From be4380f3ead04ff462866ab7e8dc9db08c2b308b Mon Sep 17 00:00:00 2001
From: Meiko Remiorz <meiko.remiorz@web.de>
Date: Mon, 18 Dec 2023 15:45:05 +0100
Subject: [PATCH] Create project-cancer-classification.ipynb

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 project-cancer-classification.ipynb | 4597 +++++++++++++++++++++++++++
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 create mode 100644 project-cancer-classification.ipynb

diff --git a/project-cancer-classification.ipynb b/project-cancer-classification.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8bc02404-8cd1-46d9-8237-2d035ebb3e79",
+   "metadata": {},
+   "source": [
+    "# **[Project] Cancer Subtype Classification**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0c5076f4",
+   "metadata": {},
+   "source": [
+    "# Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a599748",
+   "metadata": {},
+   "source": [
+    "The [TCGA Kidney Cancers Dataset](https://archive.ics.uci.edu/dataset/892/tcga+kidney+cancers) is a bulk RNA-seq dataset that contains transcriptome profiles (i.e., gene expression quantification data) of patients diagnosed with three different subtypes of kidney cancers.\n",
+    "This dataset can be used to make predictions about the specific subtype of kidney cancers given the normalized transcriptome profile data.\n",
+    "\n",
+    "The normalized transcriptome profile data is given as **TPM** and **FPKM** for each gene.\n",
+    "\n",
+    "> TPM (Transcripts Per Million) and FPKM (Fragments Per Kilobase Million) are two common methods for quantifying gene expression in RNA sequencing data.\n",
+    "> They both aim to account for the differences in sequencing depth and transcript length when estimating gene expression levels.\n",
+    ">\n",
+    "> **TPM** (Transcripts Per Million):\n",
+    "> - TPM is a measure of gene expression that normalizes for both library size (sequencing depth) and transcript length.\n",
+    "> - The main idea behind TPM is to express the abundance of a transcript relative to the total number of transcripts in a sample, scaled to one million.\n",
+    ">\n",
+    "> **FPKM** (Fragments Per Kilobase Million):\n",
+    "> - FPKM is another method for quantifying gene expression, which is commonly used in older RNA-seq analysis pipelines. It's similar in concept to TPM but differs in the way it's calculated.\n",
+    "> - FPKM also normalizes for library size and transcript length, but it measures gene expression as the number of fragments (i.e., reads) per kilobase of exon model per million reads.\n",
+    ">\n",
+    "> TPM is generally considered more robust to variations in library size, making it a preferred choice in many modern RNA-seq analysis workflows.\n",
+    "\n",
+    "We provide one dataset for each kidney cancer subtype:\n",
+    "\n",
+    "- [TCGA-KICH](https://portal.gdc.cancer.gov/projects/TCGA-KICH): kidney chromophobe (renal clear cell carcinoma)\n",
+    "- [TCGA-KIRC](https://portal.gdc.cancer.gov/projects/TCGA-KIRC): kidney renal clear cell carcinoma\n",
+    "- [TCGA-KIRP](https://portal.gdc.cancer.gov/projects/TCGA-KIRP): kidney renal papillary cell carcinoma\n",
+    "\n",
+    "> This and _much_ more data is openly available on the [NCI Genomic Data Commons (GDC) Data Portal](https://portal.gdc.cancer.gov/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16712787",
+   "metadata": {},
+   "source": [
+    "# Data access"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6421ef6c",
+   "metadata": {},
+   "source": [
+    "There are two ways to access the data: via the TNT homepage or the GDC Data Portal."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b977e8b8",
+   "metadata": {},
+   "source": [
+    "## Download from the TNT homepage (_recommended_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "800fa7bd",
+   "metadata": {},
+   "source": [
