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olp-cs/test-type-error.ipynb

Last active July 16, 2024 10:15
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GEOparse - Failing test: `test_merge_and_average` fails with a TypeError
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test-type-error.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "d27fa437",
"metadata": {
"id": "d27fa437"
},
"source": [
"I have the following Python code:\n",
"```Python\n",
"tmp_data = tmp_data.groupby(group_by_column).mean()[[expression_column]]\n",
"```\n",
"where\n",
"- `tmp_data` is a pandas dataframe that contains both numeric and string columns;\n",
"- `expression_column` = 'VALUE'\n",
"- `group_by_column` = 'GB_ACC'\n",
"\n",
"I am getting the following errors:\n",
"```\n",
"TypeError: agg function failed [how->mean,dtype->object]\n",
"TypeError: Could not convert string 'DNA segment, Chr 8, ERATO Doi 594, expressed' to numeric\n",
"```"
]
},
{
"cell_type": "markdown",
"source": [
"# Reproducing the input"
],
"metadata": {
"id": "UjiKlqFl_-xI"
},
"id": "UjiKlqFl_-xI"
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b5fbcf1",
"metadata": {
"id": "5b5fbcf1"
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"source": [
"print(pd. __version__)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "C8LCFiU_-4xk",
"outputId": "ff5a44f1-75d4-4a52-cd59-9cdac9aa9da5"
},
"id": "C8LCFiU_-4xk",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2.0.3\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c83d3e72",
"metadata": {
"id": "c83d3e72"
},
"outputs": [],
"source": [
"expression_column = 'VALUE'"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "063b4ccc",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 435
},
"id": "063b4ccc",
"outputId": "c9a28a99-246c-4cad-ae4f-d62e03cefd36"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Unnamed: 0 ID_REF VALUE LogRatioError PValueLogRatio \\\n",
"0 0 1 -1.627476 0.1360 6.410000e-33 \n",
"1 1 2 0.141225 1.3400 1.000000e+00 \n",
"2 2 3 0.182768 0.0519 4.330000e-04 \n",
"3 3 4 -0.393227 0.0608 1.020000e-10 \n",
"4 4 5 -0.986599 0.1050 6.320000e-21 \n",
"\n",
" gProcessedSignal rProcessedSignal ID GB_ACC \\\n",
"0 9130.0 215.0 1 U02079 \n",
"1 41.4 57.2 2 NM_008154 \n",
"2 5130.0 7810.0 3 AK015719 \n",
"3 4650.0 1880.0 4 AK003367 \n",
"4 2910.0 301.0 5 BC003333 \n",
"\n",
" Gene_Desc Gene_Sym SPOT_ID \\\n",
"0 nuclear factor of activated T-cells, cytoplasm... Nfatc2 NaN \n",
"1 G-protein coupled receptor 3 Gpr3 NaN \n",
"2 tropomodulin 2 Tmod2 NaN \n",
"3 mitochondrial ribosomal protein L15 Mrpl15 NaN \n",
"4 RIKEN cDNA 0610033I05 gene 0610033I05Rik NaN \n",
"\n",
" SEQUENCE \n",
"0 ACCTGGATGACGCAGCCACTTCAGAAAGCTGGGTTGGGACAGAAAG... \n",
"1 CTGTACAATGCTCTCACTTACTACTCAGAGACAACGGTAACTCGGA... \n",
"2 CACCAGGCTCAGTGCCTAGTATCGGCTTCACCTAGTGTGGTTACTC... \n",
"3 CAAGAAGTCTAGAAATTCTGTGCAAGCCTATTCCATTCTTTCTGCG... \n",
"4 AGAACTGGGTGGCAGATATCCTAGAGTTTTGACCAACGTTCACAGC... "
],
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" <td>nuclear factor of activated T-cells, cytoplasm...</td>\n",
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" <td>5130.0</td>\n",
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" <td>AK015719</td>\n",
" <td>tropomodulin 2</td>\n",
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" <td>CACCAGGCTCAGTGCCTAGTATCGGCTTCACCTAGTGTGGTTACTC...</td>\n",
" </tr>\n",
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" <th>3</th>\n",
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" <td>1.020000e-10</td>\n",
" <td>4650.0</td>\n",
" <td>1880.0</td>\n",
" <td>4</td>\n",
" <td>AK003367</td>\n",
" <td>mitochondrial ribosomal protein L15</td>\n",
" <td>Mrpl15</td>\n",
" <td>NaN</td>\n",
" <td>CAAGAAGTCTAGAAATTCTGTGCAAGCCTATTCCATTCTTTCTGCG...</td>\n",
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" <td>2910.0</td>\n",
" <td>301.0</td>\n",
" <td>5</td>\n",
" <td>BC003333</td>\n",
" <td>RIKEN cDNA 0610033I05 gene</td>\n",
" <td>0610033I05Rik</td>\n",
" <td>NaN</td>\n",
" <td>AGAACTGGGTGGCAGATATCCTAGAGTTTTGACCAACGTTCACAGC...</td>\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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"\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "tmp_data",
