Update documentation

cumc · Oct 26, 2023 · cfdc4f3 · cfdc4f3
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Showing 3 changed files with 9 additions and 11 deletions.
diff --git a/_sources/code/fine_mapping/SuSiE/SuSiE.ipynb b/_sources/code/fine_mapping/SuSiE/SuSiE.ipynb
@@ -385,7 +385,6 @@
     "                             refine=TRUE,\n",
     "                             compute_univariate_zscore=FALSE,\n",
     "                             coverage=${coverage})\n",
-    "            fitted[[r]]$sets_secondary = susie_get_cs(fitted[[r]], fdat$residual_X_scaled[[r]], coverage=${secondary_coverage})\n",
     "            fitted[[r]]$analysis_time <- proc.time() - st\n",
     "            fitted[[r]] <- post_process_susie(fitted[[r]], fdat, r, signal_cutoff = ${pip_cutoff})\n",
     "            if (!is.null(fitted[[r]]$sets$cs)) {\n",
@@ -400,13 +399,13 @@
     "        }\n",
     "      # generate weights for TWAS using some alternative approaches --- this is not exactly fine-mapping but makes sense to do it here for data production\n",
     "      fitted[[r]]$susie_weights = susie_weights(fdat$fitted[[r]])\n",
-    "      fitted[[r]]$susie_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$susie_weights, fdat$residual_Y_scaled[[r]])\n",
+    "      fitted[[r]]$susie_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$susie_weights, fdat$residual_Y_scaled[[r]])^2\n",
     "      fitted[[r]]$enet_weights = glmnet_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]])\n",
-    "      fitted[[r]]$enet_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$enet_weights, fdat$residual_Y_scaled[[r]])\n",
+    "      fitted[[r]]$enet_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$enet_weights, fdat$residual_Y_scaled[[r]])^2\n",
     "      fitted[[r]]$lasso_weights = glmnet_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]], alpha = 1)\n",
-    "      fitted[[r]]$lasso_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$lasso_weights, fdat$residual_Y_scaled[[r]])\n",
+    "      fitted[[r]]$lasso_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$lasso_weights, fdat$residual_Y_scaled[[r]])^2\n",
     "      fitted[[r]]$mr_ash_weights = mr_ash_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]], beta.init=fitted[[r]]$lasso_weights)\n",
-    "      fitted[[r]]$mr_ash_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$mr_ash_weights, fdat$residual_Y_scaled[[r]])\n",
+    "      fitted[[r]]$mr_ash_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$mr_ash_weights, fdat$residual_Y_scaled[[r]])^2\n",
     "    }\n",
     "    names(fitted) <- names(fdat$residual_Y_scaled)\n",
     "    saveRDS(fitted, ${_output:ar}, compress='xz')"

diff --git a/code/fine_mapping/SuSiE/SuSiE.html b/code/fine_mapping/SuSiE/SuSiE.html
@@ -782,7 +782,6 @@ <h2>Univariate SuSiE<a class="headerlink" href="#univariate-susie" title="Permal
                              refine=TRUE,
                              compute_univariate_zscore=FALSE,
                              coverage=${coverage})
-            fitted[[r]]$sets_secondary = susie_get_cs(fitted[[r]], fdat$residual_X_scaled[[r]], coverage=${secondary_coverage})
             fitted[[r]]$analysis_time &lt;- proc.time() - st
             fitted[[r]] &lt;- post_process_susie(fitted[[r]], fdat, r, signal_cutoff = ${pip_cutoff})
             if (!is.null(fitted[[r]]$sets$cs)) {
@@ -797,13 +796,13 @@ <h2>Univariate SuSiE<a class="headerlink" href="#univariate-susie" title="Permal
         }
       # generate weights for TWAS using some alternative approaches --- this is not exactly fine-mapping but makes sense to do it here for data production
       fitted[[r]]$susie_weights = susie_weights(fdat$fitted[[r]])
-      fitted[[r]]$susie_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$susie_weights, fdat$residual_Y_scaled[[r]])
+      fitted[[r]]$susie_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$susie_weights, fdat$residual_Y_scaled[[r]])^2
       fitted[[r]]$enet_weights = glmnet_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]])
-      fitted[[r]]$enet_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$enet_weights, fdat$residual_Y_scaled[[r]])
+      fitted[[r]]$enet_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$enet_weights, fdat$residual_Y_scaled[[r]])^2
       fitted[[r]]$lasso_weights = glmnet_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]], alpha = 1)
-      fitted[[r]]$lasso_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$lasso_weights, fdat$residual_Y_scaled[[r]])
+      fitted[[r]]$lasso_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$lasso_weights, fdat$residual_Y_scaled[[r]])^2
       fitted[[r]]$mr_ash_weights = mr_ash_weights(fdat$residual_X_scaled[[r]], fdat$residual_Y_scaled[[r]], beta.init=fitted[[r]]$lasso_weights)
-      fitted[[r]]$mr_ash_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$mr_ash_weights, fdat$residual_Y_scaled[[r]])
+      fitted[[r]]$mr_ash_r2 = cor(fdat$residual_X_scaled[[r]] %*% fitted[[r]]$mr_ash_weights, fdat$residual_Y_scaled[[r]])^2
     }
     names(fitted) &lt;- names(fdat$residual_Y_scaled)
     saveRDS(fitted, ${_output:ar}, compress=&#39;xz&#39;)

diff --git a/searchindex.js b/searchindex.js