From 2c08e415f618b0e68cc5b0f1d6731de712ba5173 Mon Sep 17 00:00:00 2001
From: Niko Papadopoulos <nikolaos.papadopoulos@embl.de>
Date: Thu, 15 Dec 2022 13:27:58 +0100
Subject: [PATCH] added statistical comparison of avg pLDDT distributions
---
analysis/review-proteome_coverage.ipynb | 666 ++++++++++++++++++++++++
1 file changed, 666 insertions(+)
create mode 100644 analysis/review-proteome_coverage.ipynb
diff --git a/analysis/review-proteome_coverage.ipynb b/analysis/review-proteome_coverage.ipynb
new file mode 100644
index 0000000..bb42fdb
--- /dev/null
+++ b/analysis/review-proteome_coverage.ipynb
@@ -0,0 +1,666 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "51ce05c0-6933-42fd-a5c5-e5809e6858d9",
+ "metadata": {},
+ "source": [
+ "The reviewers challenged us to revisit the statement that the AlphaFold prediction quality for _Spongilla_ was the same as for other species."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c3ea6175-d71f-4ae7-8934-52f818a80a0e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tqdm import tqdm\n",
+ "import glob\n",
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "570141f0-2fc4-4bb7-ad0d-7a9e76531c81",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plddt = {}\n",
+ "for path in glob.glob(\"../data/alphafold_performance/*\"):\n",
+ " basename = path.split(\"/\")[-1]\n",
+ " species = basename.split(\".\")[0]\n",
+ " tmp = pd.read_csv(path, sep=\"\\t\", index_col=0)\n",
+ " plddt[species] = tmp[\"plddt\"].T.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "89e50e57-402b-4d56-9203-95e5ca4a4065",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "genes = []\n",
+ "score = []\n",
+ "with np.load(\"../data/results/spongilla_plddt.npz\") as spongilla:\n",
+ " for gene in spongilla.keys():\n",
+ " genes.append(gene)\n",
+ " score.append(np.mean(spongilla[gene]))\n",
+ "\n",
+ "plddt[\"S_lacustris\"] = np.array(score)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9637a995-be1d-467e-8073-4238e8ae080e",
+ "metadata": {},
+ "source": [
+ "To do goodness-of-fit tests it is important to know what sort of distribution we are analysing. We will use D'Agostino and Pearson's test to check if the pLDDT score distributions are normal. The null hypothesis is that they are from a normal distribution, so a small p-value means we can reject it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "46755534-f0c1-4c1c-8536-857fe54c1850",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import scipy.stats as stats\n",
+ "from scipy.stats import normaltest"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b38831fe-e45e-4e23-b91d-79cfaeae0476",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Two-sided χ-squared probability for the hypothesis test (rounded to 10 decimals)\n",
+ "A_thaliana: 0.0\n",
+ "M_musculus: 0.0\n",
+ "D_rerio: 0.0\n",
+ "S_cerevisiae: 0.0\n",
+ "H_sapiens: 0.0\n",
+ "D_discoideum: 0.0\n",
+ "C_elegans: 0.0\n",
+ "D_melanogaster: 0.0\n",
+ "S_lacustris: 0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Two-sided χ-squared probability for the hypothesis test (rounded to 10 decimals)\")\n",
+ "for species, score in plddt.items():\n",
+ " stat, pval = normaltest(score)\n",
+ " print(f\"{species}: {np.round_(pval, decimals=10)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0dddfa7d-df6b-4851-8a70-a1dfa4055a59",
+ "metadata": {},
+ "source": [
