diff --git a/.test/config/config.yaml b/.test/config/config.yaml
index 46356ffe..e9f061fe 100644
--- a/.test/config/config.yaml
+++ b/.test/config/config.yaml
@@ -60,11 +60,6 @@ diffexp:
       # in the primary_variable sample sheet column and will be considered as
       # denominator in the fold change/effect size estimation).
       base_level: untreated
-    model_Y:
-      full: ~condition + batch_effect
-      reduced: ~batch_effect
-      primary_variable: condition
-      base_level: untreated
   # significance level to use for volcano, ma- and qq-plots
   sig-level:
     volcano-plot: 0.05
@@ -111,7 +106,7 @@ enrichment:
 
 meta_comparisons:
   # comparison is only run if set to `true`
-  activate: true
+  activate: false
   # Define here the comparisons under interest
   comparisons:
     # Define any name for comparison. You can add as many comparisions as you want
diff --git a/workflow/resources/datavzrd/diffexp-template.yaml b/workflow/resources/datavzrd/diffexp-template.yaml
index 3a5ee780..fe82b179 100644
--- a/workflow/resources/datavzrd/diffexp-template.yaml
+++ b/workflow/resources/datavzrd/diffexp-template.yaml
@@ -134,7 +134,7 @@ views:
           display-mode: hidden
         regex('qval_.+'):
           display-mode: hidden
-        ?f"regex('signed_pi_value_{params.primary_variable}.+')":
+        regex('signed_pi_value_.+'):
           display-mode: normal
           plot:
             heatmap:
@@ -147,33 +147,8 @@ views:
                 - -1
                 - 0
                 - 1
-        ?f"regex('signed_pi_value_(?!{params.primary_variable}).+')":
-          display-mode: detail
-          plot:
-            heatmap:
-              scale: linear
-              range:
-                - "#e6550d"
-                - "white"
-                - "#6baed6"
-              domain:
-                - -1
-                - 0
-                - 1
-        ?f"regex('^b_{params.primary_variable}(?!.*_se$)')":
-          plot:
-            heatmap:
-              scale: linear
-              range:
-                - "#e6550d"
-                - "white"
-                - "#6baed6"
-              domain:
-                - -1
-                - 0
-                - 1
-        ?f"regex('^b_(?!{params.primary_variable})(?!.*_se$)')":
-          display-mode: detail
+        regex('^b_(?!.*_se$)'):
+          display-mode: normal
           plot:
             heatmap:
               scale: linear
diff --git a/workflow/resources/datavzrd/go-enrichment-template.yaml b/workflow/resources/datavzrd/go-enrichment-template.yaml
index 4d5c16dd..1469c3ae 100644
--- a/workflow/resources/datavzrd/go-enrichment-template.yaml
+++ b/workflow/resources/datavzrd/go-enrichment-template.yaml
@@ -12,7 +12,8 @@ views:
     dataset: go_enrichment
     desc: |
       Gene ontology (GO) term enrichment analysis was performed with goatools on differentially expressed genes as determined by the sleuth model.
-      The resulting table includes several columns with key information and is sorted according to the 'p-value' column in ascending order. The "term" column lists the specific GO term, while the "class" column indicates the classification of the GO term (biological process/cellular component/molecular function). The column "FDR" is the p-value obtained from false discovery. The "enrichment" column compares the ratio of differentially expressed genes in the current study to the ratio of total genes present in the current analysis. Lastly, the "study_items" shows the name of each differentially expressed gene together with its corresponding "b" value which is the fold change seperated.
+      The resulting table includes several columns with key information and is sorted in descending order by the values of the pi-value score of sum of fold changes affected (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`).
+      The "term" column lists the specific GO term, while the "class" column indicates the classification of the GO term (biological process/cellular component/molecular function). The column "FDR" is the p-value obtained from false discovery. The "enrichment" column compares the ratio of differentially expressed genes in the current study to the ratio of total genes present in the current analysis. The "study_items" shows the name of each differentially expressed gene together with its corresponding "b" value which is the fold change seperated. Lastly, the "effect" column shows the sum of the absolute values of fold changes in the study items.
       This analysis helps to identify and understand the biological significance of gene expression changes.