"summary": "{\n \"name\": \"tmp_data\",\n \"rows\": 20,\n \"fields\": [\n {\n \"column\": \"Unnamed: 0\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 19,\n \"num_unique_values\": 20,\n \"samples\": [\n 0,\n 17,\n 15\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ID_REF\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 1,\n \"max\": 20,\n \"num_unique_values\": 20,\n \"samples\": [\n 1,\n 18,\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"VALUE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7204641514436444,\n \"min\": -1.826136,\n \"max\": 0.358304,\n \"num_unique_values\": 20,\n \"samples\": [\n -1.627476,\n -0.61222,\n -0.226702\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LogRatioError\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5979005628248862,\n \"min\": 0.0519,\n \"max\": 2.06,\n \"num_unique_values\": 20,\n \"samples\": [\n 0.136,\n 0.128,\n 0.944\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PValueLogRatio\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.443372864054248,\n \"min\": 6.41e-33,\n \"max\": 1.0,\n \"num_unique_values\": 18,\n \"samples\": [\n 6.41e-33,\n 1.0,\n 0.436\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gProcessedSignal\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3568.1972008985153,\n \"min\": 20.4,\n \"max\": 10200.0,\n \"num_unique_values\": 19,\n \"samples\": [\n 9130.0,\n 708.0,\n 20.4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rProcessedSignal\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1728.830112579861,\n \"min\": 8.89,\n \"max\": 7810.0,\n \"num_unique_values\": 20,\n \"samples\": [\n 215.0,\n 273.0,\n 52.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 1,\n \"max\": 20,\n \"num_unique_values\": 20,\n \"samples\": [\n 1,\n 18,\n 16\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"GB_ACC\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"U02079\",\n \"NM_008462\",\n \"NM_010517\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gene_Desc\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"nuclear factor of activated T-cells, cytoplasmic 2\",\n \"killer cell lectin-like receptor, subfamily A, member 2\",\n \"insulin-like growth factor binding protein 4\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Gene_Sym\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 18,\n \"samples\": [\n \"Nfatc2\",\n \"Gpr3\",\n \"Ngfrap1\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SPOT_ID\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"-- CONTROL\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"SEQUENCE\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"ACCTGGATGACGCAGCCACTTCAGAAAGCTGGGTTGGGACAGAAAGGTATATAGAGAGAAAATTTTGGAA\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"tmp_data = pd.read_csv('tmp_data.csv')\n",
"tmp_data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e24eb2ff",
"metadata": {
"id": "e24eb2ff"
},
"outputs": [],
"source": [
"group_by_column = 'GB_ACC'"
]
},
{
"cell_type": "markdown",
"source": [
"# Error message"
],
"metadata": {
"id": "qeaz5GUzADat"
},
"id": "qeaz5GUzADat"
},
{
"cell_type": "code",
"execution_count": null,
"id": "a83d91c7",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 582
},
"id": "a83d91c7",
"outputId": "71cac718-ec85-4feb-a5af-fa8de15228b4"
},
"outputs": [
{
"output_type": "error",
"ename": "TypeError",
"evalue": "Could not convert DNA segment, Chr 8, ERATO Doi 594, expressed to numeric",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1489\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1490\u001b[0;31m result = self.grouper._cython_operation(\n\u001b[0m\u001b[1;32m 1491\u001b[0m \u001b[0;34m\"aggregate\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_operation\u001b[0;34m(self, kind, values, how, axis, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 958\u001b[0m \u001b[0mngroups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 959\u001b[0;31m return cy_op.cython_operation(\n\u001b[0m\u001b[1;32m 960\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36mcython_operation\u001b[0;34m(self, values, axis, min_count, comp_ids, ngroups, **kwargs)\u001b[0m\n\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 657\u001b[0;31m return self._cython_op_ndim_compat(\n\u001b[0m\u001b[1;32m 658\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_cython_op_ndim_compat\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m return