+ "none of the distributions are normal; this means we are left with non-parametric distribution tests like the Kolmogorov-Smirnov."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "69e57e28-6465-4856-9a95-f3f045611325",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy.stats import ks_2samp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "8c41c67e-6e44-421e-bbae-b4238d2e9f48",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "species = plddt.keys()\n",
+ "result = np.zeros((len(species), len(species)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "acb7d8b8-d987-4fc5-b8e4-080f9738e4dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i, s1 in enumerate(species):\n",
+ " for j, s2 in enumerate(species):\n",
+ " stat, pval = ks_2samp(plddt[s1], plddt[s2])\n",
+ " result[i, j] = pval"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "d4acd3c6-7b27-41df-bbc5-0e9ced74ec48",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ks_result = pd.DataFrame(result, columns=species, index=species)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b40f5b10-1166-41cc-8a07-fab5e534c9b6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A_thaliana</th>\n",
+ " <th>M_musculus</th>\n",
+ " <th>D_rerio</th>\n",
+ " <th>S_cerevisiae</th>\n",
+ " <th>H_sapiens</th>\n",
+ " <th>D_discoideum</th>\n",
+ " <th>C_elegans</th>\n",
+ " <th>D_melanogaster</th>\n",
+ " <th>S_lacustris</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A_thaliana</th>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>6.934609e-13</td>\n",
+ " <td>9.114463e-18</td>\n",
+ " <td>7.262317e-19</td>\n",
+ " <td>5.750618e-12</td>\n",
+ " <td>9.016068e-113</td>\n",
+ " <td>4.454955e-08</td>\n",
+ " <td>1.026131e-23</td>\n",
+ " <td>1.648052e-254</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>M_musculus</th>\n",
+ " <td>6.934609e-13</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>2.070102e-08</td>\n",
+ " <td>5.863687e-16</td>\n",
+ " <td>5.731073e-34</td>\n",
+ " <td>2.383184e-159</td>\n",
+ " <td>5.537467e-06</td>\n",
+ " <td>6.251385e-24</td>\n",
+ " <td>1.329357e-215</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_rerio</th>\n",
+ " <td>9.114463e-18</td>\n",
+ " <td>2.070102e-08</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>4.857841e-19</td>\n",
+ " <td>8.699267e-45</td>\n",
+ " <td>1.086386e-182</td>\n",
+ " <td>1.085280e-13</td>\n",
+ " <td>2.567091e-39</td>\n",
+ " <td>4.784290e-287</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>S_cerevisiae</th>\n",
+ " <td>7.262317e-19</td>\n",
+ " <td>5.863687e-16</td>\n",
+ " <td>4.857841e-19</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>6.808127e-22</td>\n",
+ " <td>3.284647e-74</td>\n",
+ " <td>1.288430e-22</td>\n",
+ " <td>2.306440e-12</td>\n",
+ " <td>4.903494e-81</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>H_sapiens</th>\n",
+ " <td>5.750618e-12</td>\n",
+ " <td>5.731073e-34</td>\n",
+ " <td>8.699267e-45</td>\n",
+ " <td>6.808127e-22</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>1.030890e-58</td>\n",
+ " <td>1.705286e-21</td>\n",
+ " <td>8.108620e-11</td>\n",
+ " <td>4.824450e-129</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_discoideum</th>\n",
+ " <td>9.016068e-113</td>\n",
+ " <td>2.383184e-159</td>\n",
+ " <td>1.086386e-182</td>\n",
+ " <td>3.284647e-74</td>\n",
+ " <td>1.030890e-58</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>2.050222e-114</td>\n",
+ " <td>4.987552e-62</td>\n",
