drbonodojo3-6 データ統合解析
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「生命科学者のためのDr.Bonoデータ解析実践道場」読書会 @大阪 https://oum-python.connpass.com/event/149085/ ノートブックはこちら https://bit.ly/2MT52wN
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With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
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We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
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drbonodojo3-6 データ統合解析
- 1. 3.6 データ統合解析 ⼤阪⼤学免疫学フロンティア研究センター 実験免疫学 安⽔ 良明 #drbonodojo 読書会 2019/10/20
- 2. 2019/10/20 #drbonodojo 読書会 • 某所で初期研修やってます • iFrec 実験免疫学教室（坂⼝研） TregのエピジェネティクスやscRNAseqの解析 • 寂しくてPython会はじめました @yyoshiaki @oumed_python ⾃⼰紹介
- 5. いざ実践︕ 2019/10/20 #drbonodojo 読書会 Google Colabで実践してみよう。安⽔流は普段Python を使う。(Perlとシェルが苦⼿なだけ。。。) URL https://bit.ly/2MT52wN
- 6. 遺伝⼦発現のリファレンスデータセット 2019/10/20 #drbonodojo 読書会 近年のデータベースの発達は顕著で、データ量 や質、可視化の利便性全て良くなってきている。 うまく巨⼈の⽅の上に乗るのが重要。 CAGEにより400程の異な る細胞腫の発現情報を構 築した国際プロジェクト。 （Enhancerの情報をよく 使っている。） p57 GTEx CAGE, GeneChip, EST, RNA-seqなど各種発現 データを幅広く集積。 臓器ごと、細胞腫ごと に可視化。 948⼈のドナーから17382 サンプルのRNA-seqを⾏い、 臓器ごとの発現を構築。 eQTLやsplicingなども。 最近v8がリリース。 Tabula Muris 書籍には載っていないが好き。 マウスのsingle cell RNAseq データベース。20の組織から 100,000細胞を取得。 レビュアーから連続で攻撃 されて嫌いになりかけ。。。
- 7. Tabula Muris 2019/10/20 #drbonodojo 読書会 気になる遺伝⼦が出てきたらこれとGeneCardsをまず ⾒ている。
- 9. 正規表現 tips 2019/10/20 #drbonodojo 読書会 正規表現は便利な半⾯初⼼者には⾮常にわかりにくい。 可視化ツールがあるので積極的に利⽤するとよい。 https://regexper.com/
- 10. TIBCO Spotfire 2019/10/20 #drbonodojo 読書会 データの整形はしばしば思いがけないミスを⽣む。特 にデータが重複していたり⽋損している際は慎重に データを確認しながら処理をするべき。Spotfireもその 選択肢の⼀つ。
- 12. ゲノム上の座標による連結 2019/10/20 #drbonodojo 読書会 領域データの連結もしばしば直⾯する課題である。 Genomeは⼤きく⽬grepの範疇を遥かに超えている。 例としてGGGenomeを⽤いて取得されたE-box領域と HIF1AのChIP-seqの重なりを求める。 Bed file IGVによる可視化
- 13. GGGenome 2019/10/20 #drbonodojo 読書会 ⾮常に使い勝⼿のよい配列検索システム。ミスマッチ の指定まで出来る上に、ダウンロードもcsv, bedなど 様々選べる。Pandasでそのままread_csvすることも出 来て、重宝している。 @meso_cacase
- 14. bedtoolsで処理してみる。 2019/10/20 #drbonodojo 読書会 Bed fileやbam file処理の王道はbedtools。これで出来な い領域処理はほぼない。詳しくは公式ページにわかり やす図とともにマニュアルが有る。pybedtoolsなんか も。