From 09ae51b37c9279b9e9c473a033ecc3c5eb9b48bf Mon Sep 17 00:00:00 2001
From: Jesse Bloom <jbloom@fredhutch.org>
Date: Tue, 7 Nov 2023 14:52:51 -0800
Subject: [PATCH] Fix bug in reporting pre-selection counts cutoff in
 `func_scores` (#89)

See [this issue](https://github.com/dms-vep/dms-vep-pipeline-3/issues/87).
The cutoff was applied correctly before and that has not changed, this just
fixes reporting of it in CSVs and plots in `analyze_func_scores`.
---
 CHANGELOG.md                            |  3 ++
 docs/notebooks/analyze_func_scores.html | 50 ++++++++++++-------------
 scripts/func_scores.py                  |  2 +-
 3 files changed, 29 insertions(+), 26 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 538d223..7f73cf1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,8 @@
 # CHANGELOG
 
+#### version 3.5.4
+- Fix bug in reporting pre-selection counts cutoff in `func_scores`. See [this issue](https://github.com/dms-vep/dms-vep-pipeline-3/issues/87). The cutoff was applied correctly before and that has not changed, this just fixes reporting of it in plots in `analyze_func_scores`.
+
 #### version 3.5.3
 - Fix bug in `per_antibody_escape.csv` production in `summary` introduced in version 3.5.1.
 
diff --git a/docs/notebooks/analyze_func_scores.html b/docs/notebooks/analyze_func_scores.html
index 6ffc554..ff3b196 100644
--- a/docs/notebooks/analyze_func_scores.html
+++ b/docs/notebooks/analyze_func_scores.html
@@ -7697,23 +7697,23 @@ <h2 id="Pre--and-post-selection-counts-for-variants">Pre- and post-selection cou
 <div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
 <div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html" tabindex="0">
 <style>
-  #altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002.vega-embed {
+  #altair-viz-63a53974d9c64362a8904aec8bfeeb3e.vega-embed {
     width: 100%;
     display: flex;
   }
 
-  #altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002.vega-embed details,
-  #altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002.vega-embed details summary {
+  #altair-viz-63a53974d9c64362a8904aec8bfeeb3e.vega-embed details,
+  #altair-viz-63a53974d9c64362a8904aec8bfeeb3e.vega-embed details summary {
     position: relative;
   }
 </style>
-<div id="altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002"></div>
+<div id="altair-viz-63a53974d9c64362a8904aec8bfeeb3e"></div>
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   var VEGA_DEBUG = (typeof VEGA_DEBUG == "undefined") ? {} : VEGA_DEBUG;
   (function(spec, embedOpt){
     let outputDiv = document.currentScript.previousElementSibling;
-    if (outputDiv.id !== "altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002") {
-      outputDiv = document.getElementById("altair-viz-09cf1f2d9a2343f8b109d4dc3ba00002");
+    if (outputDiv.id !== "altair-viz-63a53974d9c64362a8904aec8bfeeb3e") {
+      outputDiv = document.getElementById("altair-viz-63a53974d9c64362a8904aec8bfeeb3e");
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     const paths = {
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@@ -7759,7 +7759,7 @@ <h2 id="Pre--and-post-selection-counts-for-variants">Pre- and post-selection cou
         .catch(showError)
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@@ -7931,23 +7931,23 @@ <h2 id="Distributions-of-functional-scores">Distributions of functional scores<a
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     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -8090,23 +8090,23 @@ <h1 id="Correlations-in-variant-functional-scores">Correlations in variant funct
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+    if (outputDiv.id !== "altair-viz-3f819a5c9f4c4b4480c3f2ce0d55b2c9") {
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     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
@@ -8160,23 +8160,23 @@ <h1 id="Correlations-in-variant-functional-scores">Correlations in variant funct
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+    if (outputDiv.id !== "altair-viz-f5116d205ff147a6a1210be1910be638") {
+      outputDiv = document.getElementById("altair-viz-f5116d205ff147a6a1210be1910be638");
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     const paths = {
       "vega": "https://cdn.jsdelivr.net/npm/vega@5?noext",
diff --git a/scripts/func_scores.py b/scripts/func_scores.py
index 1a1fcad..ceaba0f 100644
--- a/scripts/func_scores.py
+++ b/scripts/func_scores.py
@@ -87,7 +87,7 @@
     "post_selection_sample": snakemake.params.samples["post_selection_sample"],
     "pre_selection_date": snakemake.params.dates["pre_selection_sample"],
     "post_selection_date": snakemake.params.dates["post_selection_sample"],
-    "min_pre_selection_count": func_score_params["min_pre_selection_count"],
+    "min_pre_selection_count": min_pre,
 }
 for ctype in ["pre_count", "post_count"]:
     counts_summary_d[f"{ctype}_median"] = func_scores[ctype].median()