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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34
2 章 NumPy と SciPy を用いた分位数正規化
A2BP1 0.00 0.00 0.00
A2LD1 164.38 552.43 201.83
A2ML1 27.00 0.00 0.00
02c76d24-f1d2-4029-95b4-8be3bda8fdbe TCGA-EB-A51B
A1BG 400.11 420.46
A1CF 1.00 0.00
A2BP1 0.00 1.00
A2LD1 165.12 95.75
A2ML1 0.00 8.00
data_table
の行と列を眺めると、列が標本で、行が遺伝子だとわかります。では、リード数を
NumPy
配列に格納しましょう。
# 2D ndarray containing expression counts for each gene in each individual
各標本の各遺伝子の発現数を格納した
2
次元の
ndarray
counts = data_table.values
2.2
遺伝子発現分布の標本差
では、リード数データの特徴をおおまかに把握するために、標本ごとのリード数の分布をプロッ
トしてみましょう。ガウシアンカーネルを使ってデータの凹凸を平滑化して、形の全体像を捉えま
す。
まずは、いつも通り、プロットのスタイルを設定します。
# Make plots appear inline, set custom plotting style
プロットをインライン表示させる。
%matplotlib inline
また、独自のプロットスタイルを設定する。 ...
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