{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use Case 10: Reactome Pathways"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Enrichment analysis helps to focus on a subset of genes that participate in the same pathway from a larger gene set. Visualizing the pathway can aid further examination. In this use case, we demonstrate how to visualize results on Reactome Pathways using python packages."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1: Importing packages\n",
"Various Python packages are needed to download data from cptac, conduct statistical analysis to find meaningful protein clusters, and overlay pathway diagrams found on Reactome. The cptac.utils package offers a plethora of functions for these tasks. You can find more details by using help(cptac.utils)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cptac\n",
"import pandas as pd\n",
"import cptac.utils as utils\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import statsmodels as statsmodels\n",
"from IPython.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 2: Acquiring Data\n",
"\n",
"Even after importing the cptac package, we still need to load specific datasets. Here, we load the Renal Cancer proteomics data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"cptac warning: Your version of cptac (1.5.1) is out-of-date. Latest is 1.5.0. Please run 'pip install --upgrade cptac' to update it. (C:\\Users\\sabme\\anaconda3\\lib\\threading.py, line 910)\n"
]
},
{
"data": {
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" <thead>\n",
" <tr>\n",
" <th>Name</th>\n",
" <th>ARF5</th>\n",
" <th>M6PR</th>\n",
" <th>ESRRA</th>\n",
" <th>FKBP4</th>\n",
" <th>NDUFAF7</th>\n",
" <th>FUCA2</th>\n",
" <th>DBNDD1</th>\n",
" <th>CFTR</th>\n",
" <th>CYP51A1</th>\n",
" <th>USP28</th>\n",
" <th>...</th>\n",
" <th colspan=\"2\" halign=\"left\">ANK2</th>\n",
" <th>ATXN2</th>\n",
" <th>ETNK1</th>\n",
" <th>MYO6</th>\n",
" <th>GBF1</th>\n",
" <th>CTNND1</th>\n",
" <th>PRX</th>\n",
" <th>WIZ</th>\n",
" <th>HSPA12A</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Database_ID</th>\n",
" <th>ENSP00000000233.5</th>\n",
" <th>ENSP00000000412.3</th>\n",
" <th>ENSP00000000442.6</th>\n",
" <th>ENSP00000001008.4</th>\n",
" <th>ENSP00000002125.4</th>\n",
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" <th>...</th>\n",
" <th>ENSP00000500245.1</th>\n",
" <th>ENSP00000500275.1</th>\n",
" <th>ENSP00000500313.1</th>\n",
" <th>ENSP00000500633.1</th>\n",
" <th>ENSP00000500710.1</th>\n",
" <th>ENSP00000501064.1</th>\n",
" <th>ENSP00000501126.1</th>\n",
" <th>ENSP00000501261.1</th>\n",
" <th>ENSP00000501300.1</th>\n",
" <th>ENSP00000501491.1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Patient_ID</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",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>C3L-00004</th>\n",
" <td>0.248796</td>\n",
" <td>0.308289</td>\n",
" <td>-0.315825</td>\n",
" <td>0.051528</td>\n",
" <td>-0.323475</td>\n",
" <td>-0.720420</td>\n",
" <td>NaN</td>\n",
" <td>-0.832085</td>\n",
" <td>-0.629139</td>\n",
" <td>-0.317251</td>\n",
" <td>...</td>\n",
" <td>0.359093</td>\n",
" <td>1.609902</td>\n",
" <td>0.054346</td>\n",
" <td>-0.471825</td>\n",
" <td>-0.095824</td>\n",
" <td>0.240579</td>\n",
