PythonでGO Enrichmentの結果を図示する

example.csv

term,score,size,name,cluster
GO:0090407,1.5297085894511266,41,organophosphate biosynthetic process,C1
GO:0006073,1.5163222189613395,83,cellular glucan metabolic process,C1
GO:0043085,1.44498285471817,14,positive regulation of catalytic activity,C1
GO:0010451,1.42343275529096,7,floral meristem growth,C1
GO:0009259,2.08444273888555,51,ribonucleotide metabolic process,C2
GO:0000209,2.626510646478822,685,protein polyubiquitination,C2
GO:1901970,3.197995679060633,207,positive regulation of mitotic sister chromatid separation,C2
GO:0055086,2.043050310202272,162,nucleobase-containing small molecule metabolic process,C2
GO:0043094,4.978646909720386,59,cellular metabolic compound salvage,C4
GO:0042819,1.5612111272483267,17,vitamin B6 biosynthetic process,C4
GO:0015979,8.466807502800558,130,photosynthesis,C4
GO:0097502,1.45417892854421,1,mannosylation,C5
GO:0044848,1.36510627820935,12,biological phase,C5
GO:0007163,2.6923744596164827,87,establishment or maintenance of cell polarity,C5
GO:0043414,3.215776159654917,116,macromolecule methylation,C5
GO:0000723,2.572069750528699,1393,telomere maintenance,C5
GO:0031331,3.2990295934320666,329,positive regulation of cellular catabolic process,C5
GO:0080135,2.5196148192030727,410,regulation of cellular response to stress,C6
GO:0040011,1.35544252617625,52,locomotion,C6
GO:0034502,2.739128030964704,705,protein localization to chromosome,C6
GO:0009553,3.35598374726875,991,embryo sac development,C6
GO:0052541,1.5212716561130444,10,plant-type cell wall cellulose metabolic process,C6
GO:0071215,3.6449534934267973,834,cellular response to abscisic acid stimulus,C7
GO:0009812,2.5663773876723597,159,flavonoid metabolic process,C7
GO:0002239,2.42790703640653,70,response to oomycetes,C7
GO:0045764,1.9573492416484901,263,positive regulation of cellular amino acid metabolic process,C7

# y軸にGO Termの名前を置きたいので、余白を多めにとる
plt.rcParams['figure.subplot.left'] = 0.5

df = pl.read_csv("./example.csv").sort("size")

terms = df.select("name").to_series().to_list()
sizes = df.select("size").to_series().to_list()

fig = plt.figure(figsize=(15, 10))
xgs = 10

# colorbarを手動で置く必要があるので、gridspecを使う
# colorbarは上半分くらいでいいので、y側も2分割
gs = fig.add_gridspec(2, xgs)

# colorbar部分を開けておく
ax = fig.add_subplot(gs[0:2, 0:xgs-2])

# scoresをrgbaに変換
cmap = plt.get_cmap("gnuplot")
norm = mpl.colors.Normalize(vmin=0, vmax=10)
scores = df.select("score").to_series().apply(lambda x: cmap(norm(x))).to_list()

# barplot
ax.barh(terms, sizes, color=scores)
ax.tick_params(axis="x", labelsize=15)
ax.set_xlabel("size", fontsize=20)
ax.tick_params(axis="y", labelsize=15)

# colorbar作成
cbar = fig.add_subplot(gs[0, xgs-1])
mpl.colorbar.Colorbar(
    cbar,
    mappable=mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
    orientation="vertical",
).set_label("score", fontsize=20)

plt.show()

# y軸にGO Termの名前を置きたいので、余白を多めにとる
plt.rcParams['figure.subplot.left'] = 0.5

background_gene = 10000

df = pl.read_csv("./example.csv").sort("score")

terms = df.select("name").to_series().to_list()
sizes = df.select("size").to_series().to_list()
ratio = list(map(lambda x: x / background_gene, sizes))

vmin = 0
vmax = 10

fig = plt.figure(figsize=(17.5, 10))
xgs = 10

# colorbarを手動で置く必要があるので、gridspecを使う
# colorbarは上半分くらいでいいので、y側も2分割
gs = fig.add_gridspec(2, xgs)

# colorbar部分を開けておく
ax = fig.add_subplot(gs[0:2, 0:xgs-1])

cmap = plt.get_cmap("gnuplot")
scores = df.select("score").to_series().to_list()

# barplot
scatter = ax.scatter(ratio, terms, c=scores, s=sizes, cmap=cmap, vmin=vmin, vmax=vmax)
ax.tick_params(axis="x", labelsize=15)
ax.set_xlabel("Gene Ratio", fontsize=20)
ax.tick_params(axis="y", labelsize=15)

handles, labels = scatter.legend_elements(prop="sizes", alpha=0.5)
legend = ax.legend(
    handles,
    labels,
    # 単純な位置指定だといい位置にいかないので、bbox_to_anchorでマニュアル調整
    bbox_to_anchor=(1.15, 0.5),
    title="size",
    title_fontsize=15,
    markerscale=0.4
)

cbar = fig.add_subplot(gs[0, xgs-1])

# colorbarをつける
# 位置を好き勝手したいので、ColorbarBaseを使用
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
mpl.colorbar.Colorbar(
    cbar,
    mappable=mpl.cm.ScalarMappable(norm=norm, cmap=cmap),
    orientation="vertical",
).set_label("score", fontsize=20)

plt.show()

# seabornの設定はお好みで
sns.set(style="darkgrid", palette="muted", color_codes=True)
sns.set_context("paper")

# x軸にGO Termの名前を置きたいので、余白を多めにとる
plt.rcParams['figure.subplot.bottom'] = 0.5

df = pl.read_csv("example.csv")

terms = df.select("name").to_series().to_list()
clusters = df.select("cluster").to_series().to_list()

fig = plt.figure(figsize=(15, 7.5))
ax = fig.add_subplot(111)

# 後でcolorbarを加えるために取得する
cmap = plt.get_cmap("gnuplot")
scatter = ax.scatter(
    terms,
    clusters,
    s=df.select(pl.col("size")).to_series().to_list(),
    c=df.select("score"),
    cmap=cmap,
    vmin=0,
    vmax=10,
)

# axisのラベル制御
# rotationするときは開始位置をhaで適切に指定する必要がある
# e.g., 315 -> left, 45 -> right
ax.set_xticklabels(terms, rotation=315, ha="left")
ax.tick_params(axis="y", labelsize=20)

# sizeのlegendを作成しておく
handles, labels = scatter.legend_elements(prop="sizes", alpha=0.5)
legend = ax.legend(
    handles,
    labels,
    # 単純な位置指定だといい位置にいかないので、bbox_to_anchorでマニュアル調整
    bbox_to_anchor=(1.175, 1),
    title="size",
    title_fontsize=15,
    markerscale=0.4
)

# colorbarをつける
fig.colorbar(scatter, ax=ax, pad=0.01).set_label("score", size=15)

fig.tight_layout()

plt.show()