     page-size: 25
     render-table:
@@ -83,7 +84,33 @@ views:
                 - "#bcbd22"
               domain:
                 - 0.0
-                - 1.0   
+                - 1.0
+        effect:
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300        
+        pi_value:
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300
         study_items: 
           display-mode: detail 
           custom-plot:
diff --git a/workflow/resources/datavzrd/meta_comparison-diffexp-template.yaml b/workflow/resources/datavzrd/meta_comparison-diffexp-template.yaml
index 21ca23d8..0fd2b5b9 100644
--- a/workflow/resources/datavzrd/meta_comparison-diffexp-template.yaml
+++ b/workflow/resources/datavzrd/meta_comparison-diffexp-template.yaml
@@ -11,7 +11,8 @@ views:
   comparison_table:
     dataset: comparison_table
     desc: |
-      Table for values of meta comparison. The table is sorted in descending order by the absolute value of the difference of the effects.
+      Table for values of meta comparison.       
+      The table is sorted in descending order by the pi-value score of the difference in effects of the fold change of the primary variable of the model under consideration (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`).
     page-size: 25
     render-table:
       columns:
@@ -22,7 +23,7 @@ views:
               url: f"https://www.ensembl.org/{str.capitalize(?params.species)}/Transcript/Summary?t={{target_id}}"
         target_id:
           display-mode: hidden
-        min q-value:
+        qval_min:
           plot:
             heatmap:
               scale: linear
@@ -43,7 +44,7 @@ views:
                 - "#fdae6b"
               domain:
                 - 0
-                - 300
+                - 30
         regex('effect\s+\w+\s+\(beta\s+score\)'):
           display-mode: normal
           plot:
@@ -57,6 +58,19 @@ views:
                 - -8
                 - 0
                 - 8
+        pi_value:
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300
   comparison_plot:
     desc: |
       The axes represent the log2-fold changes (beta-scores) for the two models, with each point representing a gene. Points on the diagonal indicate no difference between the comparisons, while deviations from the diagonal suggest differences in gene expression fold changes between the treatments.
diff --git a/workflow/resources/datavzrd/meta_comparison-go_terms-template.yaml b/workflow/resources/datavzrd/meta_comparison-go_terms-template.yaml
index 542c694d..cf85f10d 100644
--- a/workflow/resources/datavzrd/meta_comparison-go_terms-template.yaml
+++ b/workflow/resources/datavzrd/meta_comparison-go_terms-template.yaml
@@ -11,7 +11,8 @@ views:
   comparison_table:
     dataset: comparison_table
     desc: |
-        Table for values of meta comparison. The table is sorted in descending order based on the absolute value of the maximum difference between the positive effects and the negative effects.
+      Table for values of meta comparison. 
+      The table is sorted in descending order by the pi-value score of the maximum difference in positive and negative total effects of fold changes influenced by the enrichment (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`).
     page-size: 25
     render-table:
       columns:
@@ -66,6 +67,19 @@ views:
               domain:
                 - 0
                 - -300
+        pi_value:
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300
   comparison_plot:
     desc: |
       The left side shows the summed positive beta-scores within a GO-term and the right side shows the summed negative beta-scores, with each point representing a GO-term. This illustrates whether a GO-term is enriched positively, negatively, or in both directions.
diff --git a/workflow/resources/datavzrd/meta_comparison-pathways-template.yaml b/workflow/resources/datavzrd/meta_comparison-pathways-template.yaml
index 549bb7a0..6b1faf13 100644
--- a/workflow/resources/datavzrd/meta_comparison-pathways-template.yaml
+++ b/workflow/resources/datavzrd/meta_comparison-pathways-template.yaml
@@ -11,7 +11,8 @@ views:
   comparison_table:
     dataset: comparison_table
     desc: |
-      Table for values of meta comparison. The table is sorted in descending order by the absolute value of the difference of the effects.
+      Table for values of meta comparison. 
+      The table is sorted in descending order by the pi-value score of the difference in effects of the total perturbation accumulation (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`).