" <td>0.689392</td>\n",
" <td>NaN</td>\n",
" <td>-0.072279</td>\n",
" <td>-0.655139</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C3L-00010</th>\n",
" <td>0.245087</td>\n",
" <td>0.131992</td>\n",
" <td>-0.016811</td>\n",
" <td>-0.114728</td>\n",
" <td>-0.816992</td>\n",
" <td>0.104260</td>\n",
" <td>NaN</td>\n",
" <td>1.266034</td>\n",
" <td>-0.573329</td>\n",
" <td>-0.172712</td>\n",
" <td>...</td>\n",
" <td>-1.592004</td>\n",
" <td>-0.542294</td>\n",
" <td>NaN</td>\n",
" <td>-0.208478</td>\n",
" <td>-0.143536</td>\n",
" <td>0.049889</td>\n",
" <td>0.499405</td>\n",
" <td>1.422138</td>\n",
" <td>-0.058976</td>\n",
" <td>-0.189293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C3L-00011</th>\n",
" <td>0.576821</td>\n",
" <td>0.443678</td>\n",
" <td>-0.544452</td>\n",
" <td>0.120776</td>\n",
" <td>-0.075035</td>\n",
" <td>-0.738826</td>\n",
" <td>NaN</td>\n",
" <td>-0.484732</td>\n",
" <td>-0.725274</td>\n",
" <td>1.079570</td>\n",
" <td>...</td>\n",
" <td>-0.523301</td>\n",
" <td>-0.389680</td>\n",
" <td>NaN</td>\n",
" <td>0.550085</td>\n",
" <td>-1.617542</td>\n",
" <td>0.485815</td>\n",
" <td>-0.327129</td>\n",
" <td>NaN</td>\n",
" <td>0.344999</td>\n",
" <td>-0.688779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C3L-00026</th>\n",
" <td>0.293042</td>\n",
" <td>0.250350</td>\n",
" <td>0.142924</td>\n",
" <td>-0.173812</td>\n",
" <td>0.589458</td>\n",
" <td>0.238597</td>\n",
" <td>0.957386</td>\n",
" <td>-3.178976</td>\n",
" <td>0.372489</td>\n",
" <td>0.404721</td>\n",
" <td>...</td>\n",
" <td>-0.249875</td>\n",
" <td>-1.492455</td>\n",
" <td>NaN</td>\n",
" <td>0.005406</td>\n",
" <td>-0.281776</td>\n",
" <td>0.057764</td>\n",
" <td>-0.073197</td>\n",
" <td>NaN</td>\n",
" <td>-0.159555</td>\n",
" <td>-0.023920</td>\n",
" </tr>\n",
" <tr>\n",
" <th>C3L-00079</th>\n",
" <td>0.174315</td>\n",
" <td>0.026805</td>\n",
" <td>-0.663191</td>\n",
" <td>0.075943</td>\n",
" <td>-0.240099</td>\n",
" <td>-0.293380</td>\n",
" <td>NaN</td>\n",
" <td>0.008966</td>\n",
" <td>-0.469829</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>-1.423105</td>\n",
" <td>-0.921139</td>\n",
" <td>NaN</td>\n",
" <td>-0.042932</td>\n",
" <td>-0.921862</td>\n",
" <td>0.385364</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.387207</td>\n",
" <td>-1.057636</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 11889 columns</p>\n",
"</div>"
],
"text/plain": [
"Name ARF5 M6PR ESRRA \\\n",
"Database_ID ENSP00000000233.5 ENSP00000000412.3 ENSP00000000442.6 \n",
"Patient_ID \n",
"C3L-00004 0.248796 0.308289 -0.315825 \n",
"C3L-00010 0.245087 0.131992 -0.016811 \n",
"C3L-00011 0.576821 0.443678 -0.544452 \n",
"C3L-00026 0.293042 0.250350 0.142924 \n",
"C3L-00079 0.174315 0.026805 -0.663191 \n",
"\n",
"Name FKBP4 NDUFAF7 FUCA2 \\\n",
"Database_ID ENSP00000001008.4 ENSP00000002125.4 ENSP00000002165.5 \n",
"Patient_ID \n",
"C3L-00004 0.051528 -0.323475 -0.720420 \n",
"C3L-00010 -0.114728 -0.816992 0.104260 \n",
"C3L-00011 0.120776 -0.075035 -0.738826 \n",
"C3L-00026 -0.173812 0.589458 0.238597 \n",
"C3L-00079 0.075943 -0.240099 -0.293380 \n",
"\n",
"Name DBNDD1 CFTR CYP51A1 \\\n",
"Database_ID ENSP00000002501.6 ENSP00000003084.6 ENSP00000003100.8 \n",
"Patient_ID \n",
"C3L-00004 NaN -0.832085 -0.629139 \n",
"C3L-00010 NaN 1.266034 -0.573329 \n",