+ " <td>3.228598e-15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C_elegans</th>\n",
+ " <td>4.454955e-08</td>\n",
+ " <td>5.537467e-06</td>\n",
+ " <td>1.085280e-13</td>\n",
+ " <td>1.288430e-22</td>\n",
+ " <td>1.705286e-21</td>\n",
+ " <td>2.050222e-114</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>1.466712e-09</td>\n",
+ " <td>1.278533e-145</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_melanogaster</th>\n",
+ " <td>1.026131e-23</td>\n",
+ " <td>6.251385e-24</td>\n",
+ " <td>2.567091e-39</td>\n",
+ " <td>2.306440e-12</td>\n",
+ " <td>8.108620e-11</td>\n",
+ " <td>4.987552e-62</td>\n",
+ " <td>1.466712e-09</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>6.907128e-69</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>S_lacustris</th>\n",
+ " <td>1.648052e-254</td>\n",
+ " <td>1.329357e-215</td>\n",
+ " <td>4.784290e-287</td>\n",
+ " <td>4.903494e-81</td>\n",
+ " <td>4.824450e-129</td>\n",
+ " <td>3.228598e-15</td>\n",
+ " <td>1.278533e-145</td>\n",
+ " <td>6.907128e-69</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A_thaliana M_musculus D_rerio S_cerevisiae \\\n",
+ "A_thaliana 1.000000e+00 6.934609e-13 9.114463e-18 7.262317e-19 \n",
+ "M_musculus 6.934609e-13 1.000000e+00 2.070102e-08 5.863687e-16 \n",
+ "D_rerio 9.114463e-18 2.070102e-08 1.000000e+00 4.857841e-19 \n",
+ "S_cerevisiae 7.262317e-19 5.863687e-16 4.857841e-19 1.000000e+00 \n",
+ "H_sapiens 5.750618e-12 5.731073e-34 8.699267e-45 6.808127e-22 \n",
+ "D_discoideum 9.016068e-113 2.383184e-159 1.086386e-182 3.284647e-74 \n",
+ "C_elegans 4.454955e-08 5.537467e-06 1.085280e-13 1.288430e-22 \n",
+ "D_melanogaster 1.026131e-23 6.251385e-24 2.567091e-39 2.306440e-12 \n",
+ "S_lacustris 1.648052e-254 1.329357e-215 4.784290e-287 4.903494e-81 \n",
+ "\n",
+ " H_sapiens D_discoideum C_elegans D_melanogaster \\\n",
+ "A_thaliana 5.750618e-12 9.016068e-113 4.454955e-08 1.026131e-23 \n",
+ "M_musculus 5.731073e-34 2.383184e-159 5.537467e-06 6.251385e-24 \n",
+ "D_rerio 8.699267e-45 1.086386e-182 1.085280e-13 2.567091e-39 \n",
+ "S_cerevisiae 6.808127e-22 3.284647e-74 1.288430e-22 2.306440e-12 \n",
+ "H_sapiens 1.000000e+00 1.030890e-58 1.705286e-21 8.108620e-11 \n",
+ "D_discoideum 1.030890e-58 1.000000e+00 2.050222e-114 4.987552e-62 \n",
+ "C_elegans 1.705286e-21 2.050222e-114 1.000000e+00 1.466712e-09 \n",
+ "D_melanogaster 8.108620e-11 4.987552e-62 1.466712e-09 1.000000e+00 \n",
+ "S_lacustris 4.824450e-129 3.228598e-15 1.278533e-145 6.907128e-69 \n",
+ "\n",
+ " S_lacustris \n",
+ "A_thaliana 1.648052e-254 \n",
+ "M_musculus 1.329357e-215 \n",
+ "D_rerio 4.784290e-287 \n",
+ "S_cerevisiae 4.903494e-81 \n",
+ "H_sapiens 4.824450e-129 \n",
+ "D_discoideum 3.228598e-15 \n",
+ "C_elegans 1.278533e-145 \n",
+ "D_melanogaster 6.907128e-69 \n",
+ "S_lacustris 1.000000e+00 "
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ks_result"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a5866286-45ce-45a2-a143-e7e9a5fd59c3",
+ "metadata": {},
+ "source": [
+ "This tells us that all of the distributions have easy-to-differentiate ECDFs. Let's plot them:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "2eece4e8-0b18-460f-adcf-3cf98fe2d56d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "87696667-c0b2-4b74-9ddb-901ef4c371a6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots()\n",
+ "for s in species:\n",
+ " x = np.sort(plddt[s])\n",
+ " y = np.arange(len(x))/float(len(x))\n",