     page-size: 25
     render-table:
       columns:
@@ -34,6 +35,20 @@ views:
                 - 0.25
         pathway id:
           display-mode: hidden
+        pi_value:
+          label: signed_pi_value
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300
         difference:
           plot:
             heatmap:
diff --git a/workflow/resources/datavzrd/spia-template.yaml b/workflow/resources/datavzrd/spia-template.yaml
index ea4f1bcf..9e505ce2 100644
--- a/workflow/resources/datavzrd/spia-template.yaml
+++ b/workflow/resources/datavzrd/spia-template.yaml
@@ -12,7 +12,8 @@ views:
     dataset: spia_table
     desc: |
       The SPIA pathway impact analysis results are summarized in a table with the following columns. The "Name" column lists the name of the pathway under investigation. The "total perturbation accumulation" column quantifies the overall perturbation or disruption occurring within each pathway. The "Status" column reflects the activity or response of the pathway. The "gene_ratio" column presents the number of differentially expressed genes present in the pathway compared to the total number of genes in the pathway. The "pathway id" column provides a unique identification number for each pathway. 
-      This analysis aids in understanding the impact and activity of various pathways in the context of the studied biological system. The table is sorted in descending order by the absolute values of the signed versions of the pi-value score of the total pertubation accumulation (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`). The sign reflects the sign of the effect (i.e. positive for upregulation, negative for downregulation). The column "study_items" (click on the "+") shows the name of each differentially expressed gene together with its corresponding fold change. 
+      This analysis aids in understanding the impact and activity of various pathways in the context of the studied biological system. 
+      The table is sorted in descending order by the absolute values of the signed versions of the pi-value score of the total pertubation accumulation (as proposed  by `Xiao et al. 2014 <https://dx.doi.org/10.1093/bioinformatics/`). The sign reflects the sign of the effect (i.e. positive for upregulation, negative for downregulation). The column "study_items" (click on the "+") shows the name of each differentially expressed gene together with its corresponding fold change. 
     page-size: 25
     render-table:
       columns:
@@ -31,6 +32,19 @@ views:
           display-mode: hidden
         number of DE genes per pathway:
           display-mode: hidden
+        signed_pi_value:
+          display-mode: normal
+          plot:
+            heatmap:
+              scale: linear
+              range:
+                - "#e6550d"
+                - "white"
+                - "#6baed6"
+              domain:
+                - -300
+                - 0
+                - 300
         gene_ratio:
           custom-plot:
             data: |
diff --git a/workflow/scripts/compare_diffexp.py b/workflow/scripts/compare_diffexp.py
index 58580a6d..169a76e5 100644
--- a/workflow/scripts/compare_diffexp.py
+++ b/workflow/scripts/compare_diffexp.py
@@ -27,12 +27,14 @@ def prepare(df):
     .with_columns(
         pl.min_horizontal("qval", "qval_y").alias("qval_min"),
     )
+    .with_columns(
+        pl.min_horizontal("pval", "pval_y").alias("pval_min"),
+    )
     .filter(pl.col("qval_min") <= 0.05)
     .rename(
         {
             "beta": effect_x,
             "beta_y": effect_y,
-            "qval_min": "min q-value",
         }
     )
     .collect()
@@ -44,11 +46,25 @@ def prepare(df):
 combined = combined.with_columns(
     abs(pl.col(effect_x) - pl.col(effect_y)).alias("difference")
 )
-combined_sorted = combined.sort("difference", descending=True)
-combined_pd = combined_sorted.select(
-    ["ext_gene", "target_id", "min q-value", effect_x, effect_y, "difference"]
-).to_pandas()
-combined_pd.to_csv(snakemake.output[0], sep="\t", index=False)
+combined = (
+    combined.with_columns(
+        (-pl.col("pval_min").log(base=10) * pl.col("difference")).alias("pi_value")
+    )
+    .sort(pl.col("pi_value").abs(), descending=True)
+    .select(
+        pl.col(
+            "ext_gene",
+            "target_id",
+            "qval_min",
+            effect_x,
+            effect_y,
+            "difference",
+            "pi_value",
+        )
+    )
+    .to_pandas()
+)
+combined.to_csv(snakemake.output[0], sep="\t", index=False)
 
