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href="https://www.thermofisher.com/blog/learning-at-the-bench/contact-us/"><span >お問い合わせ</span></a></li> </ul></nav></div></section> </div><div class="breadcrumb"><span class="breadcrumb-link-wrap"><a href="https://www.thermofisher.com/blog/?">Accelerating Science</a></span><span class="breadcrumb-link-wrap"><a class="breadcrumb-link" href="https://www.thermofisher.com/blog/learning-at-the-bench/"><span class="breadcrumb-link-text-wrap">Learning at the Bench</span></a><meta ></span> <span aria-label="breadcrumb separator">/</span> <span class="breadcrumb-link-wrap"><a class="breadcrumb-link" href="https://www.thermofisher.com/blog/learning-at-the-bench/mol-bio/"><span class="breadcrumb-link-text-wrap">分子生物学実験関連</span></a><meta ></span> <span aria-label="breadcrumb separator">/</span> <span class="breadcrumb-current-title">Pythonを使ってNGSデータを可視化してみよう！～準備編～</span></div></div></header><div class="site-inner"><div class="content-sidebar-wrap"><main class="content"><article class="post-16411 post type-post status-publish format-standard has-post-thumbnail category-mol-bio tag-ngs tag-gene-expression entry" aria-label="Pythonを使ってNGSデータを可視化してみよう！～準備編～"><header class="entry-header"><h1 class="entry-title">Pythonを使ってNGSデータを可視化してみよう！～準備編～</h1> <span class="post-info-author"> 作成者 <a href="https://www.thermofisher.com/blog/author/lgbadmin/" title="LatB Staff の投稿" rel="author">LatB Staff</a> </span> <span class="post-info-date"> 02.22.2024 </span> </header><div class="entry-content"><p>次世代シーケンス（NGS）は、ゲノム解析、トランスクリプトーム解析、メタゲノム解析など、さまざまなアプリケーションで活用されていますが、NGSで得られる膨大なデータの取り扱いは一筋縄ではいきません。<br /> そこで今回は、Python™を使って、比較的簡単にNGSデータの可視化を行う方法についてご紹介いたします。お手持ちのIon Torrent™発現解析データを使って、ぜひ試してみてください。<br /> 本ブログでは、Pythonを使ったデータの可視化について、準備編としてプログラムのインストールとデータの読み込みまでをご紹介します。<br /> ※Windows™ PCの利用を想定しています。</p> <p>▼こんな方におすすめです！<br /> ・次世代シーケンシング解析を始めたばかりの方<br /> ・コマンドラインのプログラムに興味があるけれど未経験の方<br /> ・NGSデータの可視化に興味のある方</p> <div id="toc_container" class="no_bullets"><p class="toc_title">▼もくじ</p><ul class="toc_list"><li><a href="#PythonJupyter_Notebook">PythonとJupyter Notebook のインストール</a></li><li><a href="#Jupyter_Notebook">Jupyter Notebookの起動とデータの読み込み</a></li><li><a href="#i">まとめ</a><ul><li><a href="#NGS">次世代シーケンサ（NGS）入門</a></li></ul></li></ul></div> <h2><span id="PythonJupyter_Notebook">PythonとJupyter Notebook のインストール</span></h2> <p>まず、<a href="https://www.python.org/downloads/" target="_blank" rel="noopener">Pythonの公式サイト</a>にアクセスして、プログラムをインストールします。<br /> ご自身のPCに対応したファイルをダウンロードし、ファイルをダブルクリックします。表示されるウィンドウで、<i>Add python_exe to PATH</i>にチェックし、<i>Install Now</i>をクリックしてインストールを開始します。インストールが完了したら、Windowsシステムツールのコマンドプロンプトを選択して起動し、<span style="color: #0000ff">python</span>と入力してキーボードのエンターキーを押し、Pythonが起動することを確認します。</p> <p> </p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#555555"> <td style="text-align: left" width="20"><span style="color: white">C: \Users\UserName > <span style="color: yellow"><b>python</b></span><br /> Python 3.11.5 (tags/v3.11.5:cce6ba9, Aug 24 2023, 14:38:34) [MSC v.1936 64 bit Type<br /> "help", "copyright", "credits" or "license" for more information.<br /> >>></span></td> </tr> </tbody> </table> <p>確認したら、<span style="color: #0000ff">quit()</span>または<span style="color: #0000ff">exit()</span>と入力してエンターキーを押し、pythonを終了します。