self._call_cython_op(\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_call_cython_op\u001b[0;34m(self, values, min_count, ngroups, comp_ids, mask, result_mask, **kwargs)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[0mout_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_output_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mngroups\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_numeric\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 542\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_cython_vals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/ops.py\u001b[0m in \u001b[0;36m_get_cython_function\u001b[0;34m(cls, kind, how, dtype, is_numeric)\u001b[0m\n\u001b[1;32m 172\u001b[0m \u001b[0;31m# raise NotImplementedError here rather than TypeError later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m raise NotImplementedError(\n\u001b[0m\u001b[1;32m 174\u001b[0m \u001b[0;34mf\"function is not implemented for this dtype: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotImplementedError\u001b[0m: function is not implemented for this dtype: [how->mean,dtype->object]",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1691\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1692\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1693\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'DNA segment, Chr 8, ERATO Doi 594, expressed'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1695\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1696\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1697\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-25aa53ce8d0d>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtmp_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup_by_column\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mexpression_column\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, numeric_only, engine, engine_kwargs)\u001b[0m\n\u001b[1;32m 1853\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_numba_agg_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msliding_mean\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1854\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1855\u001b[0;31m result = self._cython_agg_general(\n\u001b[0m\u001b[1;32m 1856\u001b[0m \u001b[0;34m\"mean\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1857\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[0;34m(self, how, alt, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 1505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m \u001b[0mnew_mgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouped_reduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1508\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_agged_manager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_mgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1509\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrap_aggregated_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mgrouped_reduce\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m 1501\u001b[0m \u001b[0;31m# while others do not.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1502\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mblk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1503\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1504\u001b[0m \u001b[0mresult_blocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextend_blocks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1505\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/internals/blocks.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0mone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 328\u001b[0m \"\"\"\n\u001b[0;32m--> 329\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 331\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_op_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36marray_func\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m 1501\u001b[0m \u001b[0;31m# try to python agg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1502\u001b[0m \u001b[0;31m# TODO: shouldn't min_count matter?