 
 # we cannot use vegafusion here because it makes the point selection impossible since
@@ -59,12 +75,12 @@ def prepare(df):
 point_selector = alt.selection_point(fields=["ext_gene"], empty=False)
 
 points = (
-    alt.Chart(combined_pd)
+    alt.Chart(combined)
     .mark_circle(size=15, tooltip={"content": "data"})
     .encode(
         alt.X(effect_x),
         alt.Y(effect_y),
-        alt.Color("min q-value", scale=alt.Scale(scheme="viridis")),
+        alt.Color("qval_min", scale=alt.Scale(scheme="viridis")),
         opacity=alt.value(0.5),
     )
 )
@@ -84,7 +100,7 @@ def prepare(df):
 )
 
 text_background = (
-    alt.Chart(combined_pd)
+    alt.Chart(combined)
     .mark_text(
         align="left",
         baseline="middle",
@@ -102,7 +118,7 @@ def prepare(df):
 )
 
 text = (
-    alt.Chart(combined_pd)
+    alt.Chart(combined)
     .mark_text(
         align="left",
         baseline="middle",
diff --git a/workflow/scripts/compare_enrichment.py b/workflow/scripts/compare_enrichment.py
index 6c2211dd..e1e70731 100644
--- a/workflow/scripts/compare_enrichment.py
+++ b/workflow/scripts/compare_enrichment.py
@@ -37,9 +37,10 @@ def prepare(df):
     # Select necessary columns
     df = (
         df.select(
-            cs.by_name("GO", "term", "p_uncorrected", "p_fdr_bh", "study_items")
+            [cs.by_name("GO", "term", "p_uncorrected", "p_fdr_bh", "study_items")]
         )
         .with_columns(
+            [
                 pl.col("study_items")
                 .map_elements(
                     extract_study_items,
@@ -50,6 +51,7 @@ def prepare(df):
                     ),
                 )
                 .alias("parsed_terms")
+            ]
         )
         .with_columns(
             [
@@ -66,12 +68,83 @@ def prepare(df):
     return df
 
 
-def plot(df, effect_x, effect_y, title, xlabel, ylabel):
-    xlabel = (f"effect {xlabel}",)
-    ylabel = (f"effect {ylabel}",)
+prepared_diffexp_x = prepare(diffexp_x)
+prepared_diffexp_y = prepare(diffexp_y)
+combined = (
+    prepared_diffexp_x.join(
+        prepared_diffexp_y, on=["GO", "term"], how="outer", suffix="_y"
+    )
+    .rename(
+        {
+            "cumulative_b_scores_positive": effect_x_pos,
+            "cumulative_b_scores_positive_y": effect_y_pos,
+            "cumulative_b_scores_negative": effect_x_neg,
+            "cumulative_b_scores_negative_y": effect_y_neg,
+        }
+    )
+    .with_columns(
+        pl.col(effect_x_pos).cast(pl.Float64),
+        pl.col(effect_y_pos).cast(pl.Float64),
+        pl.col(effect_x_neg).cast(pl.Float64),
+        pl.col(effect_y_neg).cast(pl.Float64),
+        pl.col("p_fdr_bh").cast(pl.Float64),
+        pl.col("p_fdr_bh_y").cast(pl.Float64),
+    )
+    .with_columns(pl.min_horizontal("p_fdr_bh", "p_fdr_bh_y").alias("min_p_fdr_bh"))
+    .fill_null(0)  # Set missing values to 0
+    .filter(pl.col("min_p_fdr_bh") <= 0.05)
+    .collect()
+)
+
+
+# we cannot use vegafusion here because it makes the point selection impossible since
+# it prunes the required ext_gene column
+# alt.data_transformers.enable("vegafusion")
+
 
