| Description | GeneRatio | BgRatio | pvalue | p.adjust | qvalue | geneID | Count | group | |
| ID | |||||||||
| GO:0002455 | humoral immune response mediated by circulatin... | 22/178 | 150/18670 | 19.365993 | 16.222197 | 16.298589 | HLA-DQB1/CD55/IGHM/PTPRC/TRBC2/IGHG2/IGKV3-20/... | 22 | B6_up |
| GO:0006958 | complement activation, classical pathway | 20/178 | 137/18670 | 17.588789 | 14.989062 | 15.065454 | CD55/IGHM/TRBC2/IGHG2/IGKV3-20/IGHV4-34/IGHV3-... | 20 | B6_up |
| GO:0006956 | complement activation | 20/178 | 175/18670 | 15.453684 | 13.008859 | 13.085251 | CD55/IGHM/TRBC2/IGHG2/IGKV3-20/IGHV4-34/IGHV3-... | 20 | B6_up |
| GO:0038096 | Fc-gamma receptor signaling pathway involved i... | 18/178 | 139/18670 | 14.916693 | 12.675988 | 12.752379 | PTPRC/LYN/IGHG2/IGKV3-20/IGHV4-34/IGHV3-30/IGL... | 18 | B6_up |
| GO:0002673 | regulation of acute inflammatory response | 18/178 | 159/18670 | 13.871614 | 11.817674 | 11.894066 | HLA-E/CD55/IGHG2/IGKV3-20/IGHV4-34/IGHV3-30/IG... | 18 | B6_up |
首先我們可以借助 DotPlot的類方法parse_from_tidy_data 對數據進行封裝，然后直接調用plot函數進行繪圖。當然，你也可以通過DotPlot的構造函數__init__()來實例化DotPlot對象。
new_keys = {'item_key': 'Description','group_key': 'group','sizes_key': 'Count'}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **new_keys)
sct = dp.plot(size_factor=10, cmap='Reds') # 通過size_factor 調節圖中點的大小
dp = dotplot.DotPlot.parse_from_tidy_data(data, item_key='Description', group_key='group', sizes_key='Count') # 該效果完全同上，這是python語言特性 sct = dp.plot(size_factor=10, cmap='Reds')
我們可以通過color_key指定data中的列做顏色映射。
new_keys = {'item_key': 'Description','group_key': 'group','sizes_key': 'Count','color_key': 'pvalue'}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **new_keys)
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True)
可以通過circle_key增加一列作為虛線圓圈的映射。
DEFAULT_CLUSTERPROFILE_KEYS = {
'item_key': 'Description', 'group_key': 'group',
'sizes_key': 'Count', 'color_key': 'pvalue',
'circle_key': 'qvalue'
}
dp = dotplot.DotPlot.parse_from_tidy_data(data, **DEFAULT_CLUSTERPROFILE_KEYS)
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True)
當然，更多的參數我們可以通過signature來查看，我對這些參數都做了類型注釋，應該是通俗易懂的：
?dp.plot
Signature:
dp.plot(
size_factor:float=15,
vmin:float=0,
vmax:float=None,
path:Union[os.PathLike, NoneType]=None,
cmap:Union[str, matplotlib.colors.Colormap]='Reds',
cluster_row:bool=False,
cluster_col:bool=False,
cluster_kws:Union[Dict, NoneType]=None,
**kwargs,
)
Docstring:
:param size_factor: `size factor` * `value` for the actually representation of scatter size in the final figure
:param vmin: `vmin` in `matplotlib.pyplot.scatter`
:param vmax: `vmax` in `matplotlib.pyplot.scatter`
:param path: path to save the figure
:param cmap: color map supported by matplotlib
:param kwargs: dot_title, circle_title, colorbar_title, dot_color, circle_color
other kwargs are passed to `matplotlib.Axes.scatter`
:param cluster_row, whether to cluster the row
:param cluster_col, whether to cluster the col
:param cluster_kws, key args for cluster, including `cluster_method`, `cluster_metric`， 'cluster_n'
:return:
因此，我們可以通過關鍵字參數修改圖例中的部分組件：
sct = dp.plot(size_factor=10, cmap='Reds', cluster_row=True, dot_title = 'Count', circle_title='-log10(qvalue)', colorbar_title = '-log10(pvalue)')
dotplot在數據可視化中是一個強有力的展示方式，選擇一個合適的可視化方式勝過千言萬語
最后，最適合的可視化方式是最直觀、最簡潔的，不是炫技，別被花里胡哨的可視化所迷住雙眼而忽略了信息的傳達。
到此這篇關于教你怎么用python繪制dotplot的文章就介紹到這了,更多相關python繪制dotplot內容請搜索腳本之家以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持腳本之家！
