diff --git a/manuscript/figures/flu-2016-2018-ha-embeddings-by-clade.html b/manuscript/figures/flu-2016-2018-ha-embeddings-by-clade.html
index 88ca09f7..26ecbf10 100644
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"parent_mutation": 0.0266321390456337, "pca1": -2.420716016116636, "pca2": -2.055508912737804, "pca_label": 5.0, "mds1": -21.099493633021257, "mds2": 6.5163331521233845, "mds3": -8.782422408819032, "mds_label": 4.0, "tsne_x": -26.886634145105464, "tsne_y": -5.355538080507517, "t-sne_label": 4.0, "umap_x": 11.328975, "umap_y": 14.856743, "umap_label": 6.0, "clade_membership_color": "A1b/197R"}]}};
       var embedOpt = {"mode": "vega-lite"};
 
       function showError(el, error){
diff --git a/manuscript/figures/flu-2018-2020-mds-by-clade.png b/manuscript/figures/flu-2018-2020-mds-by-clade.png
index 6301a5d9..24088c9a 100644
Binary files a/manuscript/figures/flu-2018-2020-mds-by-clade.png and b/manuscript/figures/flu-2018-2020-mds-by-clade.png differ
diff --git a/seasonal-flu-nextstrain-2018-2020/2021-03-09Notebook.ipynb b/seasonal-flu-nextstrain-2018-2020/2021-03-09Notebook.ipynb
index 9b8d2aa7..fdbd4498 100644
--- a/seasonal-flu-nextstrain-2018-2020/2021-03-09Notebook.ipynb
+++ b/seasonal-flu-nextstrain-2018-2020/2021-03-09Notebook.ipynb
@@ -67,140 +67,164 @@
     "static_mds_chart = snakemake.output.MDS_Supplement_PNG"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "dc772cba",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5e135bbc",
+   "id": "06532877",
    "metadata": {},
    "outputs": [],
    "source": [
-    "clades_to_plot = [\n",
-    "    '3c2',\n",
-    "    '3c3.A',\n",
-    "    'A1',\n",
-    "    'A1b/131K',\n",
-    "    'A1b/135K',\n",
-    "    'A1b/135N',\n",
-    "    'A1b/137F',\n",
-    "    'A1b/186D',\n",
-    "    'A1b/197R',\n",
-    "    'A1b/94N',\n",
-    "    'A2',\n",
-    "    'A3',\n",
-    "]\n",
-    "domain = clades_to_plot"
+    "embeddings_df = pd.read_csv(embeddings_path, sep=\"\\t\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "51de9a8a",
+   "id": "bc994d18",
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(clades_to_plot)"
+    "embeddings_df = embeddings_df.rename(\n",
+    "    columns={\n",
+    "        \"num_date\": \"date\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53923a14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clade_counts = embeddings_df[\"clade_membership\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b3a1e7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clade_counts"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "dc772cba",
+   "id": "f10ad3f9",
    "metadata": {},
    "source": [
-    "## Load data"
+    "Only assign colors to clades with at least 10 samples. This approach allows us to clearly see larger clades using fewer colors."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3b0b80a2",
+   "id": "5e4c5882",
    "metadata": {},
    "outputs": [],
    "source": [
-    "colors = pd.read_csv(colors_path, sep=\"\\t\", names=[i for i in range(0,101)], nrows=101)"
+    "clades_to_plot_with_color = sorted(clade_counts[clade_counts >= 10].index.values)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28c2742c",
+   "id": "e4570623",
    "metadata": {},
    "outputs": [],
    "source": [
-    "clade_color_range = colors.iloc[len(clades_to_plot) - 1].dropna().tolist()"
+    "clades_to_plot = sorted(embeddings_df[\"clade_membership\"].drop_duplicates().values)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57ada75a",
+   "id": "de1a59a2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(clade_color_range)"
+    "clades_to_plot"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40527d62",
+   "id": "5e135bbc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#domain.append(\"other\")"
+    "domain = clades_to_plot_with_color"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75fb5a9d",
+   "id": "51de9a8a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(clades_to_plot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b0b80a2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#clade_color_range.append(\"#999999\")"
+    "colors = pd.read_csv(colors_path, sep=\"\\t\", names=[i for i in range(0,101)], nrows=101)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "06532877",
+   "id": "28c2742c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df = pd.read_csv(embeddings_path, sep=\"\\t\")"
+    "clade_color_range = colors.iloc[len(clades_to_plot_with_color) - 1].dropna().tolist()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "bc994d18",
+   "id": "57ada75a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df = embeddings_df.rename(\n",
-    "    columns={\n",
-    "        \"num_date\": \"date\",\n",
-    "    }\n",
-    ")"
+    "len(clade_color_range)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "53923a14",