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1503\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_agg_py_fallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m 4668\u001b[0m )\n\u001b[1;32m 4669\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4670\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4672\u001b[0m def _reindex_indexer(\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 96\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 97\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;31m# we want to transform an object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 157\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 158\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnew_func\u001b[0;34m(values, axis, skipna, mask, **kwargs)\u001b[0m\n\u001b[1;32m 419\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 423\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdatetimelike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1697\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1698\u001b[0m \u001b[0;31m# e.g. \"foo\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1699\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Could not convert {x} to numeric\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1700\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Could not convert DNA segment, Chr 8, ERATO Doi 594, expressed to numeric"
]
}
],
"source": [
"tmp_data.groupby(group_by_column).mean()[[expression_column]]"
]
},
{
"cell_type": "markdown",
"source": [
"# Exploring context that may be relevant"
],
"metadata": {
"id": "IF-wNk_BAZTM"
},
"id": "IF-wNk_BAZTM"
},
{
"cell_type": "code",
"execution_count": null,
"id": "a40c49f8",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a40c49f8",
"outputId": "58b40aed-acb7-4071-84b5-a68f0bad5edb"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7856b1218130>"
]
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"tmp_data.groupby(group_by_column)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "99274ef4",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "99274ef4",
"outputId": "3606a413-c66b-455d-f797-c3db718c0a67"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Unnamed: 0 ID_REF VALUE LogRatioError PValueLogRatio \\\n",
"GB_ACC \n",
"AB045323 9 9 10 0.240589 0.3090 4.360000e-01 \n",
"AK003367 3 3 4 -0.393227 0.0608 1.020000e-10 \n",
"AK003755 13 13 14 -1.548040 0.1300 7.210000e-33 \n",
"AK004524 16 16 17 -0.148402 0.8010 8.530000e-01 \n",
"AK004937 15 15 16 -0.226702 0.9440 8.100000e-01 \n",
"AK005789 10 10 11 0.320937 0.3590 3.710000e-01 \n",
"AK010722 12 12 13 -0.012207 0.3640 9.730000e-01 \n",
"AK015719 2 2 3 0.182768 0.0519 4.330000e-04 \n",
"BC003241 14 14 15 0.007342 0.2980 9.800000e-01 \n",
"BC003333 4 4 5 -0.986599 0.1050 6.320000e-21 \n",
"NM_008029 6 6 7 -1.484182 0.1250 1.420000e-32 \n",
"NM_008154 1 1 2 0.141225 1.3400 1.000000e+00 \n",
"NM_008462 5 5 6 0.023881 0.1020 8.150000e-01 \n",
"NM_009750 8 8 9 -1.034478 1.7800 1.000000e+00 \n",
"NM_010517 11 11 12 0.358304 2.0600 1.000000e+00 \n",
"NM_023120 19 19 20 -0.084895 0.9380 9.280000e-01 \n",
"NM_025999 17 17 18 -0.612220 0.1280 1.690000e-06 \n",
"NM_054088 7 7 8 -1.826136 0.4150 1.100000e-05 \n",
"U02079 0 0 1 -1.627476 0.1360 6.410000e-33 \n",
"\n",
" gProcessedSignal rProcessedSignal ID GB_ACC \\\n",
"GB_ACC \n",
"AB045323 9 161.0 280.00 10 AB045323 \n",
"AK003367 3 4650.0 1880.00 4 AK003367 \n",
"AK003755 13 10200.0 290.00 14 AK003755 \n",
"AK004524 16 96.5 68.60 17 AK004524 \n",
"AK004937 15 89.0 52.80 16 AK004937 \n",
"AK005789 10 125.0 261.00 11 AK005789 \n",
"AK010722 12 184.0 179.00 13 AK010722 \n",
"AK015719 2 5130.0 7810.00 3 AK015719 \n",
"BC003241 14 221.0 225.00 15 BC003241 \n",
"BC003333 4 2910.0 301.00 5 BC003333 \n",
"NM_008029 6 10200.0 336.00 7 NM_008029 \n",
"NM_008154 1 41.4 57.20 2 NM_008154 \n",
"NM_008462 5 708.0 748.00 6 NM_008462 \n",
"NM_009750 8 96.2 8.89 9 NM_009750 \n",
"NM_010517 11 20.4 46.60 12 NM_010517 \n",
"NM_023120 19 76.8 63.20 20 NM_023120 \n",
"NM_025999 17 1120.0 273.00 18 NM_025999 \n",
"NM_054088 7 719.0 10.70 8 NM_054088 \n",
"U02079 0 9130.0 215.00 1 U02079 \n",
"\n",
" Gene_Desc \\\n",
"GB_ACC \n",
"AB045323 9 DNA segment, Chr 8, ERATO Doi 594, expressed \n",
"AK003367 3 mitochondrial ribosomal protein L15 \n",
"AK003755 13 DNA segment, Chr 4, ERATO Doi 421, expressed \n",
"AK004524 16 unnamed protein product; hypothetical SOCS domain \n",
"AK004937 15 RIKEN cDNA 1300007O09 gene \n",
"AK005789 10 dynein, cytoplasmic, light chain 2B \n",
"AK010722 12 RIKEN cDNA 2410075D05 gene \n",
"AK015719 2 tropomodulin 2 \n",
