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| #!/usr/bin/env python | |
| # coding: utf-8 | |
| # # Different Expression Analysis | |
| # | |
| # An important task of bulk rna-seq analysis is the different expression , which we can perform with omicverse. For different expression analysis, ov change the `gene_id` to `gene_name` of matrix first. When our dataset existed the batch effect, we can use the SizeFactors of DEseq2 to normalize it, and use `t-test` of `wilcoxon` to calculate the p-value of genes. Here we demonstrate this pipeline with a matrix from `featureCounts`. The same pipeline would generally be used to analyze any collection of RNA-seq tasks. | |
| # | |
| # Colab_Reproducibility：https://colab.research.google.com/drive/1q5lDfJepbtvNtc1TKz-h4wGUifTZ3i0_?usp=sharing | |
| # In[1]: | |
| import omicverse as ov | |
| import scanpy as sc | |
| import matplotlib.pyplot as plt | |
| ov.plot_set() | |
| # ## Geneset Download | |
| # | |
| # When we need to convert a gene id, we need to prepare a mapping pair file. Here we have pre-processed 6 genome gtf files and generated mapping pairs including `T2T-CHM13`, `GRCh38`, `GRCh37`, `GRCm39`, `danRer7`, and `danRer11`. If you need to convert other id_mapping, you can generate your own mapping using gtf Place the files in the `genesets` directory. | |
| # In[2]: | |
| ov.utils.download_geneid_annotation_pair() | |
| # Note that this dataset has not been processed in any way and is only exported by `featureCounts`, and Sequence alignment was performed from the genome file of CRCm39 | |
| # | |
| # sample data can be download from: https://raw.githubusercontent.com/Starlitnightly/omicverse/master/sample/counts.txt | |
| # In[3]: | |
| #data=pd.read_csv('https://raw.githubusercontent.com/Starlitnightly/omicverse/master/sample/counts.txt',index_col=0,sep='\t',header=1) | |
| data=ov.read('data/counts.txt',index_col=0,header=1) | |
| #replace the columns `.bam` to `` | |
| data.columns=[i.split('/')[-1].replace('.bam','') for i in data.columns] | |
| data.head() | |
| # ## ID mapping | |
| # | |
| # We performed the gene_id mapping by the mapping pair file `GRCm39` downloaded before. | |
| # In[4]: | |
| data=ov.bulk.Matrix_ID_mapping(data,'genesets/pair_GRCm39.tsv') | |
| data.head() | |
| # ## Different expression analysis with ov | |
| # | |
| # We can do differential expression analysis very simply by ov, simply by providing an expression matrix. To run DEG, we simply need to: | |
| # | |
| # - Read the raw count by featureCount or any other qualify methods. | |
| # - Create an ov DEseq object. | |
| # In[5]: | |
| dds=ov.bulk.pyDEG(data) | |
| # We notes that the gene_name mapping before exist some duplicates, we will process the duplicate indexes to retain only the highest expressed genes | |
| # In[6]: | |
| dds.drop_duplicates_index() | |
| print('... drop_duplicates_index success') | |
| # We also need to remove the batch effect of the expression matrix, `estimateSizeFactors` of DEseq2 to be used to normalize our matrix | |
| # In[7]: | |
| dds.normalize() | |
| print('... estimateSizeFactors and normalize success') | |
| # Now we can calculate the different expression gene from matrix, we need to input the treatment and control groups | |
| # In[8]: | |
| treatment_groups=['4-3','4-4'] | |
| control_groups=['1--1','1--2'] | |
| result=dds.deg_analysis(treatment_groups,control_groups,method='ttest') | |
| result.head() | |
| # One important thing is that we do not filter out low expression genes when processing DEGs, and in future versions I will consider building in the corresponding processing. | |
| # In[9]: | |
| print(result.shape) | |
| result=result.loc[result['log2(BaseMean)']>1] | |
| print(result.shape) | |
| # We also need to set the threshold of Foldchange, we prepare a method named `foldchange_set` to finish. This function automatically calculates the appropriate threshold based on the log2FC distribution, but you can also enter it manually. | |