+   "id": "40527d62",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df[\"clade_membership\"].value_counts()"
+    "domain.append(\"other\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e4570623",
+   "id": "75fb5a9d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df[\"clade_membership\"].drop_duplicates().sort_values().tolist()"
+    "clade_color_range.append(\"#999999\")"
    ]
   },
   {
@@ -211,7 +235,7 @@
    "outputs": [],
    "source": [
     "embeddings_df[\"clade_membership_color\"] = embeddings_df[\"clade_membership\"].apply(\n",
-    "    lambda clade: clade if clade in clades_to_plot else \"other\"\n",
+    "    lambda clade: clade if clade in clades_to_plot_with_color else \"other\"\n",
     ")"
    ]
   },
@@ -225,6 +249,36 @@
     "embeddings_df.head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dfafbe8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embeddings_df[\"clade_membership_color\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6652ac0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eba1af54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clade_color_range"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/seasonal-flu-nextstrain/2022-03-29-final-figures.py.ipynb b/seasonal-flu-nextstrain/2022-03-29-final-figures.py.ipynb
index 206db227..581604df 100644
--- a/seasonal-flu-nextstrain/2022-03-29-final-figures.py.ipynb
+++ b/seasonal-flu-nextstrain/2022-03-29-final-figures.py.ipynb
@@ -67,44 +67,6 @@
     "static_mds_chart = snakemake.output.MDS_Supplement_PNG"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e135bbc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clades_to_plot = [\n",
-    "    '3c',\n",
-    "    '3c2',\n",
-    "    '3c2.A',\n",
-    "    '3c3',\n",
-    "    '3c3.A',\n",
-    "    '3c3.B',\n",
-    "    'A1',\n",
-    "    'A1a',\n",
-    "    'A1b',\n",
-    "    'A1b/131K',\n",
-    "    'A1b/135K',\n",
-    "    'A1b/135N',\n",
-    "    'A2',\n",
-    "    'A3',\n",
-    "    'A4',\n",
-    "]\n",
-    "\n",
-    "domain = clades_to_plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51de9a8a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(clades_to_plot)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -155,85 +117,125 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "697b4c7a",
+   "id": "05af41cb",
    "metadata": {},
    "outputs": [],
    "source": [
-    "colors = pd.read_csv(colors_path, sep=\"\\t\", names=[i for i in range(0,101)], nrows=101)"
+    "embeddings_df = pd.read_csv(embeddings_path, sep=\"\\t\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f392e626",
+   "id": "01e5961b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "colors.head(16)"
+    "embeddings_df = embeddings_df.rename(\n",
+    "    columns={\n",
+    "        \"numdate\": \"date\",\n",
+    "    }\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28c2742c",
+   "id": "a7224da1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "clade_color_range = colors.iloc[len(clades_to_plot) - 1].dropna().tolist()"
+    "clades_to_plot = sorted(embeddings_df[\"clade_membership\"].drop_duplicates().values)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "57ada75a",
+   "id": "b6ee15f1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(clade_color_range)"
+    "clades_to_plot"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "40527d62",
+   "id": "de8bc9d4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "domain.append(\"other\")"
+    "len(clades_to_plot)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "75fb5a9d",
+   "id": "697b4c7a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "clade_color_range.append(\"#999999\")"
+    "colors = pd.read_csv(colors_path, sep=\"\\t\", names=[i for i in range(0,101)], nrows=101)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "05af41cb",
+   "id": "f392e626",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df = pd.read_csv(embeddings_path, sep=\"\\t\")"
+    "colors.head(16)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "01e5961b",
+   "id": "5e135bbc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings_df = embeddings_df.rename(\n",
-    "    columns={\n",
-    "        \"numdate\": \"date\",\n",
-    "    }\n",
-    ")"
+    "domain = clades_to_plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28c2742c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clade_color_range = colors.iloc[len(clades_to_plot) - 1].dropna().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57ada75a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(clade_color_range)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40527d62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "domain.append(\"other\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75fb5a9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clade_color_range.append(\"#999999\")"
    ]
   },
   {