: &[f64]) -> Vec<f64> {
         let mut x0 = row.to_vec();
         let n = row.len() as f64;
         // To speedup, sort f64 in acending order in place, then do a binary search
         x0.sort_unstable_by(|a, b| a.partial_cmp(b).unwrap()); // if descending -> b.partial_cmp(a)
-        // binary_search assumes that the elements are sorted in less-to-greater order.
-        // partition_point return the index of the first element of the second partition)
-        // since partition_point is just a wrapper of self.binary_search_by(|x| if pred(x) { Less } else { Greater }).unwrap_or_else(|i| i)
+                                                               // binary_search assumes that the elements are sorted in less-to-greater order.
+                                                               // partition_point return the index of the first element of the second partition)
+                                                               // since partition_point is just a wrapper of self.binary_search_by(|x| if pred(x) { Less } else { Greater }).unwrap_or_else(|i| i)
         row.iter()
-            .map(|v| ((x0.partition_point(|x| x < v)) as f64) / n)
-            // .map(|v| (v / (1.0 -v)).ln()) // // https://github.com/rcastelo/GSVA/blob/devel/R/gsva.R, line 820
+            .map(|v| ((x0.partition_point(|x| x <= v)) as f64) / n)
+            .map(|v| (v / (1.0 - v)).ln()) // https://github.com/rcastelo/GSVA/blob/devel/R/gsva.R, line 820
             .collect()
     }
 
@@ -135,16 +137,17 @@ impl GSVA {
         // println!("s1 {:?}, s2 {:?}, row: {:?}", x.len(), y.len(), &row);
         return row;
     }
-    /// mat_density [n_genes, n_samples] 
-    /// return  gene_density matrix [n_genes, n_samples] 
+    /// mat_density [n_genes, n_samples]
+    /// return  gene_density matrix [n_genes, n_samples]
     fn mat_d(&self, mat_density: &[Vec<f64>], pre_cdf: &[f64]) -> Vec<Vec<f64>> {
         // let D = vec![vec![0;mat[0].lend()]; mat.len()];
         mat_density
-            .par_iter().enumerate()
-            .map(|(i, vv)| 
-                
-                {   // print!("gene idx: {:?}", i);
-                    self.row_d(vv, vv, pre_cdf)})
+            .par_iter()
+            .enumerate()
+            .map(|(i, vv)| {
+                // print!("gene idx: {:?}", i);
+                self.row_d(vv, vv, pre_cdf)
+            })
             .collect()
     }
 
@@ -277,7 +280,7 @@ pub fn gsva(
     abs_rnk: bool,
     tau: f64,
     min_size: usize,
-    max_size: usize
+    max_size: usize,
 ) -> GSEAResult {
     // # Randomize feature order to break ties randomly
     // rng = default_rng(seed=seed)
@@ -327,7 +330,6 @@ pub fn gsva(
     gsea.indices = sort_idxs;
     gsea.rankings = mat_score;
     return gsea;
-
 }
 
 mod tests {
@@ -361,8 +363,9 @@ mod tests {
         //let expr = transpose(&gene_exp);
         println!("{:?}", sample_names);
         let gsumm = gsva(
-            gene_name, gene_exp, gene_sets, true, false, true, false, 1.0, 1, 1000);
-        let file = File::create("data/gsva.rs.out").expect("Cannot write filename");
+            gene_name, gene_exp, gene_sets, true, false, true, false, 1.0, 1, 1000,
+        );
+        let file = File::create("tests/data/gsva.rs.out").expect("Cannot write filename");
         let mut wrt = csv::WriterBuilder::new().delimiter(b'\t').from_writer(file);
         let _ = wrt.write_record(gct.header.get_vec().iter());
         let mut record = Vec::<String>::new();