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#555555"> <td style="text-align: left" width="20"><span style="color: white"><span style="color: white">C: \Users\UserName > <span style="color: yellow"><b>python</b></span><br /> Python 3.11.5 (tags/v3.11.5:cce6ba9, Aug 24 2023, 14:38:34) [MSC v.1936 64 bit Type "help", "copyright", "credits" or "license" for more information.<br /> >>> <span style="color: yellow"><b>quit()</b></span></p> <p>C:\Users\UserName ></span></td> </tr> </tbody> </table> <p>最初に、pipというツールがインストールされていることを確認します。pipは、ライブラリ（pythonで特定の処理を進めるために複数の機能をまとめたもの）のインストールや、インストール前にライブラリ間の依存関係の解消、重複インストールのチェックなどを自動で行ってくれる便利なツールです。<span style="color: #0000ff">pip -V</span>と入力してキーボードのエンターキーを押し、pipのバージョンが表示されることを確認します。</p> <p> </p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#555555"> <td style="text-align: left" width="20"><span style="color: white">C:\Users\UserName > <span style="color: yellow"><b>pip -V</b></span><br /> pip 23.2.1 from C:\Uses\UserName\AppData\Local\Programs\Python\Python311\Lib\site-packages\pip (python 3.11) </span></td> </tr> </tbody> </table> <p>次に、今回pythonの操作で使用する<a href="https://jupyter.org/" target="_blank" rel="noopener">Jupyter™ Notebook</a>をインストールします。Jupyter Notebookは、Pythonをブラウザー上で開き、プログラムを実行したり、実行した結果を表示させたりまとめて保存したりすることのできるツールです。どなたでも無償でご利用いただけます。<br /> <span style="color: #0000ff">python -m pip install jupyter</span>と入力してエンターキーを押し、インストールします。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#555555"> <td style="text-align: left" width="20"><span style="color: white">C:\Users\UserName > <span style="color: yellow"><b>python -m pip install jupyter</b></span><br /> Collecting jupyter<br /> using cached jupyter-1.0.0-py2.py3-none-any.whl (2.7 kB)<br /> Collecting notebook (from jupyter)<br /> Obtaining dependency information for notebook from<br /> https://files.pythonhosted.org/packages/29/e0/50b4873fcb99651dd21302da50ae2c49113ccf3df<br /> b901fc6aaa3117e7ed/notebook-7.0.4-py3-none-any.whl.metadata<br /> Downloading notebook-7.0.4-py3-none-any.whl.metadata (10 kB)<br /> C:\Users\UserName > </span></td> </tr> </tbody> </table> <p>プロンプト（最後が”>”で終わる行）が戻ってくればインストール完了です。</p> <h2><span id="Jupyter_Notebook">Jupyter Notebookの起動とデータの読み込み</span></h2> <p>インストールしたJupyter Notebookを起動してみましょう。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#555555"> <td style="text-align: left" width="20"><span style="color: white">C:\Users\UserName > <span style="color: yellow"><b>jupyter notebook</b></span></span></td> </tr> </tbody> </table> <p>コマンドプロンプトで<span style="color: #0000ff">jupyter notebook</span>と入力してエンターキーを押すと、ブラウザーが起動します。<br /> ブラウザー上にJupyter Notebook のDashboard の画面が表示されます。</p> <p><img decoding="async" class="aligncenter size-full wp-image-16422" src="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/8ada265d3292f9357629d57ca8c1294b.png" alt="Jupyter Notebook画面1" width="270" height="390" srcset="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/8ada265d3292f9357629d57ca8c1294b.png 270w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/8ada265d3292f9357629d57ca8c1294b-208x300.png 208w" sizes="(max-width: 270px) 100vw, 270px" /><br /> Newをクリックし、Notebookを選択し、Select KernelのウィンドウでそのままSelectをクリックすると、新規のファイルが作成されますので、ここにコマンドを入力して処理を進めます。解析データ等をインポートする場合、Uploadをクリックしてファイルを選択すると、現在作業しているディレクトリに保存できます。