+ " plt.plot(x, y, label=s)\n",
+ " \n",
+ "ax.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2f919858-185e-4dd8-bf55-c32d8a945fca",
+ "metadata": {},
+ "source": [
+ "This is indeed the case. _Spongilla_ and _Dictyostelium_ differ even more from the other organisms, enough so that the naked eye can spot it. There is no overwhelming similarity in the profiles, however."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "cd2f071e-f8d6-4398-a049-00b97c1abe38",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy.stats import kruskal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "fb769fd9-d424-4d50-a3bc-42e265474574",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "result = np.zeros((len(species), len(species)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "9e9a0a70-ec9b-41c6-9172-54e7ccf639f9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for i, s1 in enumerate(species):\n",
+ " for j, s2 in enumerate(species):\n",
+ " stat, pval = kruskal(plddt[s1], plddt[s2])\n",
+ " result[i, j] = pval"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "c3d5068b-6f88-47f1-9ade-956b863aae55",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kruskal_result = pd.DataFrame(result, columns=species, index=species)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "7d7597b1-5a93-48a2-a9d9-1e296e5b3fbd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>A_thaliana</th>\n",
+ " <th>M_musculus</th>\n",
+ " <th>D_rerio</th>\n",
+ " <th>S_cerevisiae</th>\n",
+ " <th>H_sapiens</th>\n",
+ " <th>D_discoideum</th>\n",
+ " <th>C_elegans</th>\n",
+ " <th>D_melanogaster</th>\n",
+ " <th>S_lacustris</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A_thaliana</th>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>5.401098e-11</td>\n",
+ " <td>1.294740e-09</td>\n",
+ " <td>4.059266e-11</td>\n",
+ " <td>4.322773e-14</td>\n",
+ " <td>3.897159e-126</td>\n",
+ " <td>4.104535e-01</td>\n",
+ " <td>2.307912e-03</td>\n",
+ " <td>1.998652e-273</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>M_musculus</th>\n",
+ " <td>5.401098e-11</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>3.243668e-01</td>\n",
+ " <td>9.471571e-03</td>\n",
+ " <td>1.298086e-38</td>\n",
+ " <td>1.058769e-163</td>\n",
+ " <td>1.906173e-07</td>\n",
+ " <td>1.179170e-14</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_rerio</th>\n",
+ " <td>1.294740e-09</td>\n",
+ " <td>3.243668e-01</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>2.391935e-03</td>\n",
+ " <td>1.350664e-38</td>\n",
+ " <td>2.799068e-176</td>\n",
+ " <td>9.242227e-06</td>\n",
+ " <td>6.695887e-14</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>S_cerevisiae</th>\n",
+ " <td>4.059266e-11</td>\n",
+ " <td>9.471571e-03</td>\n",
+ " <td>2.391935e-03</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>8.916050e-27</td>\n",
+ " <td>2.290830e-97</td>\n",
+ " <td>3.716571e-09</td>\n",
+ " <td>5.815376e-15</td>\n",
+ " <td>1.645357e-141</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>H_sapiens</th>\n",
+ " <td>4.322773e-14</td>\n",
+ " <td>1.298086e-38</td>\n",
+ " <td>1.350664e-38</td>\n",
+ " <td>8.916050e-27</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>3.002254e-61</td>\n",
+ " <td>3.791151e-14</td>\n",
+ " <td>1.497413e-03</td>\n",
+ " <td>2.972335e-129</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_discoideum</th>\n",
+ " <td>3.897159e-126</td>\n",
