Add a tool for ITHCNA

[Gregor Sturm]

Intratumoral heterogeneity scores based on CNAs and gene expressions
The calculations of intratumoral heterogeneity scores were inspired by a previous study and modified as follows35. First, to calculate ITHCNA, we used the relative expression value matrix generated by inferCNV and calculated the pairwise cell–cell distances using Pearson's correlation coefficients for each patient. ITHCNA was defined as interquartile range (IQR) of the distribution for all malignant cell pairs' Pearson's correlation coefficients. Similarly, we also used gene expression profiles of cancer cells of each patient to construct the distribution of the intratumoral distances. ITHGEX was assigned as the IQR of the distribution. Public single-cell lung cancer datasets GSE131907 and E-MTAB-6149 were used to calculate the ITHGEX scores of early-stage and advanced stage lung cancer.

https://www.nature.com/articles/s41467-021-22801-0#Sec11

[Gregor Sturm]

@elisapanizzolo, would be great if you could add a PR with your implementation of ITHCNA to this library.

Here's an example of how a tool is implemented in infercnvpy:

infercnvpy/infercnvpy/tl/_infercnv.py

Lines 14 to 66 in cf40cb2

 def cnv_score( 
     adata: AnnData, 
     *, 
     obs_key: str = "cnv_leiden", 
     use_rep: str = "cnv", 
     key_added: str = "cnv_score", 
     inplace: bool = True, 
 ): 
     """Assign each cnv cluster a CNV score. 
  
     Clusters with a high score are likely affected by copy number abberations. 
     Based on this score, cells can be divided into tumor/normal cells. 
  
     Ths score is currently simply defined as the mean of result of 
     :func:`infercnvpy.tl.infercnv` for each cluster. 
  
     Parameters 
     ---------- 
     adata 
         annotated data matrix 
     obs_key 
         Key under which the clustering is stored in adata.obs. Usually 
         the result of :func:`infercnvpy.tl.leiden`, but could also be 
         other clusters, e.g. obtained from transcriptomics data. 
     use_rep 
         Key under which the result of :func:`infercnvpy.tl.infercnv` is stored 
         in adata. 
     key_added 
         Key under which the score will be stored in `adata.obs`. 
     inplace 
         If True, store the result in adata, otherwise return it. 
  
     Returns 
     ------- 
     Depending on the value of `inplace`, either returns `None` or a vector 
     with scores. 
     """ 
     if obs_key not in adata.obs.columns and obs_key == "cnv_leiden": 
         raise ValueError( 
             "`cnv_leiden` not found in `adata.obs`. Did you run `tl.leiden`?" 
         ) 
     cluster_score = { 
         cluster: np.mean( 
             np.abs(adata.obsm[f"X_{use_rep}"][adata.obs[obs_key] == cluster, :]) 
         ) 
         for cluster in adata.obs[obs_key].unique() 
     } 
     score_array = np.array([cluster_score[c] for c in adata.obs[obs_key]]) 
  
     if inplace: 
         adata.obs[key_added] = score_array 
     else: 
         return score_array 

We can discuss details after the holidays.

Cheers,
Gregor

[Gregor Sturm]

Never mind, I already implemented it here based on @elisapanizzolo's code.

Will eventually add it to this repo.