+if not combined.is_empty():
+    combined = (
+        combined.with_columns(
+            pl.max_horizontal(
+                abs(pl.col(effect_x_pos) - pl.col(effect_y_pos)),
+                abs(pl.col(effect_x_neg) - pl.col(effect_y_neg)),
+            ).alias("difference")
+        )
+        .with_columns(
+            (-pl.col("min_p_fdr_bh").log(base=10) * pl.col("difference")).alias(
+                "pi_value"
+            )
+        )
+        .sort(pl.col("pi_value").abs(), descending=True)
+    )
+else:
+    combined = combined.with_columns(
+        [pl.lit(None).alias("difference"), pl.lit(None).alias("pi_value")]
+    )
+
+
+combined_pd = combined.select(
+    pl.col(
+        "GO",
+        "term",
+        "min_p_fdr_bh",
+        effect_x_pos,
+        effect_y_pos,
+        effect_x_neg,
+        effect_y_neg,
+        "difference",
+        "pi_value",
+    )
+).to_pandas()
+
+combined_pd.to_csv(snakemake.output[0], sep="\t", index=False)
+
+
+def plot(df, effect_x, effect_y, title, xlabel, ylabel):
     # Filter out rows where either effect_x or effect_y is zero because of logarithmic scale
-    df = df.select(["GO", "term", "min_p_fdr_bh", effect_x, effect_y]).to_pandas()
+    min_value = min(df[effect_x].min(), df[effect_y].min())
+    max_value = max(df[effect_x].max(), df[effect_y].max())
+    point_selector = alt.selection_single(fields=["term"], empty=False)
 
     alt.data_transformers.disable_max_rows()
     points = (
@@ -147,6 +220,7 @@ def plot(df, effect_x, effect_y, title, xlabel, ylabel):
     )
 
     chart = (
+        # alt.layer(line, points, text_background, text)
         alt.layer(line, points, text_background, text)
         .add_params(point_selector)
         .properties(title=title)
@@ -236,10 +310,20 @@ def plot(df, effect_x, effect_y, title, xlabel, ylabel):
 
 # Update the plot function calls to include the logarithmic scale and filter out zero values
 positive_chart = plot(
-    combined, effect_x_pos, effect_y_pos, "Positive effect", label_x, label_y
+    combined_pd,
+    effect_x_pos,
+    effect_y_pos,
+    "Positive effect",
+    f"effect {label_x}",
+    f"effect {label_y}",
 )
 negative_chart = plot(
-    combined, effect_x_neg, effect_y_neg, "Negative effect", label_x, label_y
+    combined_pd,
+    effect_x_neg,
+    effect_y_neg,
+    "Negative effect",
+    f"effect {label_x}",
+    f"effect {label_y}",
 )
 
 final_chart = alt.hconcat(positive_chart, negative_chart).resolve_scale(color="shared")
diff --git a/workflow/scripts/compare_pathways.py b/workflow/scripts/compare_pathways.py
index 92e67810..7ccdbe36 100644
--- a/workflow/scripts/compare_pathways.py
+++ b/workflow/scripts/compare_pathways.py
@@ -7,7 +7,6 @@
 label_x = list(snakemake.params.labels)[0]
 label_y = list(snakemake.params.labels)[1]
 
-
 effect_x = f"effect {label_x}"
 effect_y = f"effect {label_y}"
 
@@ -31,19 +30,16 @@ def prepare(df):
 prepared_diffexp_x = prepare(diffexp_x)
 prepared_diffexp_y = prepare(diffexp_y)
 combined = (
-    prepared_diffexp_x.join(prepared_diffexp_y, on=["Name"], suffix="_y")
-    .cast(
-        {
-            cs.by_name(
-                "Combined FDR",
-                "Combined FDR_y",
-                "total perturbation accumulation",
-                "total perturbation accumulation_y",
-            ): pl.Float64
-        }
+    prepared_diffexp_x.join(prepared_diffexp_y, on=["Name"], how="outer", suffix="_y")
+    .with_columns(
+        pl.col("Combined FDR").cast(pl.Float64),
+        pl.col("Combined FDR_y").cast(pl.Float64),
+        pl.col("total perturbation accumulation").cast(pl.Float64),
+        pl.col("total perturbation accumulation_y").cast(pl.Float64),
     )
     .with_columns(pl.min_horizontal("Combined FDR", "Combined FDR_y").alias("min fdr"))
     .filter(pl.col("min fdr") <= 0.05)
+    .fill_null(0)
     .rename(
         {
             "total perturbation accumulation": effect_x,
@@ -67,8 +63,8 @@ def prepare(df):
     combined = combined.with_columns(
         [pl.lit(None).alias("difference"), pl.lit(None).alias("pi_value")]
     )
-combined = combined.select(
-    [
+combined_pd = combined.select(
+    pl.col(
         "Name",
         "min fdr",
         effect_x,
@@ -76,13 +72,13 @@ def prepare(df):
         "difference",
         "pathway id",
         "pi_value",
-    ]
-)
-combined.write_csv(snakemake.output[0], separator="\t")
-
-df = combined.select(["min fdr", effect_x, effect_y]).to_pandas()
+    )
+).to_pandas()
+combined_pd.to_csv(snakemake.output[0], sep="\t", index=False)
 