+    "The download from the TNT homepage is straightforward:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dda97b16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2023-12-14 13:25:57--  http://www.tnt.uni-hannover.de/edu/vorlesungen/AMLG/data/project-cancer-classification.tar.gz\n",
+      "Resolving www.tnt.uni-hannover.de (www.tnt.uni-hannover.de)... 130.75.31.71\n",
+      "Connecting to www.tnt.uni-hannover.de (www.tnt.uni-hannover.de)|130.75.31.71|:80... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 1113432357 (1.0G) [application/x-gzip]\n",
+      "Saving to: ‘project-cancer-classification.tar.gz’\n",
+      "\n",
+      "project-cancer-clas 100%[===================>]   1.04G  62.8MB/s    in 20s     \n",
+      "\n",
+      "2023-12-14 13:26:19 (52.5 MB/s) - ‘project-cancer-classification.tar.gz’ saved [1113432357/1113432357]\n",
+      "\n",
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+      "project-cancer-classification/tcga-kirp-geq/c43bacec-dabb-4478-9303-57dcee5e38fa/7b54d220-229c-421f-8d6d-891a27354951.rna_seq.augmented_star_gene_counts.tsv\n",
+      "project-cancer-classification/tcga-kirp-geq/c43bacec-dabb-4478-9303-57dcee5e38fa/logs/\n",
+      "project-cancer-classification/tcga-kirp-geq/c43bacec-dabb-4478-9303-57dcee5e38fa/logs/7b54d220-229c-421f-8d6d-891a27354951.rna_seq.augmented_star_gene_counts.tsv.parcel\n",
+      "project-cancer-classification/tcga-kirp-geq/347040ad-b9d1-4234-b4f1-54e319494a3f/\n",
+      "project-cancer-classification/tcga-kirp-geq/347040ad-b9d1-4234-b4f1-54e319494a3f/e8903645-a516-4e44-b802-128eeffce68f.rna_seq.augmented_star_gene_counts.tsv\n",
+      "project-cancer-classification/tcga-kirp-geq/347040ad-b9d1-4234-b4f1-54e319494a3f/annotations.txt\n",
+      "project-cancer-classification/tcga-kirp-geq/347040ad-b9d1-4234-b4f1-54e319494a3f/logs/\n",
+      "project-cancer-classification/tcga-kirp-geq/347040ad-b9d1-4234-b4f1-54e319494a3f/logs/e8903645-a516-4e44-b802-128eeffce68f.rna_seq.augmented_star_gene_counts.tsv.parcel\n",
+      "project-cancer-classification/tcga-kirp-geq/bd59015a-ece4-45c2-8728-ccc767f2a80d/\n",
+      "project-cancer-classification/tcga-kirp-geq/bd59015a-ece4-45c2-8728-ccc767f2a80d/64d0e31c-fd09-4b6e-8003-bb89a86a77b5.rna_seq.augmented_star_gene_counts.tsv\n",
+      "project-cancer-classification/tcga-kirp-geq/bd59015a-ece4-45c2-8728-ccc767f2a80d/logs/\n",
+      "project-cancer-classification/tcga-kirp-geq/bd59015a-ece4-45c2-8728-ccc767f2a80d/logs/64d0e31c-fd09-4b6e-8003-bb89a86a77b5.rna_seq.augmented_star_gene_counts.tsv.parcel\n",
+      "mv: cannot move 'project-cancer-classification/' to 'data/project-cancer-classification': File exists\n",
+      "removed 'project-cancer-classification.tar.gz'\n"
+     ]
+    }
+   ],
+   "source": [
+    "! wget http://www.tnt.uni-hannover.de/edu/vorlesungen/AMLG/data/project-cancer-classification.tar.gz\n",
+    "! tar -xzvf project-cancer-classification.tar.gz\n",
+    "! mv -v project-cancer-classification/ data/\n",
+    "! rm -v project-cancer-classification.tar.gz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc2db880",
+   "metadata": {},
+   "source": [
+    "In the `data/` folder you will now find many files in the [TSV format](https://en.wikipedia.org/wiki/Tab-separated_values) ([CSV](https://en.wikipedia.org/wiki/Comma-separated_values)-like with tabs as delimiter) containing the normalized transcriptome profile data.\n",
+    "\n",
+    "To start, you can read a TSV file into a [pandas](https://pandas.pydata.org) [`DataFrame`](pandas dataframe to dict) using the [`pandas.read_csv()`](https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html#pandas-read-csv) function with the `sep` parameter set to `\\t`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "2adae4ff",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 1 and the array at index 1 has size 60660",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[60], line 24\u001b[0m\n\u001b[1;32m     22\u001b[0m                 df \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;241m4\u001b[39m:]\n\u001b[1;32m     23\u001b[0m                 df \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(df)\n\u001b[0;32m---> 24\u001b[0m                 df \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvstack\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[43ml\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m                 rick\u001b[38;5;241m.