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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42
2 章 NumPy と SciPy を用いた分位数正規化
counts_log = np.log(counts + 1)
counts_var = most_variable_rows(counts_log, n=1500)
yr, yc = bicluster(counts_var, linkage_method='ward',
distance_metric='euclidean')
with plt.style.context('style/thinner.mplstyle'):
plot_bicluster(counts_var, yr, yc)
2.5
生存率予測
上の図から、標本データが少なくとも
2
つ、もしかすると
3
つのグループに自然に分かれるこ
とが見てとれます。果たしてこのグループ分けには意味があるでしょうか。この問いには、論文の
データリポジトリ（
http://bit.ly/2tiZtR6
）にある患者データにアクセスして答えましょう。この
データに若干の前処理を施すと、各患者の生存率情報を含む患者の表（
http://bit.ly/2tjp6BD
）が
得られます。続いて、この生存率情報とリード数のグループのマッチングを行うと、患者の遺伝子
発現から病理の差が予測できるかどうかがわかります。
patients = pd.read_csv('data/patients.csv', index_col=0)
patients.head()
標本
図
2-3
このヒートマップは、全標本中の全遺伝子の遺伝子発現量を表す。色は発現量を示す。行と列はクラス ...
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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.