-point_selector = alt.selection_single(fields=["term"], empty="all")
+min_value = min(combined_pd[effect_x].min(), combined_pd[effect_y].min())
+max_value = max(combined_pd[effect_x].max(), combined_pd[effect_y].max())
+point_selector = alt.selection_single(fields=["Name"], empty=False)
 
 alt.data_transformers.disable_max_rows()
 points = (
@@ -123,6 +119,25 @@ def prepare(df):
         strokeDash=alt.value([5, 5]),
     )
 )
+x_axis = (
+    alt.Chart(pl.DataFrame({effect_x: [0, 0], effect_y: [min_value, max_value]}))
+    .mark_line(color="lightgrey")
+    .encode(
+        x=effect_x,
+        y=effect_y,
+        strokeDash=alt.value([5, 5]),
+    )
+)
+
+y_axis = (
+    alt.Chart(pl.DataFrame({effect_x: [min_value, max_value], effect_y: [0, 0]}))
+    .mark_line(color="lightgrey")
+    .encode(
+        x=effect_x,
+        y=effect_y,
+        strokeDash=alt.value([5, 5]),
+    )
+)
 
 text_background = (
     alt.Chart(df)
diff --git a/workflow/scripts/postprocess_diffexp.py b/workflow/scripts/postprocess_diffexp.py
index 8f511014..c6e5c19e 100644
--- a/workflow/scripts/postprocess_diffexp.py
+++ b/workflow/scripts/postprocess_diffexp.py
@@ -3,8 +3,9 @@
 
 def process_columns(df):
     """compute confidence interval for every column starting with b_"""
-    matching_columns = [col for col in df.columns if col.startswith(
-        'b_') and not col.endswith('_se')]
+    matching_columns = [
+        col for col in df.columns if col.startswith("b_") and not col.endswith("_se")
+    ]
 
     for col in matching_columns:
         df[f"{col}_lower"] = df[col] - df[f"{col}_se"]
@@ -14,32 +15,34 @@ def process_columns(df):
 
 
 def sort_columns(df, matching_columns):
-    b_column_order = [f"{prefix}{suffix}" for prefix in matching_columns for suffix in ['_lower', '', '_upper', '_se']]
-    signed_pi_columns = [col for col in df.columns if col.startswith('signed_pi_value')]
-    other_columns = [col for col in df.columns if not col.startswith('b_') and not col.startswith('signed_pi_value')]
-    return df[other_columns + b_column_order + signed_pi_columns]
+    b_column_order = [
+        f"{prefix}{suffix}"
+        for prefix in matching_columns
+        for suffix in ["_lower", "", "_upper", "_se"]
+    ]
+    other_columns = [col for col in df.columns if not col.startswith("b_")]
+    return df[other_columns + b_column_order]
 
 
 def sort_rows(df):
     """Sort DataFrame by the absolute value of signed_p_value of primary variable in ascending order."""
     signed_pi_start = f"signed_pi_value_{snakemake.params['model']['primary_variable']}"
-    columns_with_prefix = [
-        col for col in df.columns if col.startswith(signed_pi_start)]
+    columns_with_prefix = [col for col in df.columns if col.startswith(signed_pi_start)]
 
     if len(columns_with_prefix) != 1:
         raise ValueError(
-            f"Expected exactly one column starting with '{signed_pi_start}', found {len(columns_with_prefix)}")
+            f"Expected exactly one column starting with '{signed_pi_start}', found {len(columns_with_prefix)}"
+        )
 
     signed_pi_col = columns_with_prefix[0]
 