| # In[10]: | |
| # -1 means automatically calculates | |
| dds.foldchange_set(fc_threshold=-1, | |
| pval_threshold=0.05, | |
| logp_max=6) | |
| # ## Visualize the DEG result and specific genes | |
| # | |
| # To visualize the DEG result, we use `plot_volcano` to do it. This fuction can visualize the gene interested or high different expression genes. There are some parameters you need to input: | |
| # | |
| # - title: The title of volcano | |
| # - figsize: The size of figure | |
| # - plot_genes: The genes you interested | |
| # - plot_genes_num: If you don't have interested genes, you can auto plot it. | |
| # In[11]: | |
| dds.plot_volcano(title='DEG Analysis',figsize=(4,4), | |
| plot_genes_num=8,plot_genes_fontsize=12,) | |
| # To visualize the specific genes, we only need to use the `dds.plot_boxplot` function to finish it. | |
| # In[12]: | |
| dds.plot_boxplot(genes=['Ckap2','Lef1'],treatment_groups=treatment_groups, | |
| control_groups=control_groups,figsize=(2,3),fontsize=12, | |
| legend_bbox=(2,0.55)) | |
| # In[13]: | |
| dds.plot_boxplot(genes=['Ckap2'],treatment_groups=treatment_groups, | |
| control_groups=control_groups,figsize=(2,3),fontsize=12, | |
| legend_bbox=(2,0.55)) | |
| # ## Pathway enrichment analysis by ov | |
| # | |
| # Here we use the `gseapy` package, which included the GSEA analysis and Enrichment. We have optimised the output of the package and given some better looking graph drawing functions | |
| # | |
| # Similarly, we need to download the pathway/genesets first. Five genesets we prepare previously, you can use `ov.utils.download_pathway_database()` to download automatically. Besides, you can download the pathway you interested from enrichr: https://maayanlab.cloud/Enrichr/#libraries | |
| # In[37]: | |
| ov.utils.download_pathway_database() | |
| # In[14]: | |
| pathway_dict=ov.utils.geneset_prepare('genesets/WikiPathways_2019_Mouse.txt',organism='Mouse') | |
| # Note that the `pvalue_type` we set to `auto`, this is because when the genesets we enrichment if too small, use the `adjusted pvalue` we can't get the correct result. So you can set `adjust` or `raw` to get the significant geneset. | |
| # | |
| # If you didn't have internet, please set `background` to all genes expressed in rna-seq,like: | |
| # | |
| # ```python | |
| # enr=ov.bulk.geneset_enrichment(gene_list=deg_genes, | |
| # pathways_dict=pathway_dict, | |
| # pvalue_type='auto', | |
| # background=dds.result.index.tolist(), | |
| # organism='mouse') | |
| # ``` | |
| # In[15]: | |
| deg_genes=dds.result.loc[dds.result['sig']!='normal'].index.tolist() | |
| enr=ov.bulk.geneset_enrichment(gene_list=deg_genes, | |
| pathways_dict=pathway_dict, | |
| pvalue_type='auto', | |
| organism='mouse') | |
| # To visualize the enrichment, we use `geneset_plot` to finish it | |
| # In[21]: | |
| ov.bulk.geneset_plot(enr,figsize=(2,5),fig_title='Wiki Pathway enrichment', | |
| cax_loc=[2, 0.45, 0.5, 0.02], | |
| bbox_to_anchor_used=(-0.25, -13),node_diameter=10, | |
| custom_ticks=[5,7],text_knock=3, | |
| cmap='Reds') | |
| # ## Multi pathway enrichment | |
| # | |
| # In addition to pathway enrichment for a single database, OmicVerse supports enriching and visualizing multiple pathways at the same time, which is implemented using [`pyComplexHeatmap`](https://dingwb.github.io/PyComplexHeatmap/build/html/notebooks/gene_enrichment_analysis.html), and Citetation is welcome! | |
| # In[22]: | |
| pathway_dict=ov.utils.geneset_prepare('genesets/GO_Biological_Process_2023.txt',organism='Mouse') | |
| enr_go_bp=ov.bulk.geneset_enrichment(gene_list=deg_genes, | |
| pathways_dict=pathway_dict, | |
| pvalue_type='auto', | |
| organism='mouse') | |
| pathway_dict=ov.utils.geneset_prepare('genesets/GO_Molecular_Function_2023.txt',organism='Mouse') | |
| enr_go_mf=ov.bulk.geneset_enrichment(gene_list=deg_genes, | |
