U mdg @snddlmZmZddlmZddlZddlZddl m Z ddl m Z ddl mZddlmZmZddlmZmZdd lmZdd lmZdd lmZmZmZmZmZmZm Z dd dZ!eeeeeeeddddddd de e"ee"e#ee$e#e#eee$ej%fdddZ&eeee eedddddidddde e"ee"ee'ee"ee(ee#e#e#ee d ddZ)dS))OptionalDict)warnN)AnnData)issparse)logging)view_to_actualcheck_nonnegative_integers) _get_obs_rep _set_obs_rep) _doc_params)pca) doc_adatadoc_dist_params doc_layerdoc_check_valuesdoc_copy doc_inplace doc_pca_chunkFc Cs|r |n|}|dkr td|dkr<|jd}t|}|dkrLtd|rbt|sbtdtt|rtj |dd}tj |dd}t | }n*tj |ddd}tj |ddd}t |}t |||} t || } | t| | d |} tj | | |d } | S) Nrz#Pearson residuals require theta > 0z3Pearson residuals require `clip>=0` or `clip=None`.zT`normalize_pearson_residuals()` expects raw count data, but non-integers were found.)axisT)rZkeepdims)Za_minZa_max) copy ValueErrorshapenpsqrtr r UserWarningrsumZsqueezearrayclip) Xthetar check_valuesrnZ sums_genesZ sums_cells sum_totalmudiff residualsr)^/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/experimental/pp/_normalization.py_pearson_residualss2    r+)adata dist_paramsr#layerinplacerdT)r"r r#r.r/r)r,r"r r#r.r/rreturncCs|r|std|}t|t||d}|r4|nd}d|} t| } t|||||d} t|||d} |rt|| |d| |j d<ntfd| i| } tjd | d |r|S|s| Sd S) aH Applies analytic Pearson residual normalization, based on [Lause21]_. The residuals are based on a negative binomial offset model with overdispersion `theta` shared across genes. By default, residuals are clipped to `sqrt(n_obs)` and overdispersion `theta=100` is used. Expects raw count input. Params ------ {adata} {dist_params} {check_values} {layer} {inplace} {copy} Returns ------- If `inplace=True`, `adata.X` or the selected layer in `adata.layers` is updated with the normalized values. `adata.uns` is updated with the following fields. If `inplace=False`, the same fields are returned as dictionary with the normalized values in `results_dict['X']`. `.uns['pearson_residuals_normalization']['theta']` The used value of the overdisperion parameter theta. `.uns['pearson_residuals_normalization']['clip']` The used value of the clipping parameter. `.uns['pearson_residuals_normalization']['computed_on']` The name of the layer on which the residuals were computed. z0`copy=True` cannot be used with `inplace=False`.)r.zadata.Xz(computing analytic Pearson residuals on )r)r"r computed_onpearson_residuals_normalizationr!z finished ({time_passed}))timeN) rrrr logginfor+dictr uns)r,r"r r#r.r/rr!r2msgstartr(Z settings_dictZ results_dictr)r)r*normalize_pearson_residualsCs(3     r;)r,r-Z pca_chunkr#r/2)r"r n_comps random_state kwargs_pcause_highly_variabler#r/) r,r"r r=r>r?r@r#r/r1c Cs\|rd|krtd|dkr0d|kr0d}|rp|dd|jdf} t| j| jg| jgd} n t|j|jg|jgd} t| |||dt| f||d||rT| j d} t f| d | i} |rt j |j|fd |jd <| jd |jd |jd<n| jd |jd <| j d |j d <| |j d<| jd |jd <dS| SdS)aT Applies analytic Pearson residual normalization and PCA, based on [Lause21]_. The residuals are based on a negative binomial offset model with overdispersion `theta` shared across genes. By default, residuals are clipped to `sqrt(n_obs)`, overdispersion `theta=100` is used, and PCA is run with 50 components. Operates on the subset of highly variable genes in `adata.var['highly_variable']` by default. Expects raw count input. Params ------ {adata} {dist_params} {pca_chunk} use_highly_variable If `True`, uses gene selection present in `adata.var['highly_variable']` to subset the data before normalizing (default). Otherwise, proceed on the full dataset. {check_values} {inplace} Returns ------- If `inplace=False`, returns the Pearson residual-based PCA results (as :class:`~anndata.AnnData` object). If `inplace=True`, updates `adata` with the following fields: `.uns['pearson_residuals_normalization']['pearson_residuals_df']` The subset of highly variable genes, normalized by Pearson residuals. `.uns['pearson_residuals_normalization']['theta']` The used value of the overdisperion parameter theta. `.uns['pearson_residuals_normalization']['clip']` The used value of the clipping parameter. `.obsm['X_pca']` PCA representation of data after gene selection (if applicable) and Pearson residual normalization. `.varm['PCs']` The principal components containing the loadings. When `inplace=True` and `use_highly_variable=True`, this will contain empty rows for the genes not selected. `.uns['pca']['variance_ratio']` Ratio of explained variance. `.uns['pca']['variance']` Explained variance, equivalent to the eigenvalues of the covariance matrix. Zhighly_variablezYou passed `use_highly_variable=True`, but no HVG selection was found (e.g., there was no 'highly_variable' column in adata.var).'NT)obsvar)r"r r#)r=r>r3Zpearson_residuals_df)rZPCsr ZX_pca)Zvar_keysrrBrrr!rAr;r r8r7Zto_dfrZzerosZn_varsZvarmZobsm) r,r"r r=r>r?r@r#r/Z adata_subZ adata_pcaZ norm_settingsZ norm_dictr)r)r*normalize_pearson_residuals_pcasBC   rC)F)*typingrrwarningsrnumpyrZpandaspdZanndatarZ scipy.sparserZscanpyrr5Z scanpy._utilsrr Z scanpy.getr r r Zscanpy.preprocessing._pcar Zscanpy.experimental._docsrrrrrrrr+floatboolstrZndarrayr;intr7rCr)r)r)r*s~      $ ) H