update main figures

Author: xueweic

Main Figures/Figure2/.ipynb_checkpoints/Figure2a-checkpoint.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2b. Representative simulation examples.\n",
     "\n",
-    "Representative simulation examples highlighting limitations of methods based on ‘one causal’ assumption: (i) failure to detect true causal signals with heterogeneous effects and (ii) spurious detection of non-causal variants with strongest marginal effect. Additionally, (iii) COLOC (V5) demonstrates reduced sensitivity to weak causal effects in GWAS."
+    "Representative simulation examples illustrating limitations of competing methods: (i) methods under “one-causal” assumption fail to detect causal signals with heterogeneous effects and (ii) spurious detection of non-causal variants with strongest marginal effect; (iii) COLOC (V5) shows reduced sensitivity to weak causal effects in GWAS."
    ]
   },
   {

Main Figures/Figure2/.ipynb_checkpoints/Figure2b-checkpoint.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2a. Statistical power and False Discovery Rate (FDR) comparisons.\n",
     "\n",
-    "Statistical power and False Discovery Rate (FDR) comparisons of *ColocBoost* against COLOC (V5), MOLOC and HyPrColoc, in simulation settings  involving 2, 5, 10, and 20 traits, up to five causal variants per trait per locus, with genotype data and induced colocalization configurations designed to mimic real xQTL datasets. Details of power and FDR calculations are provided in Methods. The X-axis represents the maximum number of causal variants across traits."
+    "Statistical power and False Discovery Rate (FDR) comparisons of ColocBoost against COLOC (V5), MOLOC and HyPrColoc, in simulation settings involving 2, 5, 10, and 20 traits, up to five causal variants per trait per locus, with genotype data and induced colocalization configurations designed to mimic real world xQTL datasets. Details of power and FDR calculations are provided in Methods. The X-axis represents the maximum number of causal variants across traits."
    ]
   },
   {

Main Figures/Figure2/.ipynb_checkpoints/Figure2c-checkpoint.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2b. Representative simulation examples.\n",
     "\n",
-    "Representative simulation examples highlighting limitations of methods based on ‘one causal’ assumption: (i) failure to detect true causal signals with heterogeneous effects and (ii) spurious detection of non-causal variants with strongest marginal effect. Additionally, (iii) COLOC (V5) demonstrates reduced sensitivity to weak causal effects in GWAS."
+    "Representative simulation examples illustrating limitations of competing methods: (i) methods under “one-causal” assumption fail to detect causal signals with heterogeneous effects and (ii) spurious detection of non-causal variants with strongest marginal effect; (iii) COLOC (V5) shows reduced sensitivity to weak causal effects in GWAS."
    ]
   },
   {

Main Figures/Figure2/.ipynb_checkpoints/Figure2d-checkpoint.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2c. Variant‐level precision‐recall curves.\n",
     "\n",
-    "Variant‐level precision‐recall curves by varying the colocalization score threshold (*ColocBoost* uses VCP; MOLOC/COLOC/HyPrColoc use variant-level scores from their respective methods)."
+    "Variant‐level precision‐recall curves by varying the colocalization score threshold (*ColocBoost* uses VCP; COLOC/HyPrColoc use variant-level scores from their respective methods; Methods and Supplementary Note)."
    ]
   },
   {

Main Figures/Figure2/.ipynb_checkpoints/Figure2e-checkpoint.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2d. Statistical power and False Discovery Rate (FDR) comparisons.\n",
     "\n",
-    "Statistical power and FDR comparisons for GWAS-prioritized version of *ColocBoost (GWAS-xQTL)* where there is a simulated “disease trait”, fow which we lower the per-SNP heritability in the locus compared to other molecular traits, reflecting expectations from real world applications."
+    "Statistical power and FDR comparisons for trait-prioritized mode of *ColocBoost* (GWAS-xQTL) where there is a simulated “disease trait”, for which we lower the per-SNP heritability in the locus relative to other molecular traits to reflect expectations from real world applications."
    ]
   },
   {

Main Figures/Figure2/Figure2a.ipynb

@@ -8,7 +8,7 @@
    "source": [
     "# Figure 2e. Statistical power and False Discovery Rate (FDR) comparisons.\n",
     "\n",
-    "Statistical power and FDR comparisons for GWAS-prioritized version of *ColocBoost (GWAS-xQTL)* where there is a simulated “disease trait”, fow which we lower the per-SNP heritability in the locus compared to other molecular traits, reflecting expectations from real world applications."
+    "Statistical power and FDR comparison of *ColocBoost* with OPERA for GWAS colocalization, evaluated at the gene level (Methods). "
    ]
   },
   {