"BC003241 14 cleavage stimulation factor, 3\\' pre-RNA, subu... \n",
"BC003333 4 RIKEN cDNA 0610033I05 gene \n",
"NM_008029 6 FMS-like tyrosine kinase 4 \n",
"NM_008154 1 G-protein coupled receptor 3 \n",
"NM_008462 5 killer cell lectin-like receptor, subfamily A,... \n",
"NM_009750 8 nerve growth factor receptor (TNFRSF16) associ... \n",
"NM_010517 11 insulin-like growth factor binding protein 4 \n",
"NM_023120 19 guanine nucleotide binding protein (G protein)... \n",
"NM_025999 17 RIKEN cDNA 2610110L04 gene \n",
"NM_054088 7 adiponutrin \n",
"U02079 0 nuclear factor of activated T-cells, cytoplasm... \n",
"\n",
" Gene_Sym SPOT_ID \\\n",
"GB_ACC \n",
"AB045323 9 D8Ertd594e NaN \n",
"AK003367 3 Mrpl15 NaN \n",
"AK003755 13 D4Ertd421e NaN \n",
"AK004524 16 NaN NaN \n",
"AK004937 15 1300007O09Rik NaN \n",
"AK005789 10 Dncl2b NaN \n",
"AK010722 12 2410075D05Rik NaN \n",
"AK015719 2 Tmod2 NaN \n",
"BC003241 14 Cstf3 NaN \n",
"BC003333 4 0610033I05Rik NaN \n",
"NM_008029 6 Flt4 NaN \n",
"NM_008154 1 Gpr3 NaN \n",
"NM_008462 5 Klra2 NaN \n",
"NM_009750 8 Ngfrap1 NaN \n",
"NM_010517 11 Igfbp4 NaN \n",
"NM_023120 19 Gnb1l NaN \n",
"NM_025999 17 2610110L04Rik NaN \n",
"NM_054088 7 Adpn NaN \n",
"U02079 0 Nfatc2 NaN \n",
"\n",
" SEQUENCE \n",
"GB_ACC \n",
"AB045323 9 GATTCAGACTCGGGAGGAGCATCCCAACCTCTCCTTGAGGATAAAG... \n",
"AK003367 3 CAAGAAGTCTAGAAATTCTGTGCAAGCCTATTCCATTCTTTCTGCG... \n",
"AK003755 13 AGCAAAGAGATCTCCCTCAGTGTGCCCATAGGTGGCGGTGCGAGCT... \n",
"AK004524 16 CGGAGCCCTGCGCGCCCAGAGCCCCCTCCCACCCGCTTCCACCAAG... \n",
"AK004937 15 CAGACACAAACCCTAGGTTGTATTGTAGACCGGAGTTTAAGCAGGC... \n",
"AK005789 10 TGCAGAAGGCATTCCAATCCGAACAACCCTGGACAACTCCACAACG... \n",
"AK010722 12 GGAGCATCTGGAGTTCCGCTTACCGGAAATAAAGTCTTTACTATCG... \n",
"AK015719 2 CACCAGGCTCAGTGCCTAGTATCGGCTTCACCTAGTGTGGTTACTC... \n",
"BC003241 14 AAATTAGAAGAAAATCCATATGACCTTGATGCTTGGAGCATTCTCA... \n",
"BC003333 4 AGAACTGGGTGGCAGATATCCTAGAGTTTTGACCAACGTTCACAGC... \n",
"NM_008029 6 GAGGTGCTGTGGGATGACCGCCGGGGCATGCGGGTGCCCACTCAAC... \n",
"NM_008154 1 CTGTACAATGCTCTCACTTACTACTCAGAGACAACGGTAACTCGGA... \n",
"NM_008462 5 TGAATTGAAGTTCCTTAAATCCCAACTTCAAAGAAACACATACTGG... \n",
"NM_009750 8 TACAGCTGAGAAATTGTCTACGCATCCTTATGGGGGAGCTGTCTAA... \n",
"NM_010517 11 GGAGAAGCTGGCGCGCTGCCGCCCCCCCGTGGGTTGCGAGGAGTTG... \n",
"NM_023120 19 ACCGCCTGGTCCCAGATTTGTCCTCCGAGGCACACAGTCGGCTGTG... \n",
"NM_025999 17 TGCATTGATAAATGGAGTGATCGACACAGGAACTGCCCCATTTGTC... \n",
"NM_054088 7 GTCTGAGTTCCATTCCAAAGACGAAGTCGTGGATGCCCTGGTGTGT... \n",
"U02079 0 ACCTGGATGACGCAGCCACTTCAGAAAGCTGGGTTGGGACAGAAAG... "
],
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>ID_REF</th>\n",
" <th>VALUE</th>\n",
" <th>LogRatioError</th>\n",
" <th>PValueLogRatio</th>\n",
" <th>gProcessedSignal</th>\n",
" <th>rProcessedSignal</th>\n",
" <th>ID</th>\n",
" <th>GB_ACC</th>\n",
" <th>Gene_Desc</th>\n",
" <th>Gene_Sym</th>\n",
" <th>SPOT_ID</th>\n",
" <th>SEQUENCE</th>\n",
" </tr>\n",
" <tr>\n",
" <th>GB_ACC</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AB045323</th>\n",
" <th>9</th>\n",
" <td>9</td>\n",
" <td>10</td>\n",
" <td>0.240589</td>\n",
" <td>0.3090</td>\n",
" <td>4.360000e-01</td>\n",
" <td>161.0</td>\n",
" <td>280.00</td>\n",
" <td>10</td>\n",
" <td>AB045323</td>\n",
" <td>DNA segment, Chr 8, ERATO Doi 594, expressed</td>\n",
" <td>D8Ertd594e</td>\n",
" <td>NaN</td>\n",
" <td>GATTCAGACTCGGGAGGAGCATCCCAACCTCTCCTTGAGGATAAAG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK003367</th>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>-0.393227</td>\n",
" <td>0.0608</td>\n",
" <td>1.020000e-10</td>\n",
" <td>4650.0</td>\n",
" <td>1880.00</td>\n",
" <td>4</td>\n",
" <td>AK003367</td>\n",
" <td>mitochondrial ribosomal protein L15</td>\n",
" <td>Mrpl15</td>\n",
" <td>NaN</td>\n",
" <td>CAAGAAGTCTAGAAATTCTGTGCAAGCCTATTCCATTCTTTCTGCG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK003755</th>\n",
" <th>13</th>\n",
" <td>13</td>\n",
" <td>14</td>\n",
" <td>-1.548040</td>\n",
" <td>0.1300</td>\n",
" <td>7.210000e-33</td>\n",
" <td>10200.0</td>\n",
" <td>290.00</td>\n",
" <td>14</td>\n",
" <td>AK003755</td>\n",
" <td>DNA segment, Chr 4, ERATO Doi 421, expressed</td>\n",
" <td>D4Ertd421e</td>\n",
" <td>NaN</td>\n",
" <td>AGCAAAGAGATCTCCCTCAGTGTGCCCATAGGTGGCGGTGCGAGCT...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK004524</th>\n",
" <th>16</th>\n",
" <td>16</td>\n",
" <td>17</td>\n",
" <td>-0.148402</td>\n",
