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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23
1.4 正規化
1.4.3
標本間と遺伝子間の正規化：
RPKM
RNA
シーケンシングデータの最も単純な正規化手法の
1
つは
RPKM
（
reads per kilobase
transcript per million reads
：転写された
mRNA 1,000
塩基対当たり、総リード数
100
万当たりに
補正されたリード数）と呼ばれるものです。
RPKM
を計算する際には、ライブラリサイズ（各列
の総和）と遺伝子長とで正規化します。
RPKM
を求める方法を理解するために、以下の値を定義しましょう。
• C
＝ある遺伝子にマッピングされたリード数
• L
＝その遺伝子のエクソンの長さ（単位は塩基対）
• N
＝実験でマッピングされた総リード数
まず、転写された
mRNA 1,000
塩基対当たりのリード数を計算しましょう。
1
塩基対当たりのリード数は以下のようになります。
Python 3
のヒント：
*
を使ってキーワード専用引数を作る
バージョン
3.0
以降の
Python
では、「キーワード専用」引数（
https://www.python.org/
dev/peps/pep-3102/
）が使えます。このタイプの引数は、必ずキーワードを用いて呼び出
さなければならず、引数の位置のみに依存できないようになっています。例えば、前述の
binned_boxplot
は以下のように呼び出すことができますが。
>>> binned_boxplot(x, y, xlabel='my x label', ylabel='my y label') ...
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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.