November 2018
Intermediate to advanced
272 pages
6h 51m
Japanese
book
エレガントなSciPy ―Pythonによる科学技術計算
by Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 山崎 邦子, 山崎 康宏
Content preview from エレガントなSciPy ―Pythonによる科学技術計算
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14
1 章 エレガントな NumPy：科学 Python の基礎
め、標本を比較する際には、何らかのデータ変換を行ってすべての測定値を共通のスケールに揃え
る必要があります。
本章の例題の場合、実験で特定される差異が純粋に生物学的差異に対応するように、実験手法に
よるアーティファクトを除く必要があります。そこで、遺伝子発現データにセットで適用されるこ
との多い、「標本（列）間」と「遺伝子（行）間」の
2
段階の正規化を考えてみます。
1.4.1
標本間の正規化
例えば、
RNA
シーケンシング実験では、リード数が標本間で大きくばらつくことがあります。
ここでは、全遺伝子の発現リード数の分布をプロットしてみましょう。まず、列の総和を取って各
標本の全遺伝子の発現の総数を調べ、標本間の差を見ます。総リード数の分布を可視化するため、
カーネル密度推定（
KDE
）を使います。この手法は一般に、度数分布図を平滑化して、元にある
分布をより明確にするために使われます。
正規化を始める前に、描画の設定を行う必要があります（設定は章ごとに行います）。以下の
コードの各行の詳細については、描画に関するひとことメモをご覧ください。
# Make all plots appear inline in the Jupyter notebook from now onwards
%matplotlib inline
以後すべてのプロットを
Jupyter
ノートブック内でインライン表示させる。
# Use our own style file ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.