"C3L-00011 NaN -0.484732 -0.725274 \n",
"C3L-00026 0.957386 -3.178976 0.372489 \n",
"C3L-00079 NaN 0.008966 -0.469829 \n",
"\n",
"Name USP28 ... ANK2 \\\n",
"Database_ID ENSP00000003302.4 ... ENSP00000500245.1 ENSP00000500275.1 \n",
"Patient_ID ... \n",
"C3L-00004 -0.317251 ... 0.359093 1.609902 \n",
"C3L-00010 -0.172712 ... -1.592004 -0.542294 \n",
"C3L-00011 1.079570 ... -0.523301 -0.389680 \n",
"C3L-00026 0.404721 ... -0.249875 -1.492455 \n",
"C3L-00079 NaN ... -1.423105 -0.921139 \n",
"\n",
"Name ATXN2 ETNK1 MYO6 \\\n",
"Database_ID ENSP00000500313.1 ENSP00000500633.1 ENSP00000500710.1 \n",
"Patient_ID \n",
"C3L-00004 0.054346 -0.471825 -0.095824 \n",
"C3L-00010 NaN -0.208478 -0.143536 \n",
"C3L-00011 NaN 0.550085 -1.617542 \n",
"C3L-00026 NaN 0.005406 -0.281776 \n",
"C3L-00079 NaN -0.042932 -0.921862 \n",
"\n",
"Name GBF1 CTNND1 PRX \\\n",
"Database_ID ENSP00000501064.1 ENSP00000501126.1 ENSP00000501261.1 \n",
"Patient_ID \n",
"C3L-00004 0.240579 0.689392 NaN \n",
"C3L-00010 0.049889 0.499405 1.422138 \n",
"C3L-00011 0.485815 -0.327129 NaN \n",
"C3L-00026 0.057764 -0.073197 NaN \n",
"C3L-00079 0.385364 NaN NaN \n",
"\n",
"Name WIZ HSPA12A \n",
"Database_ID ENSP00000501300.1 ENSP00000501491.1 \n",
"Patient_ID \n",
"C3L-00004 -0.072279 -0.655139 \n",
"C3L-00010 -0.058976 -0.189293 \n",
"C3L-00011 0.344999 -0.688779 \n",
"C3L-00026 -0.159555 -0.023920 \n",
"C3L-00079 0.387207 -1.057636 \n",
"\n",
"[5 rows x 11889 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = cptac.Ccrcc()\n",
"df = ds.get_proteomics('umich')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"Utilizing utils, we can pull data from a range of large databases. In this example, the Hugo Gene Nomenclature database is queried for a list of proteins in the proteosome."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PSMA4', 'PSMC4', 'PSME1', 'PSMD5', 'PSMD8', 'PSMA3', 'PSME2', 'PSMD10',\n",
" 'PSMD7', 'PSMF1', 'ADRM1', 'PSMB7', 'PSMC1', 'PSMA6', 'PSMD11', 'PSMB1',\n",
" 'PSMD3', 'PSMB6', 'PSMA5', 'PSMB4', 'PSMC2', 'PSME3', 'PSMD6', 'PSMC3',\n",
" 'PSMD1', 'PSMD2', 'PSMC5', 'PSMD12', 'PSMB10', 'PSMB5', 'PSMD4',\n",
" 'PSMA7', 'PSMB2', 'PSMB9', 'PSMB8', 'PSMA1', 'PSME4', 'PSMD14', 'PSMC6',\n",
" 'PSMD13', 'PSMD9', 'PSMB3'],\n",
" dtype='object', name='Name')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hgnc_lists = utils.get_hgnc_protein_lists()\n",
"proteasome_proteins = sorted(set(hgnc_lists[\"Proteasome\"]))\n",
"included_cols = df.columns.get_level_values(0).intersection(proteasome_proteins)\n",
"included_cols"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3: Identifying Differences between Tumor and Normal Conditions\n",
"\n",
"We loop through the list of proteins, separating tumor and normal data from each other. A t-test is then performed on both data lists. For each protein with a p-value less than .05, the protein name, p-value, and the difference between tumor mean and normal mean is stored in separate lists."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"nameList =[]\n",
"pVal_list = []\n",
"deltaList = []\n",
"selected_proteins = df[included_cols]\n",
"\n",
"for protein in selected_proteins.columns:\n",
" data = selected_proteins[protein]\n",
" normal = data[data.index.str.endswith('.N')]\n",