" <td>0.8010</td>\n",
" <td>8.530000e-01</td>\n",
" <td>96.5</td>\n",
" <td>68.60</td>\n",
" <td>17</td>\n",
" <td>AK004524</td>\n",
" <td>unnamed protein product; hypothetical SOCS domain</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CGGAGCCCTGCGCGCCCAGAGCCCCCTCCCACCCGCTTCCACCAAG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK004937</th>\n",
" <th>15</th>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>-0.226702</td>\n",
" <td>0.9440</td>\n",
" <td>8.100000e-01</td>\n",
" <td>89.0</td>\n",
" <td>52.80</td>\n",
" <td>16</td>\n",
" <td>AK004937</td>\n",
" <td>RIKEN cDNA 1300007O09 gene</td>\n",
" <td>1300007O09Rik</td>\n",
" <td>NaN</td>\n",
" <td>CAGACACAAACCCTAGGTTGTATTGTAGACCGGAGTTTAAGCAGGC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK005789</th>\n",
" <th>10</th>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>0.320937</td>\n",
" <td>0.3590</td>\n",
" <td>3.710000e-01</td>\n",
" <td>125.0</td>\n",
" <td>261.00</td>\n",
" <td>11</td>\n",
" <td>AK005789</td>\n",
" <td>dynein, cytoplasmic, light chain 2B</td>\n",
" <td>Dncl2b</td>\n",
" <td>NaN</td>\n",
" <td>TGCAGAAGGCATTCCAATCCGAACAACCCTGGACAACTCCACAACG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK010722</th>\n",
" <th>12</th>\n",
" <td>12</td>\n",
" <td>13</td>\n",
" <td>-0.012207</td>\n",
" <td>0.3640</td>\n",
" <td>9.730000e-01</td>\n",
" <td>184.0</td>\n",
" <td>179.00</td>\n",
" <td>13</td>\n",
" <td>AK010722</td>\n",
" <td>RIKEN cDNA 2410075D05 gene</td>\n",
" <td>2410075D05Rik</td>\n",
" <td>NaN</td>\n",
" <td>GGAGCATCTGGAGTTCCGCTTACCGGAAATAAAGTCTTTACTATCG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AK015719</th>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0.182768</td>\n",
" <td>0.0519</td>\n",
" <td>4.330000e-04</td>\n",
" <td>5130.0</td>\n",
" <td>7810.00</td>\n",
" <td>3</td>\n",
" <td>AK015719</td>\n",
" <td>tropomodulin 2</td>\n",
" <td>Tmod2</td>\n",
" <td>NaN</td>\n",
" <td>CACCAGGCTCAGTGCCTAGTATCGGCTTCACCTAGTGTGGTTACTC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BC003241</th>\n",
" <th>14</th>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" <td>0.007342</td>\n",
" <td>0.2980</td>\n",
" <td>9.800000e-01</td>\n",
" <td>221.0</td>\n",
" <td>225.00</td>\n",
" <td>15</td>\n",
" <td>BC003241</td>\n",
" <td>cleavage stimulation factor, 3\\' pre-RNA, subu...</td>\n",
" <td>Cstf3</td>\n",
" <td>NaN</td>\n",
" <td>AAATTAGAAGAAAATCCATATGACCTTGATGCTTGGAGCATTCTCA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BC003333</th>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>-0.986599</td>\n",
" <td>0.1050</td>\n",
" <td>6.320000e-21</td>\n",
" <td>2910.0</td>\n",
" <td>301.00</td>\n",
" <td>5</td>\n",
" <td>BC003333</td>\n",
" <td>RIKEN cDNA 0610033I05 gene</td>\n",
" <td>0610033I05Rik</td>\n",
" <td>NaN</td>\n",
" <td>AGAACTGGGTGGCAGATATCCTAGAGTTTTGACCAACGTTCACAGC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_008029</th>\n",
" <th>6</th>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>-1.484182</td>\n",
" <td>0.1250</td>\n",
" <td>1.420000e-32</td>\n",
" <td>10200.0</td>\n",
" <td>336.00</td>\n",
" <td>7</td>\n",
" <td>NM_008029</td>\n",
" <td>FMS-like tyrosine kinase 4</td>\n",
" <td>Flt4</td>\n",
" <td>NaN</td>\n",
" <td>GAGGTGCTGTGGGATGACCGCCGGGGCATGCGGGTGCCCACTCAAC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_008154</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0.141225</td>\n",
" <td>1.3400</td>\n",
" <td>1.000000e+00</td>\n",
" <td>41.4</td>\n",
" <td>57.20</td>\n",
" <td>2</td>\n",
" <td>NM_008154</td>\n",
" <td>G-protein coupled receptor 3</td>\n",
" <td>Gpr3</td>\n",
" <td>NaN</td>\n",
" <td>CTGTACAATGCTCTCACTTACTACTCAGAGACAACGGTAACTCGGA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_008462</th>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>0.023881</td>\n",
" <td>0.1020</td>\n",
" <td>8.150000e-01</td>\n",
" <td>708.0</td>\n",
" <td>748.00</td>\n",
" <td>6</td>\n",
" <td>NM_008462</td>\n",
" <td>killer cell lectin-like receptor, subfamily A,...</td>\n",
" <td>Klra2</td>\n",
" <td>NaN</td>\n",
" <td>TGAATTGAAGTTCCTTAAATCCCAACTTCAAAGAAACACATACTGG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_009750</th>\n",
" <th>8</th>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>-1.034478</td>\n",
" <td>1.7800</td>\n",
" <td>1.000000e+00</td>\n",
" <td>96.2</td>\n",
" <td>8.89</td>\n",
" <td>9</td>\n",