</p> <p><img decoding="async" class="aligncenter size-large wp-image-16424" src="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255-1024x297.png" alt="Jupyter Notebook画面2" width="760" height="220" srcset="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255-1024x297.png 1024w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255-300x87.png 300w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255-768x223.png 768w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255-1536x445.png 1536w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/992824263c7b74b537135831b6605255.png 1974w" sizes="(max-width: 760px) 100vw, 760px" /><br /> まずは今回使用するライブラリをインストールします。下記のコマンドを入力したら、画面の再生ボタンのようなアイコン(▶)をクリックして、処理を実行します。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#DDDDDD"> <td style="text-align: left" width="20"><span style="color: black"><span style="color: fuchsia">!</span>pip install scikit-learn<br /> <span style="color: fuchsia">!</span>pip install pandas<br /> <span style="color: fuchsia">!</span>pip install matplotlib<br /> <span style="color: fuchsia">!</span>pip install seaborn </span></td> </tr> </tbody> </table> <p>インストールしたライブラリをインポートして、使える状態にします。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#DDDDDD"> <td style="text-align: left" width="20"><span style="color: black"><span style="color: green">import</span> sklearn<br /> <span style="color: green">import</span> pandas <span style="color: green">as</span> pd<br /> <span style="color: green">import</span> matplotlib<br /> <span style="color: green">import</span> seaborn <span style="color: green">as</span> sns<br /> <span style="color: green">from</span> matplotlib <span style="color: green">import</span> pyplot <span style="color: green">as</span> plt<br /> <span style="color: green">from</span> sklearn.decomposition <span style="color: green">import</span> PCA<br /> <span style="color: green">from</span> sklearn <span style="color: green">import</span> preprocessing<br /> <span style="color: green">from</span> mpl_toolkits.mplot3d <span style="color: green">import</span> Axes3D</span></td> </tr> </tbody> </table> <p>準備が整ったら、さっそくデータを読み込みます。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#DDDDDD"> <td style="text-align: left" width="20"><span style="color: black">df <span style="color: fuchsia">=</span> pd.read_csv<span style="color: blue">(</span><span style="color: #cc3300">"Auto_HX_102617_S5_VnV_Chef-lib-2_25pMx70ul_blackbird-cbd_77728.log2rpm.xls"</span>, sep<span style="color: fuchsia">=</span><span style="color: #cc3300">'\t'</span><span style="color: blue">,</span> index_col<span style="color: fuchsia">=</span><span style="color: green">0</span><span style="color: blue">)</span><br /> df</span></td> </tr> </tbody> </table> <p>今回はIon AmpliSeq™ Transcriptome Mouse Gene Expression Kitを使用して取得したデータを用います。Ion Torrent™次世代シーケンサでシーケンス後、シーケンサに付属のTorrent Suite™ softwareに含まれるAmpliSeq RNAプラグインで解析を行い、各遺伝子の発現量を正規化したRPM値（Read Per Million、サンプル間比較のため総マップリードが100万配列のときのリード量に換算した値）のデータを取得します。このデータはタブ区切りのテキストファイル形式となっています。Pythonにインストールしたpandasというライブラリのread_csvというコマンドで入力形式：タブ区切りを指定し、データを読み込みます。読み込んだデータにdfという名前を付けています。<br /> <span style="color: #0000ff">df</span>とだけ入力し実行すると、データの一部を表示させることができます。</p> <p><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-16428" src="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/c096c208ccafadcc596a9f1b1dfbba0b.png" alt="Jupyter Notebook画面3" width="870" height="430" srcset="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/c096c208ccafadcc596a9f1b1dfbba0b.png 870w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/c096c208ccafadcc596a9f1b1dfbba0b-300x148.png 300w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/c096c208ccafadcc596a9f1b1dfbba0b-768x380.png 768w" sizes="(max-width: 870px) 100vw, 870px" /><br /> データを縦に見てみます。