" tumor = data[~data.index.str.endswith('.N')]\n",
" pVal = stats.ttest_ind(normal, tumor, equal_var = False, nan_policy='omit').pvalue \n",
" \n",
" if pVal < .05:\n",
" name = protein[0]\n",
" delta = tumor.mean() - normal.mean()\n",
" nameList.append(name)\n",
" pVal_list.append(pVal)\n",
" deltaList.append(delta)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We apply the Benjamini & Hochberg/Yekutieli correction to the list of p-values calculated in the previous cell. A dataframe is then constructed using the lists of names, p-values, and differences between tumor mean and normal mean."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"reject_null, adj_p, alpha_sidak, alpha_bonf = statsmodels.stats.multitest.multipletests(pVal_list, alpha=0.05, method='fdr_bh')\n",
"pVal_list = adj_p\n",
"zippedList = list(zip(nameList, pVal_list, deltaList))\n",
"dfObj = pd.DataFrame(zippedList, columns = ['Name', 'P-Value', 'Delta'])\n",
"dfObj = dfObj.set_index('Name')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then simplify the abundance change (Delta column). An increase is assigned 1, a decrease is assigned -1, and no change is set at 0."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dfObj.insert(1, \"simplified_change\", dfObj[\"Delta\"])\n",
"dfObj[\"simplified_change\"][dfObj[\"Delta\"] > 0] = 1\n",
"dfObj[\"simplified_change\"][dfObj[\"Delta\"] < 0] = -1\n",
"dfObj[\"simplified_change\"][dfObj[\"Delta\"] == 0] = 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the Benjamini & Hochberg/Yekutieli correction function we are able to also evaluate whether or not to reject the null. This is evaluated and added as a column in the dataframe."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>P-Value</th>\n",
" <th>simplified_change</th>\n",
" <th>Delta</th>\n",
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" <th>reject_null</th>\n",
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" <tr>\n",
" <th>Name</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>PSMA4</th>\n",
" <td>1.911611e-10</td>\n",
" <td>1.0</td>\n",
" <td>0.148198</td>\n",
" <td>1.911611e-10</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC4</th>\n",
" <td>1.391607e-09</td>\n",
" <td>1.0</td>\n",
" <td>0.112355</td>\n",
" <td>1.391607e-09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSME1</th>\n",
" <td>2.008155e-13</td>\n",
" <td>1.0</td>\n",
" <td>0.316319</td>\n",
" <td>2.008155e-13</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD5</th>\n",
" <td>2.182605e-11</td>\n",
" <td>1.0</td>\n",
" <td>0.217871</td>\n",
" <td>2.182605e-11</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD8</th>\n",
" <td>7.052978e-09</td>\n",
" <td>1.0</td>\n",
" <td>0.138417</td>\n",
" <td>7.052978e-09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMA3</th>\n",
" <td>1.075243e-12</td>\n",
" <td>1.0</td>\n",
" <td>0.177574</td>\n",
" <td>1.075243e-12</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSME2</th>\n",
" <td>8.868141e-36</td>\n",
" <td>1.0</td>\n",
" <td>0.671231</td>\n",
" <td>8.868141e-36</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD10</th>\n",
" <td>1.370257e-56</td>\n",
" <td>1.0</td>\n",
" <td>0.468575</td>\n",
" <td>1.370257e-56</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD7</th>\n",