" <td>NM_009750</td>\n",
" <td>nerve growth factor receptor (TNFRSF16) associ...</td>\n",
" <td>Ngfrap1</td>\n",
" <td>NaN</td>\n",
" <td>TACAGCTGAGAAATTGTCTACGCATCCTTATGGGGGAGCTGTCTAA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_010517</th>\n",
" <th>11</th>\n",
" <td>11</td>\n",
" <td>12</td>\n",
" <td>0.358304</td>\n",
" <td>2.0600</td>\n",
" <td>1.000000e+00</td>\n",
" <td>20.4</td>\n",
" <td>46.60</td>\n",
" <td>12</td>\n",
" <td>NM_010517</td>\n",
" <td>insulin-like growth factor binding protein 4</td>\n",
" <td>Igfbp4</td>\n",
" <td>NaN</td>\n",
" <td>GGAGAAGCTGGCGCGCTGCCGCCCCCCCGTGGGTTGCGAGGAGTTG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_023120</th>\n",
" <th>19</th>\n",
" <td>19</td>\n",
" <td>20</td>\n",
" <td>-0.084895</td>\n",
" <td>0.9380</td>\n",
" <td>9.280000e-01</td>\n",
" <td>76.8</td>\n",
" <td>63.20</td>\n",
" <td>20</td>\n",
" <td>NM_023120</td>\n",
" <td>guanine nucleotide binding protein (G protein)...</td>\n",
" <td>Gnb1l</td>\n",
" <td>NaN</td>\n",
" <td>ACCGCCTGGTCCCAGATTTGTCCTCCGAGGCACACAGTCGGCTGTG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_025999</th>\n",
" <th>17</th>\n",
" <td>17</td>\n",
" <td>18</td>\n",
" <td>-0.612220</td>\n",
" <td>0.1280</td>\n",
" <td>1.690000e-06</td>\n",
" <td>1120.0</td>\n",
" <td>273.00</td>\n",
" <td>18</td>\n",
" <td>NM_025999</td>\n",
" <td>RIKEN cDNA 2610110L04 gene</td>\n",
" <td>2610110L04Rik</td>\n",
" <td>NaN</td>\n",
" <td>TGCATTGATAAATGGAGTGATCGACACAGGAACTGCCCCATTTGTC...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NM_054088</th>\n",
" <th>7</th>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>-1.826136</td>\n",
" <td>0.4150</td>\n",
" <td>1.100000e-05</td>\n",
" <td>719.0</td>\n",
" <td>10.70</td>\n",
" <td>8</td>\n",
" <td>NM_054088</td>\n",
" <td>adiponutrin</td>\n",
" <td>Adpn</td>\n",
" <td>NaN</td>\n",
" <td>GTCTGAGTTCCATTCCAAAGACGAAGTCGTGGATGCCCTGGTGTGT...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>U02079</th>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>-1.627476</td>\n",
" <td>0.1360</td>\n",
" <td>6.410000e-33</td>\n",
" <td>9130.0</td>\n",
" <td>215.00</td>\n",
" <td>1</td>\n",
" <td>U02079</td>\n",
" <td>nuclear factor of activated T-cells, cytoplasm...</td>\n",
" <td>Nfatc2</td>\n",
" <td>NaN</td>\n",
" <td>ACCTGGATGACGCAGCCACTTCAGAAAGCTGGGTTGGGACAGAAAG...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>\n",
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" document.querySelector('#df-bde1c7f3-472d-47d0-930f-26320bb422d9 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
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"repr_error": "Out of range float values are not JSON compliant: nan"
}
},
"metadata": {},
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}
],
"source": [
"tmp_data.groupby(group_by_column).apply(lambda x: x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58bafa3a",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 677
},
"id": "58bafa3a",
"outputId": "ab1c9967-db5f-41d2-f5e5-f22ebfe2051f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" VALUE\n",
"GB_ACC \n",
"AB045323 0.240589\n",
"AK003367 -0.393227\n",
"AK003755 -1.548040\n",
"AK004524 -0.148402\n",
"AK004937 -0.226702\n",
"AK005789 0.320937\n",
"AK010722 -0.012207\n",
"AK015719 0.182768\n",
"BC003241 0.007342\n",
"BC003333 -0.986599\n",
"NM_008029 -1.484182\n",
"NM_008154 0.141225\n",
"NM_008462 0.023881\n",
"NM_009750 -1.034478\n",
"NM_010517 0.358304\n",
"NM_023120 -0.084895\n",
"NM_025999 -0.612220\n",
"NM_054088 -1.826136\n",
"U02079 -1.627476"
],
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" <td>-0.084895</td>\n",
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" <td>-0.612220</td>\n",
" </tr>\n",
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" <th>NM_054088</th>\n",
" <td>-1.826136</td>\n",
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],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"summary": "{\n \"name\": \"tmp_data\",\n \"rows\": 19,\n \"fields\": [\n {\n \"column\": \"GB_ACC\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 19,\n \"samples\": [\n \"AB045323\",\n \"AK005789\",\n \"NM_008154\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"VALUE\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7298113867225522,\n \"min\": -1.826136,\n \"max\": 0.358304,\n \"num_unique_values\": 19,\n \"samples\": [\n 0.240589,\n 0.320937,\n 0.141225\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"tmp_data.groupby(group_by_column)[[expression_column]].mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "363dbc48",
