Source code for dynamo.preprocessing.Preprocessor

from typing import Callable, List, Optional

try:
    from typing import Literal
except ImportError:
    from typing_extensions import Literal

import numpy as np
import pandas as pd
from anndata import AnnData

from ..configuration import DKM
from ..dynamo_logger import (
    LoggerManager,
    main_info,
    main_info_insert_adata,
    main_warning,
)
from ..external import (
    normalize_layers_pearson_residuals,
    sctransform,
    select_genes_by_pearson_residuals,
)
from ..tools.connectivity import neighbors as default_neighbors
from .preprocess import normalize_cell_expr_by_size_factors_legacy, pca_monocle
from .preprocessor_utils import _infer_labeling_experiment_type
from .preprocessor_utils import (
    filter_cells_by_outliers as monocle_filter_cells_by_outliers,
)
from .preprocessor_utils import (
    filter_genes_by_outliers as monocle_filter_genes_by_outliers,
)
from .preprocessor_utils import (
    is_log1p_transformed_adata,
    log1p_adata,
    normalize_cell_expr_by_size_factors,
    select_genes_by_dispersion_general,
)
from .utils import (
    collapse_species_adata,
    convert2symbol,
    convert_layers2csr,
    detect_experiment_datatype,
    unique_var_obs_adata,
)