1列目はこのIon AmpliSeq Transcriptome Mouse Gene Expression Kitでアンプリコンが設計されている遺伝子の名前、2列目以降は各サンプルのRPM値です。IonCode_0109からIonCode_0116まで、8サンプル分表示されています。</p> <p>データはバーコード名で表示されているので、分かりやすいようにサンプル名に変換します。<br /> df.columnsというコマンドで、データの列名を変更します。</p> <table style="border-collapse: collapse;border-color: gray" border="2"> <tbody> <tr bgcolor="#DDDDDD"> <td style="text-align: left" width="20"><span style="color: black">df.columns <span style="color: fuchsia">=</span> <span style="color: blue">[</span><span style="color: #cc3300">"UMR_1"<span style="color: blue">,</span> "UMR_2"<span style="color: blue">,</span> "MUR_1"<span style="color: blue">,</span> "MUR_2"<span style="color: blue">,</span> "MMP_1"<span style="color: blue">,</span> "MMP_2"<span style="color: blue">,</span> "e17_1"<span style="color: blue">,</span> "e17_2"</span><span style="color: blue">]</span><br /> df</span></td> </tr> </tbody> </table> <p>先ほどと同様にdfとだけ入力し、データを表示させてサンプル名が変わっていることを確認します。</p> <p><img loading="lazy" decoding="async" class="aligncenter size-full wp-image-16430" src="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/5110bc4f4d18688019d78392057062b2.png" alt="Jupyter Notebook画面4" width="636" height="246" srcset="https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/5110bc4f4d18688019d78392057062b2.png 636w, https://www.thermofisher.com/blog/learning-at-the-bench/wp-content/uploads/sites/13/2024/02/5110bc4f4d18688019d78392057062b2-300x116.png 300w" sizes="(max-width: 636px) 100vw, 636px" /></p> <h2><span id="i">まとめ</span></h2> <ul> <li>コマンドラインを使った解析で比較的簡単にNGSデータの可視化を行えます。</li> <li>普段お使いのWindows PCで、比較的手軽にPythonを利用いただけます。</li> <li>このほかにもPythonにはさまざまなNGSデータ解析ツールが備わっており、活用することでNGSデータを理解し、その生物学的意味の考察に役立てることができます。</li> </ul> <p><a href="https://www.thermofisher.com/blog/learning-at-the-bench/ngs-gene-expression-2-csd-ts-bfx-24012/?CID=csd_ngs_r04_jp_cp0000_pjt0000_csd00000_0so_blg_ngs_gene_expression_1_csd_ts_bfx_24009_Social_LAB" target="_blank" rel="noopener">次回</a>は読み込んだデータを使ってデータの可視化を実施してみます。Ion Torrent™ 次世代シーケンスデータを可視化して理解に役立てたい、論文等でわかりやすい図を作成したい、Pythonなどのツールを活用した解析をやってみたいが具体的にどうしたらよいかわからない、といった場合には、ぜひ弊社テクニカルサポートの<a href="https://www.thermofisher.com/jp/ja/home/products-and-services/services/technical-services/bioinformatics-services/consulting.html?CID=csd_ngs_r04_jp_cp0000_pjt0000_csd00000_0so_blg_ngs_gene_expression_1_csd_ts_bfx_24009_Social_LAB" target="_blank" rel="noopener">データ解析コンサルティングサービス</a>をご検討ください。解析手法のレクチャーやお手持ちのデータを使った受託解析まで、幅広いご相談に対応いたします。</p> <h3><span id="NGS">次世代シーケンサ（NGS）入門</span></h3> <p>次世代シーケンスの原理や何ができるかがよくわからない、または自分の研究領域にどのように活用できるかわからないという方向けに、次世代シーケンスの基本や各研究領域に特化したアプリケーションをまとめました。リンク先から、それぞれの領域に応じたページをご覧いただけます。</p> <p><a class="btn btn-red" href="https://www.thermofisher.com/jp/ja/home/life-science/sequencing/next-generation-sequencing/ngs-basics.html?CID=csd_ngs_r04_jp_cp0000_pjt0000_csd00000_0so_blg_ngs_gene_expression_1_csd_ts_bfx_24009_Social_LAB" target="_blank" rel="noopener">詳細を見る</a></p> <p> </p> <p>Python is a trademark of Python Software Foundation.<br /> Windows is a trademark of Microsoft Corporation.<br /> Jupyter is a trademark of NumFOCUS, Inc.</p> 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