" <td>1.926944e-05</td>\n",
" <td>1.0</td>\n",
" <td>0.080727</td>\n",
" <td>1.926944e-05</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMF1</th>\n",
" <td>2.042107e-05</td>\n",
" <td>1.0</td>\n",
" <td>0.184903</td>\n",
" <td>2.042107e-05</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMF1</th>\n",
" <td>1.563592e-27</td>\n",
" <td>1.0</td>\n",
" <td>0.381929</td>\n",
" <td>1.563592e-27</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ADRM1</th>\n",
" <td>4.086117e-03</td>\n",
" <td>1.0</td>\n",
" <td>0.068020</td>\n",
" <td>4.086117e-03</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB7</th>\n",
" <td>1.087052e-08</td>\n",
" <td>-1.0</td>\n",
" <td>-0.263777</td>\n",
" <td>1.087052e-08</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC1</th>\n",
" <td>2.837536e-18</td>\n",
" <td>1.0</td>\n",
" <td>0.169574</td>\n",
" <td>2.837536e-18</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMA6</th>\n",
" <td>4.235036e-24</td>\n",
" <td>1.0</td>\n",
" <td>0.277224</td>\n",
" <td>4.235036e-24</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD11</th>\n",
" <td>4.601922e-04</td>\n",
" <td>1.0</td>\n",
" <td>0.064925</td>\n",
" <td>4.601922e-04</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB1</th>\n",
" <td>1.976143e-29</td>\n",
" <td>1.0</td>\n",
" <td>0.278219</td>\n",
" <td>1.976143e-29</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD3</th>\n",
" <td>2.011238e-12</td>\n",
" <td>1.0</td>\n",
" <td>0.141735</td>\n",
" <td>2.011238e-12</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB6</th>\n",
" <td>5.243052e-27</td>\n",
" <td>-1.0</td>\n",
" <td>-0.716784</td>\n",
" <td>5.243052e-27</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMA5</th>\n",
" <td>2.114399e-35</td>\n",
" <td>1.0</td>\n",
" <td>0.302883</td>\n",
" <td>2.114399e-35</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB4</th>\n",
" <td>1.921437e-21</td>\n",
" <td>1.0</td>\n",
" <td>0.255068</td>\n",
" <td>1.921437e-21</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC2</th>\n",
" <td>4.943515e-10</td>\n",
" <td>1.0</td>\n",
" <td>0.109437</td>\n",
" <td>4.943515e-10</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSME3</th>\n",
" <td>8.587448e-06</td>\n",
" <td>-1.0</td>\n",
" <td>-0.129204</td>\n",
" <td>8.587448e-06</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD6</th>\n",
" <td>9.883283e-13</td>\n",
" <td>1.0</td>\n",
" <td>0.130206</td>\n",
" <td>9.883283e-13</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC3</th>\n",
" <td>3.781102e-15</td>\n",
" <td>1.0</td>\n",
" <td>0.139832</td>\n",
" <td>3.781102e-15</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD1</th>\n",
" <td>1.100341e-05</td>\n",
" <td>1.0</td>\n",
" <td>0.082433</td>\n",
" <td>1.100341e-05</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD1</th>\n",
" <td>2.889130e-08</td>\n",
" <td>1.0</td>\n",
" <td>0.509608</td>\n",
" <td>2.889130e-08</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD2</th>\n",
" <td>1.523451e-03</td>\n",
" <td>1.0</td>\n",
" <td>0.060321</td>\n",
" <td>1.523451e-03</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC5</th>\n",
" <td>3.164878e-16</td>\n",
" <td>1.0</td>\n",
" <td>0.155371</td>\n",