"metadata": {
"id": "363dbc48"
},
"outputs": [],
"source": []
}
],
"metadata": {
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"language": "python",
"name": "python3"
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"version": 3
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython3",
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"colab": {
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}
Raw
tmp_data.csv
ID_REF VALUE LogRatioError PValueLogRatio gProcessedSignal rProcessedSignal ID GB_ACC Gene_Desc Gene_Sym SPOT_ID SEQUENCE
0 1 -1.627476 0.1360 6.410000e-33 9130.0 215.00 1 U02079 nuclear factor of activated T-cells, cytoplasmic 2 Nfatc2 NaN ACCTGGATGACGCAGCCACTTCAGAAAGCTGGGTTGGGACAGAAAGGTATATAGAGAGAAAATTTTGGAA
1 2 0.141225 1.3400 1.000000e+00 41.4 57.20 2 NM_008154 G-protein coupled receptor 3 Gpr3 NaN CTGTACAATGCTCTCACTTACTACTCAGAGACAACGGTAACTCGGACTTATGTGATGCTGGCCTTGGTGT
2 3 0.182768 0.0519 4.330000e-04 5130.0 7810.00 3 AK015719 tropomodulin 2 Tmod2 NaN CACCAGGCTCAGTGCCTAGTATCGGCTTCACCTAGTGTGGTTACTCAGGGCACGCAGAGCTACAGAACAC
3 4 -0.393227 0.0608 1.020000e-10 4650.0 1880.00 4 AK003367 mitochondrial ribosomal protein L15 Mrpl15 NaN CAAGAAGTCTAGAAATTCTGTGCAAGCCTATTCCATTCTTTCTGCGGGGACAACCAATTCCGAAAAGAAT
4 5 -0.986599 0.1050 6.320000e-21 2910.0 301.00 5 BC003333 RIKEN cDNA 0610033I05 gene 0610033I05Rik NaN AGAACTGGGTGGCAGATATCCTAGAGTTTTGACCAACGTTCACAGCACACATATTGATCTTATAGGACCT
5 6 0.023881 0.1020 8.150000e-01 708.0 748.00 6 NM_008462 killer cell lectin-like receptor, subfamily A, member 2 Klra2 NaN TGAATTGAAGTTCCTTAAATCCCAACTTCAAAGAAACACATACTGGATTTCACTGACACATCATAAAAGC
6 7 -1.484182 0.1250 1.420000e-32 10200.0 336.00 7 NM_008029 FMS-like tyrosine kinase 4 Flt4 NaN GAGGTGCTGTGGGATGACCGCCGGGGCATGCGGGTGCCCACTCAACTGTTGCGCGATGCCCTGTACCTGC
7 8 -1.826136 0.4150 1.100000e-05 719.0 10.70 8 NM_054088 adiponutrin Adpn NaN GTCTGAGTTCCATTCCAAAGACGAAGTCGTGGATGCCCTGGTGTGTTCCTGCTTCATTCCCCTCTTCTCT
8 9 -1.034478 1.7800 1.000000e+00 96.2 8.89 9 NM_009750 nerve growth factor receptor (TNFRSF16) associated protein 1 Ngfrap1 NaN TACAGCTGAGAAATTGTCTACGCATCCTTATGGGGGAGCTGTCTAACCACCACGATCACCATGATGAATT
9 10 0.240589 0.3090 4.360000e-01 161.0 280.00 10 AB045323 DNA segment, Chr 8, ERATO Doi 594, expressed D8Ertd594e NaN GATTCAGACTCGGGAGGAGCATCCCAACCTCTCCTTGAGGATAAAGGCCTGAGCGATTGCCCTGGGGAGC
10 11 0.320937 0.3590 3.710000e-01 125.0 261.00 11 AK005789 dynein, cytoplasmic, light chain 2B Dncl2b NaN TGCAGAAGGCATTCCAATCCGAACAACCCTGGACAACTCCACAACGGTTCAGTATGCGGGTCTTCTCCAC
11 12 0.358304 2.0600 1.000000e+00 20.4 46.60 12 NM_010517 insulin-like growth factor binding protein 4 Igfbp4 NaN GGAGAAGCTGGCGCGCTGCCGCCCCCCCGTGGGTTGCGAGGAGTTGGTGCGGGAGCCAGGCTGCGGTTGT
12 13 -0.012207 0.3640 9.730000e-01 184.0 179.00 13 AK010722 RIKEN cDNA 2410075D05 gene 2410075D05Rik NaN GGAGCATCTGGAGTTCCGCTTACCGGAAATAAAGTCTTTACTATCGGTGATTGGAGGGCAGTTCACTAAC
13 14 -1.548040 0.1300 7.210000e-33 10200.0 290.00 14 AK003755 DNA segment, Chr 4, ERATO Doi 421, expressed D4Ertd421e NaN AGCAAAGAGATCTCCCTCAGTGTGCCCATAGGTGGCGGTGCGAGCTTGCGGTTATTGGCCAGTGACTTGC
14 15 0.007342 0.2980 9.800000e-01 221.0 225.00 15 BC003241 cleavage stimulation factor, 3\' pre-RNA, subunit 3 Cstf3 NaN AAATTAGAAGAAAATCCATATGACCTTGATGCTTGGAGCATTCTCATTCGAGAGGCACAGAATCAACCTA
15 16 -0.226702 0.9440 8.100000e-01 89.0 52.80 16 AK004937 RIKEN cDNA 1300007O09 gene 1300007O09Rik NaN CAGACACAAACCCTAGGTTGTATTGTAGACCGGAGTTTAAGCAGGCACTACCTGTCTGTCTTTTCTTCAT
16 17 -0.148402 0.8010 8.530000e-01 96.5 68.60 17 AK004524 unnamed protein product; hypothetical SOCS domain NaN NaN CGGAGCCCTGCGCGCCCAGAGCCCCCTCCCACCCGCTTCCACCAAGTGCATGGAGCCAACATCCGCATGG
17 18 -0.612220 0.1280 1.690000e-06 1120.0 273.00 18 NM_025999 RIKEN cDNA 2610110L04 gene 2610110L04Rik NaN TGCATTGATAAATGGAGTGATCGACACAGGAACTGCCCCATTTGTCGCCTACAGATGACTGGAGCAAATG
18 19 0.079690 0.0878 3.640000e-01 821.0 987.00 19 NaN NaN NaN -- CONTROL NaN
19 20 -0.084895 0.9380 9.280000e-01 76.8 63.20 20 NM_023120 guanine nucleotide binding protein (G protein), beta polypeptide 1-like Gnb1l NaN ACCGCCTGGTCCCAGATTTGTCCTCCGAGGCACACAGTCGGCTGTGAACACGCTCCATTTCTGCCCACCA
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