+ " <td>1.058769e-163</td>\n",
+ " <td>2.799068e-176</td>\n",
+ " <td>2.290830e-97</td>\n",
+ " <td>3.002254e-61</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>1.923321e-111</td>\n",
+ " <td>5.084089e-65</td>\n",
+ " <td>7.809348e-03</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C_elegans</th>\n",
+ " <td>4.104535e-01</td>\n",
+ " <td>1.906173e-07</td>\n",
+ " <td>9.242227e-06</td>\n",
+ " <td>3.716571e-09</td>\n",
+ " <td>3.791151e-14</td>\n",
+ " <td>1.923321e-111</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>2.288044e-03</td>\n",
+ " <td>5.246039e-227</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D_melanogaster</th>\n",
+ " <td>2.307912e-03</td>\n",
+ " <td>1.179170e-14</td>\n",
+ " <td>6.695887e-14</td>\n",
+ " <td>5.815376e-15</td>\n",
+ " <td>1.497413e-03</td>\n",
+ " <td>5.084089e-65</td>\n",
+ " <td>2.288044e-03</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " <td>3.730213e-131</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>S_lacustris</th>\n",
+ " <td>1.998652e-273</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>0.000000e+00</td>\n",
+ " <td>1.645357e-141</td>\n",
+ " <td>2.972335e-129</td>\n",
+ " <td>7.809348e-03</td>\n",
+ " <td>5.246039e-227</td>\n",
+ " <td>3.730213e-131</td>\n",
+ " <td>1.000000e+00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " A_thaliana M_musculus D_rerio S_cerevisiae \\\n",
+ "A_thaliana 1.000000e+00 5.401098e-11 1.294740e-09 4.059266e-11 \n",
+ "M_musculus 5.401098e-11 1.000000e+00 3.243668e-01 9.471571e-03 \n",
+ "D_rerio 1.294740e-09 3.243668e-01 1.000000e+00 2.391935e-03 \n",
+ "S_cerevisiae 4.059266e-11 9.471571e-03 2.391935e-03 1.000000e+00 \n",
+ "H_sapiens 4.322773e-14 1.298086e-38 1.350664e-38 8.916050e-27 \n",
+ "D_discoideum 3.897159e-126 1.058769e-163 2.799068e-176 2.290830e-97 \n",
+ "C_elegans 4.104535e-01 1.906173e-07 9.242227e-06 3.716571e-09 \n",
+ "D_melanogaster 2.307912e-03 1.179170e-14 6.695887e-14 5.815376e-15 \n",
+ "S_lacustris 1.998652e-273 0.000000e+00 0.000000e+00 1.645357e-141 \n",
+ "\n",
+ " H_sapiens D_discoideum C_elegans D_melanogaster \\\n",
+ "A_thaliana 4.322773e-14 3.897159e-126 4.104535e-01 2.307912e-03 \n",
+ "M_musculus 1.298086e-38 1.058769e-163 1.906173e-07 1.179170e-14 \n",
+ "D_rerio 1.350664e-38 2.799068e-176 9.242227e-06 6.695887e-14 \n",
+ "S_cerevisiae 8.916050e-27 2.290830e-97 3.716571e-09 5.815376e-15 \n",
+ "H_sapiens 1.000000e+00 3.002254e-61 3.791151e-14 1.497413e-03 \n",
+ "D_discoideum 3.002254e-61 1.000000e+00 1.923321e-111 5.084089e-65 \n",
+ "C_elegans 3.791151e-14 1.923321e-111 1.000000e+00 2.288044e-03 \n",
+ "D_melanogaster 1.497413e-03 5.084089e-65 2.288044e-03 1.000000e+00 \n",
+ "S_lacustris 2.972335e-129 7.809348e-03 5.246039e-227 3.730213e-131 \n",
+ "\n",
+ " S_lacustris \n",
+ "A_thaliana 1.998652e-273 \n",
+ "M_musculus 0.000000e+00 \n",
+ "D_rerio 0.000000e+00 \n",
+ "S_cerevisiae 1.645357e-141 \n",
+ "H_sapiens 2.972335e-129 \n",
+ "D_discoideum 7.809348e-03 \n",
+ "C_elegans 5.246039e-227 \n",
+ "D_melanogaster 3.730213e-131 \n",
+ "S_lacustris 1.000000e+00 "
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kruskal_result"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
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+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.8"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
--
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