" <td>3.164878e-16</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD12</th>\n",
" <td>1.703326e-37</td>\n",
" <td>1.0</td>\n",
" <td>0.334370</td>\n",
" <td>1.703326e-37</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB10</th>\n",
" <td>4.819896e-58</td>\n",
" <td>1.0</td>\n",
" <td>1.266577</td>\n",
" <td>4.819896e-58</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB5</th>\n",
" <td>2.031051e-40</td>\n",
" <td>-1.0</td>\n",
" <td>-1.015462</td>\n",
" <td>2.031051e-40</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD4</th>\n",
" <td>2.790907e-03</td>\n",
" <td>-1.0</td>\n",
" <td>-0.090527</td>\n",
" <td>2.790907e-03</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD4</th>\n",
" <td>3.220494e-09</td>\n",
" <td>1.0</td>\n",
" <td>0.118229</td>\n",
" <td>3.220494e-09</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMA7</th>\n",
" <td>1.114386e-25</td>\n",
" <td>1.0</td>\n",
" <td>0.235643</td>\n",
" <td>1.114386e-25</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB2</th>\n",
" <td>2.936813e-20</td>\n",
" <td>1.0</td>\n",
" <td>0.225433</td>\n",
" <td>2.936813e-20</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB9</th>\n",
" <td>1.075926e-75</td>\n",
" <td>1.0</td>\n",
" <td>1.145988</td>\n",
" <td>1.075926e-75</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB8</th>\n",
" <td>1.082528e-66</td>\n",
" <td>1.0</td>\n",
" <td>0.877224</td>\n",
" <td>1.082528e-66</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMA1</th>\n",
" <td>1.743304e-25</td>\n",
" <td>1.0</td>\n",
" <td>0.258769</td>\n",
" <td>1.743304e-25</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSME4</th>\n",
" <td>1.536922e-02</td>\n",
" <td>1.0</td>\n",
" <td>0.086871</td>\n",
" <td>1.536922e-02</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD14</th>\n",
" <td>1.004902e-11</td>\n",
" <td>1.0</td>\n",
" <td>0.125789</td>\n",
" <td>1.004902e-11</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMC6</th>\n",
" <td>8.132824e-13</td>\n",
" <td>1.0</td>\n",
" <td>0.118492</td>\n",
" <td>8.132824e-13</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD13</th>\n",
" <td>9.849506e-10</td>\n",
" <td>1.0</td>\n",
" <td>0.127654</td>\n",
" <td>9.849506e-10</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMD9</th>\n",
" <td>2.760906e-02</td>\n",
" <td>1.0</td>\n",
" <td>0.065384</td>\n",
" <td>2.760906e-02</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PSMB3</th>\n",
" <td>1.100460e-03</td>\n",
" <td>1.0</td>\n",
" <td>0.092246</td>\n",
" <td>1.100460e-03</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" P-Value simplified_change Delta adj_p reject_null\n",
"Name \n",
"PSMA4 1.911611e-10 1.0 0.148198 1.911611e-10 True\n",
"PSMC4 1.391607e-09 1.0 0.112355 1.391607e-09 True\n",
"PSME1 2.008155e-13 1.0 0.316319 2.008155e-13 True\n",
"PSMD5 2.182605e-11 1.0 0.217871 2.182605e-11 True\n",
"PSMD8 7.052978e-09 1.0 0.138417 7.052978e-09 True\n",
"PSMA3 1.075243e-12 1.0 0.177574 1.075243e-12 True\n",
"PSME2 8.868141e-36 1.0 0.671231 8.868141e-36 True\n",
"PSMD10 1.370257e-56 1.0 0.468575 1.370257e-56 True\n",
"PSMD7 1.926944e-05 1.0 0.080727 1.926944e-05 True\n",
"PSMF1 2.042107e-05 1.0 0.184903 2.042107e-05 True\n",
"PSMF1 1.563592e-27 1.0 0.381929 1.563592e-27 True\n",
"ADRM1 4.086117e-03 1.0 0.068020 4.086117e-03 True\n",