-    df_sorted = df.sort_values(
-        by=signed_pi_col, key=lambda x: x.abs(), ascending=False)
+    df_sorted = df.sort_values(by=signed_pi_col, key=lambda x: x.abs(), ascending=False)
     return df_sorted
 
 
-df = pd.read_csv(snakemake.input[0], sep='\t')
+df = pd.read_csv(snakemake.input[0], sep="\t")
 df, matching_columns = process_columns(df)
 df = sort_columns(df, matching_columns)
 df = sort_rows(df)
-df = df.dropna(subset=matching_columns, how='all')
-df.to_csv(snakemake.output[0], sep='\t', index=False)
+df = df.dropna(subset=matching_columns, how="all")
+df.to_csv(snakemake.output[0], sep="\t", index=False)
diff --git a/workflow/scripts/postprocess_go_enrichment.py b/workflow/scripts/postprocess_go_enrichment.py
index edc01b39..3f98cbb4 100644
--- a/workflow/scripts/postprocess_go_enrichment.py
+++ b/workflow/scripts/postprocess_go_enrichment.py
@@ -10,9 +10,22 @@ def calculate_enrichment(ratio_str):
     return enrichment
 
 
-def sort_group(group):
-    """Sort a DataFrame group by the 'p_uncorrected' column in ascending order."""
-    return group.sort_values(by="p_uncorrected", ascending=True)
+def extract_study_items(value):
+    if value and value.strip() != "":
+        gene_values = value.split(", ")
+        data = []
+        for gene_value in gene_values:
+            parts = gene_value.split(":")
+            gene = parts[0]
+            val = float(parts[1])
+            data.append({"gene": gene, "value": val})
+        return data
+    else:
+        return []
+
+
+def calculate_sums(parsed_terms):
+    return sum(abs(item["value"]) for item in parsed_terms)
 
 
 # Load data
@@ -22,13 +35,14 @@ def sort_group(group):
 # We only want to have the study items from df_sig
 df_sig = df_sig.select(["GO", "study_items"])
 df_merged = df_enr.join(df_sig, on="GO", how="inner", suffix="_df2")
-df_merged_sorted = df_merged.with_columns(
-    pl.col("study_items_df2").alias("study_items")
-).drop("study_items_df2")
+df_merged = df_merged.with_columns(pl.col("study_items_df2").alias("study_items")).drop(
+    "study_items_df2"
+)
 
 
-if not df_merged_sorted.is_empty():
-    df_merged_sorted = df_merged_sorted.with_columns(
+if not df_merged.is_empty():
+    # Compute enrichment
+    df_merged = df_merged.with_columns(
         pl.struct(
             [
                 pl.col("ratio_in_study")
@@ -44,8 +58,28 @@ def sort_group(group):
         )
         .alias("enrichment")
     )
+
+    # Compute effect (the effect size is the sum of absolute values of the study items)
+    df = df_merged.with_columns(
+        [
+            pl.col("study_items")
+            .map_elements(lambda x: calculate_sums(extract_study_items(x)))
+            .alias("effect")
+        ]
+    )
+
+    df = df.with_columns(
+        (-pl.col("p_fdr_bh").log(base=10) * pl.col("effect")).alias("pi_value")
+    )
+
+    df = df.sort(pl.col("pi_value").abs(), descending=True)
+
 else:
-    df_merged_sorted = df_merged_sorted.with_columns(enrichment=pl.lit(None))
+    # Create new empty columns
+    cols = ["effect", "enrichment", "pi_value"]
+    new_cols = [pl.lit(None).alias(col) for col in cols]
+    df = df_merged.with_columns(new_cols)
+
 
 # Save the result to a file
-df_merged_sorted.write_csv(snakemake.output[0], separator="\t")
+df.write_csv(snakemake.output[0], separator="\t")