\u001b[39mappend(df)\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m#tsv_file_path = \"data/tcga-kich-geq/0ba21ef5-0829-422e-a674-d3817498c333/4868e8fc-e045-475a-a81d-ef43eabb7066.rna_seq.augmented_star_gene_counts.tsv\"\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# Read the TSV file into a DataFrame\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[38;5;66;03m# Display the first few rows of the DataFrame\u001b[39;00m\n\u001b[1;32m     34\u001b[0m \u001b[38;5;66;03m#print(df.head(n=20))\u001b[39;00m\n",
+      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mvstack\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/numpy/core/shape_base.py:282\u001b[0m, in \u001b[0;36mvstack\u001b[0;34m(tup)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arrs, \u001b[38;5;28mlist\u001b[39m):\n\u001b[1;32m    281\u001b[0m     arrs \u001b[38;5;241m=\u001b[39m [arrs]\n\u001b[0;32m--> 282\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_nx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43marrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 1 and the array at index 1 has size 60660"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import pickle\n",
+    "\n",
+    "\n",
+    "import os\n",
+    "#'./data/tcga-kirp-geq'\n",
+    "\n",
+    "labels = [\"kirp\", \"kirc\", \"kich\"]   # Setzen Sie hier Ihren Ordnerpfad ein\n",
+    "\n",
+    "rick = list()\n",
+    "for l in labels:\n",
+    "    root_folder = f\"./data/tcga-{l}-geq\"\n",
+    "    for root, dirs, files in os.walk(root_folder):\n",
+    "        for file in files:\n",
+    "            if file.endswith('.tsv'):\n",
+    "                # Vollständiger Pfad zur Datei\n",
+    "                file_path = os.path.join(root, file)\n",
+    "                # Hier können Sie etwas mit der Datei machen, z.B. einlesen\n",
+    "                df = pd.read_csv(filepath_or_buffer=file_path, sep=\"\\t\", header=1)\n",
+    "                df = df['tpm_unstranded']\n",
+    "\n",
+    "                df = df[4:]\n",
+    "                df = np.array(df)\n",
+    "                rick.append(df)\n",
+    "\n",
+    "\n",
+    "#tsv_file_path = \"data/tcga-kich-geq/0ba21ef5-0829-422e-a674-d3817498c333/4868e8fc-e045-475a-a81d-ef43eabb7066.rna_seq.augmented_star_gene_counts.tsv\"\n",
+    "\n",
+    "# Read the TSV file into a DataFrame\n",
+    "#df = pd.read_csv(filepath_or_buffer=tsv_file_path, sep=\"\\t\", header=1)\n",
+    "\n",
+    "# Display the first few rows of the DataFrame\n",
+    "#print(df.head(n=20))\n",
+    "rick = np.array(rick)\n",
+    "print(rick)\n",
+    "\n",
+    "# Speichern der 'kirp' Liste in einer Pickle-Datei\n",
+    "with open('rick.pickle', 'wb') as f:\n",
+    "    pickle.dump(rick, f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e339354-a7cc-4e8a-9323-4be41ef62117",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Laden der 'kirp' Liste aus der Pickle-Datei\n",
+    "with open('rick.pickle', 'rb') as f:\n",
+    "    rick = pickle.load(f)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "088db0b3-8c33-41ff-a543-1b1e50c5e589",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[2.03310e+01 0.00000e+00 2.51806e+01 ... 0.00000e+00 0.00000e+00\n",
+      "  1.43200e-01]\n",
+      " [3.70405e+01 5.00200e-01 7.74246e+01 ... 0.00000e+00 1.92000e-02\n",
+      "  9.15500e-01]\n",
+      " [4.54456e+01 9.03000e-02 7.49545e+01 ... 0.00000e+00 4.85000e-02\n",
+      "  7.54000e-01]\n",
+      " ...\n",
+      " [4.00416e+01 4.67600e-01 5.27965e+01 ... 0.00000e+00 5.98000e-02\n",