"PSMB7 1.087052e-08 -1.0 -0.263777 1.087052e-08 True\n",
"PSMC1 2.837536e-18 1.0 0.169574 2.837536e-18 True\n",
"PSMA6 4.235036e-24 1.0 0.277224 4.235036e-24 True\n",
"PSMD11 4.601922e-04 1.0 0.064925 4.601922e-04 True\n",
"PSMB1 1.976143e-29 1.0 0.278219 1.976143e-29 True\n",
"PSMD3 2.011238e-12 1.0 0.141735 2.011238e-12 True\n",
"PSMB6 5.243052e-27 -1.0 -0.716784 5.243052e-27 True\n",
"PSMA5 2.114399e-35 1.0 0.302883 2.114399e-35 True\n",
"PSMB4 1.921437e-21 1.0 0.255068 1.921437e-21 True\n",
"PSMC2 4.943515e-10 1.0 0.109437 4.943515e-10 True\n",
"PSME3 8.587448e-06 -1.0 -0.129204 8.587448e-06 True\n",
"PSMD6 9.883283e-13 1.0 0.130206 9.883283e-13 True\n",
"PSMC3 3.781102e-15 1.0 0.139832 3.781102e-15 True\n",
"PSMD1 1.100341e-05 1.0 0.082433 1.100341e-05 True\n",
"PSMD1 2.889130e-08 1.0 0.509608 2.889130e-08 True\n",
"PSMD2 1.523451e-03 1.0 0.060321 1.523451e-03 True\n",
"PSMC5 3.164878e-16 1.0 0.155371 3.164878e-16 True\n",
"PSMD12 1.703326e-37 1.0 0.334370 1.703326e-37 True\n",
"PSMB10 4.819896e-58 1.0 1.266577 4.819896e-58 True\n",
"PSMB5 2.031051e-40 -1.0 -1.015462 2.031051e-40 True\n",
"PSMD4 2.790907e-03 -1.0 -0.090527 2.790907e-03 True\n",
"PSMD4 3.220494e-09 1.0 0.118229 3.220494e-09 True\n",
"PSMA7 1.114386e-25 1.0 0.235643 1.114386e-25 True\n",
"PSMB2 2.936813e-20 1.0 0.225433 2.936813e-20 True\n",
"PSMB9 1.075926e-75 1.0 1.145988 1.075926e-75 True\n",
"PSMB8 1.082528e-66 1.0 0.877224 1.082528e-66 True\n",
"PSMA1 1.743304e-25 1.0 0.258769 1.743304e-25 True\n",
"PSME4 1.536922e-02 1.0 0.086871 1.536922e-02 True\n",
"PSMD14 1.004902e-11 1.0 0.125789 1.004902e-11 True\n",
"PSMC6 8.132824e-13 1.0 0.118492 8.132824e-13 True\n",
"PSMD13 9.849506e-10 1.0 0.127654 9.849506e-10 True\n",
"PSMD9 2.760906e-02 1.0 0.065384 2.760906e-02 True\n",
"PSMB3 1.100460e-03 1.0 0.092246 1.100460e-03 True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfObj = dfObj.assign(adj_p=adj_p, reject_null=reject_null)\n",
"sig_res = dfObj[dfObj[\"reject_null\"]]\n",
"sig_genes = dfObj.index.get_level_values(0).tolist()\n",
"sig_res"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4: Display data with Reactome\n",
"\n",
"Within utils from the cptac package we can use a function called \"reactome_pathway_overlay\" to query reactome and overlay the proteosome data we have found to be significant. A detailed guide on using this function can be read by calling `help(utils.reactome_pathway_overlay)`.\n",
"In this example I have chosen to overlay my data with the R-HSA-1236978 pathway ID. Determining desired pathway ID's in relation to your specific data can be done in a variety of methods such as gene enrichment analysis.\n",
"Finally, with the quality parameter set at 10 you can zoom in on areas of the pathway to see further detail."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expression_vals, image_path = utils.reactome_pathway_overlay(\n",
" 'R-HSA-1236978', \n",
" df=dfObj['simplified_change'], \n",
" analysis_token=None, \n",
" open_browser=False,\n",
" export_path=\"test.png\", \n",
" image_format='png', \n",
" display_col_idx=None, \n",
" diagram_colors='Modern', \n",
" overlay_colors='Standard', \n",
" quality=10)\n",
"Image(image_path)"
]
}
],
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