| pathways_dict=pathway_dict, | |
| pvalue_type='auto', | |
| organism='mouse') | |
| pathway_dict=ov.utils.geneset_prepare('genesets/GO_Cellular_Component_2023.txt',organism='Mouse') | |
| enr_go_cc=ov.bulk.geneset_enrichment(gene_list=deg_genes, | |
| pathways_dict=pathway_dict, | |
| pvalue_type='auto', | |
| organism='mouse') | |
| # In[167]: | |
| enr_dict={'BP':enr_go_bp, | |
| 'MF':enr_go_mf, | |
| 'CC':enr_go_cc} | |
| colors_dict={ | |
| 'BP':ov.pl.red_color[1], | |
| 'MF':ov.pl.green_color[1], | |
| 'CC':ov.pl.blue_color[1], | |
| } | |
| ov.bulk.geneset_plot_multi(enr_dict,colors_dict,num=3, | |
| figsize=(2,5), | |
| text_knock=3,fontsize=8, | |
| cmap='Reds' | |
| ) | |
| # In[166]: | |
| def geneset_plot_multi(enr_dict,colors_dict,num:int=5,fontsize=10, | |
| fig_title:str='',fig_xlabel:str='Fractions of genes', | |
| figsize:tuple=(2,4),cmap:str='YlGnBu', | |
| text_knock:int=5,text_maxsize:int=20,ax=None, | |
| ): | |
| from PyComplexHeatmap import HeatmapAnnotation,DotClustermapPlotter,anno_label,anno_simple,AnnotationBase | |
| for key in enr_dict.keys(): | |
| enr_dict[key]['Type']=key | |
| enr_all=pd.concat([enr_dict[i].iloc[:num] for i in enr_dict.keys()],axis=0) | |
| enr_all['Term']=[ov.utils.plot_text_set(i.split('(')[0],text_knock=text_knock,text_maxsize=text_maxsize) for i in enr_all.Term.tolist()] | |
| enr_all.index=enr_all.Term | |
| enr_all['Term1']=[i for i in enr_all.index.tolist()] | |
| del enr_all['Term'] | |
| colors=colors_dict | |
| left_ha = HeatmapAnnotation( | |
| label=anno_label(enr_all.Type, merge=True,rotation=0,colors=colors,relpos=(1,0.8)), | |
| Category=anno_simple(enr_all.Type,cmap='Set1', | |
| add_text=False,legend=False,colors=colors), | |
| axis=0,verbose=0,label_kws={'rotation':45,'horizontalalignment':'left','visible':False}) | |
| right_ha = HeatmapAnnotation( | |
| label=anno_label(enr_all.Term1, merge=True,rotation=0,relpos=(0,0.5),arrowprops=dict(visible=True), | |
| colors=enr_all.assign(color=enr_all.Type.map(colors)).set_index('Term1').color.to_dict(), | |
| fontsize=fontsize,luminance=0.8,height=2), | |
| axis=0,verbose=0,#label_kws={'rotation':45,'horizontalalignment':'left'}, | |
| orientation='right') | |
| if ax==None: | |
| fig, ax = plt.subplots(figsize=figsize) | |
| else: | |
| ax=ax | |
| #plt.figure(figsize=figsize) | |
| cm = DotClustermapPlotter(data=enr_all, x='fraction',y='Term1',value='logp',c='logp',s='num', | |
| cmap=cmap, | |
| row_cluster=True,#col_cluster=True,#hue='Group', | |
| #cmap={'Group1':'Greens','Group2':'OrRd'}, | |
| vmin=-1*np.log10(0.1),vmax=-1*np.log10(1e-10), | |
| #colors={'Group1':'yellowgreen','Group2':'orange'}, | |
| #marker={'Group1':'*','Group2':'$\\ast$'}, | |
| show_rownames=True,show_colnames=False,row_dendrogram=False, | |
| col_names_side='top',row_names_side='right', | |
| xticklabels_kws={'labelrotation': 30, 'labelcolor': 'blue','labelsize':fontsize}, | |
| #yticklabels_kws={'labelsize':10}, | |
| #top_annotation=col_ha,left_annotation=left_ha,right_annotation=right_ha, | |
| left_annotation=left_ha,right_annotation=right_ha, | |
| spines=False, | |
| row_split=enr_all.Type,# row_split_gap=1, | |
| #col_split=df_col.Group,col_split_gap=0.5, | |
| verbose=1,legend_gap=10, | |
| #dot_legend_marker='*', | |
| xlabel='Fractions of genes',xlabel_side="bottom", | |
| xlabel_kws=dict(labelpad=8,fontweight='normal',fontsize=fontsize+2), | |
| # xlabel_bbox_kws=dict(facecolor=facecolor) | |
| ) | |
| tesr=plt.gcf().axes | |
| for ax in plt.gcf().axes: | |
| if hasattr(ax, 'get_xlabel'): | |
| if ax.get_xlabel() == 'Fractions of genes': # 假设 colorbar 有一个特定的标签 | |
| cbar = ax | |
| cbar.grid(False) | |
| if ax.get_ylabel() == 'logp': # 假设 colorbar 有一个特定的标签 | |
| cbar = ax | |
| cbar.tick_params(labelsize=fontsize+2) | |
| cbar.set_ylabel(r'$−Log_{10}(P_{adjusted})$',fontsize=fontsize+2) | |
| cbar.grid(False) | |
| return ax | |
| # In[ ]: | |