+      "  1.71170e+00]\n",
+      " [3.78835e+01 1.42560e+00 6.00608e+01 ... 0.00000e+00 1.56000e-02\n",
+      "  1.25250e+00]\n",
+      " [4.08749e+01 0.00000e+00 6.17930e+01 ... 0.00000e+00 1.41000e-02\n",
+      "  1.21190e+00]]\n",
+      "Transformierte Daten: [[-1.19442877e+02  6.73161120e+01  7.25198401e+00 ...  4.14152129e+00\n",
+      "   4.59599544e+00  5.09706886e+00]\n",
+      " [-1.20146010e+01  1.00991944e+00 -1.46544493e+01 ...  5.30832262e-01\n",
+      "  -8.17291005e+00 -4.58423143e+00]\n",
+      " [ 5.36208095e+01 -4.94641674e+01 -6.31142039e+00 ... -9.77985446e+00\n",
+      "   5.76202141e-02 -6.98175867e+00]\n",
+      " ...\n",
+      " [ 7.68526642e+01 -1.67290906e+01 -3.56488589e+01 ...  3.92227734e+00\n",
+      "   1.61191707e+00 -2.11556078e+00]\n",
+      " [ 2.08641623e+01 -6.02519312e+00 -2.06334035e+01 ... -3.22483459e+01\n",
+      "   1.86685318e+00 -1.04483450e+01]\n",
+      " [-2.04825838e+00  1.46171081e+01 -5.35913106e+01 ...  7.38879468e+00\n",
+      "   6.66868029e+00  1.62500844e+01]]\n",
+      "Varianz erklärt durch jede Komponente: [0.11673362 0.07417538 0.0455907  0.03897669 0.02736743 0.02460985\n",
+      " 0.02193478 0.0186576  0.01633068 0.01507051 0.01420144 0.01397054\n",
+      " 0.01153674 0.01080128 0.0104887  0.0098909  0.00921873 0.00886091\n",
+      " 0.00855257 0.00783197 0.0075601  0.00736654 0.00693048 0.00682542\n",
+      " 0.00660169 0.00645142 0.00589864 0.00582848 0.00576389 0.00566733\n",
+      " 0.00553859 0.00534919 0.00504107 0.00484114 0.00472543 0.00460901\n",
+      " 0.00458414 0.00450365 0.00440221 0.00436067 0.00425147 0.00417729\n",
+      " 0.0040887  0.00399486 0.00385279 0.00383203 0.0037602  0.00367175\n",
+      " 0.00356195 0.00354271 0.00353029 0.00344903 0.0033579  0.00328587\n",
+      " 0.00325445 0.00323571 0.00316966 0.00315486 0.00312934 0.00302847\n",
+      " 0.00301499 0.00295996 0.00292212 0.00288084 0.00284208 0.00279687\n",
+      " 0.00278814 0.00273434 0.00271495 0.00268345 0.00262162 0.00259129\n",
+      " 0.00258267 0.00256063 0.00254636 0.00251511 0.00247141 0.00243775\n",
+      " 0.00241073 0.00240933 0.00237048 0.0023592  0.00232816 0.00231311\n",
+      " 0.00229201 0.00227123 0.0022473  0.00224123 0.00221002 0.00218027\n",
+      " 0.00216646 0.00216269 0.00213728 0.00208136 0.00207751 0.00205599\n",
+      " 0.00200776 0.00198403 0.00196768 0.0019477  0.00191694 0.00191203\n",
+      " 0.00188918 0.00187741 0.00186313 0.00184691 0.00182451 0.00180766\n",
+      " 0.00176943 0.00175167 0.00174777 0.00173873 0.00171443 0.00170616\n",
+      " 0.00169949 0.0016919  0.00165879 0.00164319 0.00163145 0.00162293\n",
+      " 0.00160792 0.00159409 0.00157819 0.00155699 0.00155253 0.0015174\n",
+      " 0.0015148  0.00150032 0.00149309 0.00147394 0.00145496 0.0014512\n",
+      " 0.00144755 0.00142722 0.00141536 0.00140113 0.0013997  0.00139108\n",
+      " 0.00136978 0.00135641 0.00134515 0.0013318  0.00131654 0.00130795\n",
+      " 0.00129645 0.00127695 0.00126905 0.00125501 0.00124351 0.00123629]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "# Angenommen, X ist Ihr Datensatz\n",
+    "# X = ...\n",
+    "X = kirp\n",
+    "\n",
+    "print(X)\n",
+    "\n",
+    "# Standardisieren der Daten\n",
+    "scaler = StandardScaler()\n",
+    "X_scaled = scaler.fit_transform(X)\n",
+    "\n",
+    "# Erstellen des PCA-Objekts\n",
+    "pca = PCA(n_components=150)  # Angenommen, Sie möchten 2 Hauptkomponenten behalten\n",
+    "\n",
+    "# Durchführen der PCA\n",
+    "X_pca = pca.fit_transform(X_scaled)\n",
+    "\n",
+    "# Die resultierenden Hauptkomponenten\n",
+    "print(\"Transformierte Daten:\", X_pca)\n",
+    "\n",
+    "# Variance Ratio für jede Komponente\n",
+    "print(\"Varianz erklärt durch jede Komponente:\", pca.explained_variance_ratio_)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b11bbe20-0494-4e7a-83ff-3cb0bfa82f3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}