[docs]class Preprocessor: def __init__( self, collapse_speicies_adata_function: Callable = collapse_species_adata, convert_gene_name_function: Callable = convert2symbol, filter_cells_by_outliers_function: Callable = monocle_filter_cells_by_outliers, filter_cells_by_outliers_kwargs: dict = {}, filter_genes_by_outliers_function: Callable = monocle_filter_genes_by_outliers, filter_genes_by_outliers_kwargs: dict = {}, normalize_by_cells_function: Callable = normalize_cell_expr_by_size_factors, normalize_by_cells_function_kwargs: dict = {}, select_genes_function: Callable = select_genes_by_dispersion_general, select_genes_kwargs: dict = {}, normalize_selected_genes_function: Callable = None, normalize_selected_genes_kwargs: dict = {}, use_log1p: bool = True, log1p_kwargs: dict = {}, pca_function: bool = pca_monocle, pca_kwargs: dict = {}, gene_append_list: List[str] = [], gene_exclude_list: List[str] = [], force_gene_list: Optional[List[str]] = None, sctransform_kwargs={}, ) -> None: """Preprocessor constructor. The default preprocess functions are those of monocle recipe by default. You can pass your own Callable objects (functions) to this constructor directly, which wil be used in the preprocess steps later. These functions parameters are saved into Preprocessor instances. You can set these attributes directly to your own implementation. Args: collapse_speicies_adata_function: function for collapsing the species data. Defaults to collapse_species_adata. convert_gene_name_function: transform gene names, by default convert2symbol, which transforms unofficial gene names to official gene names. Defaults to convert2symbol. filter_cells_by_outliers_function: filter cells by thresholds. Defaults to monocle_filter_cells_by_outliers. filter_cells_by_outliers_kwargs: arguments that will be passed to filter_cells_by_outliers. Defaults to {}. filter_genes_by_outliers_function: filter genes by thresholds. Defaults to monocle_filter_genes_by_outliers. filter_genes_by_outliers_kwargs: arguments that will be passed to filter_genes_by_outliers. Defaults to {}. normalize_by_cells_function: function for performing cell-wise normalization. Defaults to normalize_cell_expr_by_size_factors. normalize_by_cells_function_kwargs: arguments that will be passed to normalize_by_cells_function. Defaults to {}. select_genes_function: function for selecting gene features. Defaults to select_genes_by_dispersion_general. select_genes_kwargs: arguments that will be passed to select_genes. Defaults to {}. normalize_selected_genes_function: function for normalize selected genes. Defaults to None. normalize_selected_genes_kwargs: arguments that will be passed to normalize_selected_genes. Defaults to {}. use_log1p: whether to use log1p to normalize layers in adata. Defaults to True. log1p_kwargs: arguments passed to use_log1p. Defaults to {}. pca_function: function to perform pca. Defaults to pca_monocle. pca_kwargs: arguments that will be passed pca. Defaults to {}. gene_append_list: ensure that a list of genes show up in selected genes in monocle recipe pipeline. Defaults to []. gene_exclude_list: exclude a list of genes in monocle recipe pipeline. Defaults to []. force_gene_list: use this gene list as selected genes in monocle recipe pipeline. Defaults to None. sctransform_kwargs: arguments passed into sctransform function. Defaults to {}. """ self.convert_layers2csr = convert_layers2csr self.unique_var_obs_adata = unique_var_obs_adata self.log1p = log1p_adata self.log1p_kwargs = log1p_kwargs self.sctransform = sctransform self.filter_cells_by_outliers = filter_cells_by_outliers_function self.filter_genes_by_outliers = filter_genes_by_outliers_function self.normalize_by_cells = normalize_by_cells_function self.select_genes = select_genes_function self.normalize_selected_genes = normalize_selected_genes_function self.use_log1p = use_log1p self.pca = pca_function self.pca_kwargs = pca_kwargs # self.n_top_genes = n_top_genes self.convert_gene_name = convert_gene_name_function self.collapse_species_adata = collapse_speicies_adata_function self.gene_append_list = gene_append_list self.gene_exclude_list = gene_exclude_list self.force_gene_list = force_gene_list # kwargs pass to the functions above self.filter_genes_by_outliers_kwargs = filter_genes_by_outliers_kwargs self.normalize_by_cells_function_kwargs = normalize_by_cells_function_kwargs self.filter_cells_by_outliers_kwargs = filter_cells_by_outliers_kwargs self.select_genes_kwargs = select_genes_kwargs self.sctransform_kwargs = sctransform_kwargs self.normalize_selected_genes_kwargs = normalize_selected_genes_kwargs
[docs] def add_experiment_info( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """Infer the experiment type and experiment layers stored in the AnnData object and record the info in unstructured metadata (.uns). Args: adata: an AnnData object. tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type. If set to None, the experiment type would be inferred from the data. Defaults to None. Raises: ValueError: the tkey is invalid. """ if DKM.UNS_PP_KEY not in adata.uns.keys(): adata.uns[DKM.UNS_PP_KEY] = {} main_info_insert_adata("%s" % adata.uns["pp"], "uns['pp']", indent_level=2) ( has_splicing, has_labeling, splicing_labeling, has_protein, ) = detect_experiment_datatype(adata) # check whether tkey info exists if has_labeling if has_labeling: main_info("data contains labeling info, checking tkey:" + str(tkey)) if tkey not in adata.obs.keys(): raise ValueError("tkey:%s encoding the labeling time is not existed in your adata." % (str(tkey))) if tkey is not None and adata.obs[tkey].max() > 60: main_warning( "Looks like you are using minutes as the time unit. For the purpose of numeric stability, " "we recommend using hour as the time unit." ) adata.uns["pp"]["tkey"] = tkey adata.uns["pp"]["has_splicing"] = has_splicing adata.uns["pp"]["has_labeling"] = has_labeling adata.uns["pp"]["has_protein"] = has_protein adata.uns["pp"]["splicing_labeling"] = splicing_labeling # infer and set experiment type if experiment_type is None and has_labeling: experiment_type = _infer_labeling_experiment_type(adata, tkey) if experiment_type is None: experiment_type = "conventional" adata.uns["pp"]["experiment_type"] = experiment_type # infer new/total, splicing/unsplicing layers we have in experiment data layers, total_layers = None, None if has_splicing and has_labeling and splicing_labeling: layers = [ "X", "uu", "ul", "su", "sl", "spliced", "unspliced", "new", "total", ] total_layers = ["uu", "ul", "su", "sl"] if has_splicing and has_labeling and not splicing_labeling: layers = ["X", "spliced", "unspliced", "new", "total"] total_layers = ["total"] elif has_labeling and not has_splicing: layers = ["X", "total", "new"] total_layers = ["total"] elif has_splicing and not has_labeling: layers = ["X", "spliced", "unspliced"] adata.uns["pp"]["experiment_layers"] = layers adata.uns["pp"]["experiment_total_layers"] = total_layers
[docs] def standardize_adata(self, adata: AnnData, tkey: str, experiment_type: str) -> None: """Process the AnnData object to make it meet the standards of dynamo. The index of the observations would be ensured to be unique. The layers with sparse matrix would be converted to compressed csr_matrix. DKM.allowed_layer_raw_names() will be used to define only_splicing, only_labeling and splicing_labeling keys. The genes would be renamed to their official name. Args: adata: an AnnData object. tkey: the key for time information (labeling time period for the cells) in .obs. experiment_type: the experiment type. """ adata.uns["pp"] = {} adata.uns["pp"]["norm_method"] = None self.add_experiment_info(adata, tkey, experiment_type) main_info_insert_adata("tkey=%s" % tkey, "uns['pp']", indent_level=2) main_info_insert_adata("experiment_type=%s" % experiment_type, "uns['pp']", indent_level=2) main_info("making adata observation index unique...") self.unique_var_obs_adata(adata) self.convert_layers2csr(adata) if self.collapse_species_adata: main_info("applying collapse species adata...") self.collapse_species_adata(adata) if self.convert_gene_name: main_info("applying convert_gene_name function...") self.convert_gene_name(adata) main_info("making adata observation index unique after gene name conversion...") self.unique_var_obs_adata(adata)
def _filter_cells_by_outliers(self, adata: AnnData) -> None: """Select valid cells based on the method specified as the preprocessor's `filter_cells_by_outliers`. Args: adata: an AnnData object. """ if self.filter_cells_by_outliers: main_info("filtering outlier cells...") main_info("cell filter kwargs:" + str(self.filter_cells_by_outliers_kwargs)) self.filter_cells_by_outliers(adata, **self.filter_cells_by_outliers_kwargs) def _filter_genes_by_outliers(self, adata: AnnData) -> None: """Select valid genes based on the method specified as the preprocessor's `filter_genes_by_outliers`. Args: adata: an AnnData object. """ if self.filter_genes_by_outliers: main_info("filtering outlier genes...") main_info("gene filter kwargs:" + str(self.filter_genes_by_outliers_kwargs)) self.filter_genes_by_outliers(adata, **self.filter_genes_by_outliers_kwargs) def _select_genes(self, adata: AnnData) -> None: """selecting gene by features, based on method specified as the preprocessor's `select_genes`. Args: adata: an AnnData object. """ if self.select_genes: main_info("selecting genes...") main_info("select_genes kwargs:" + str(self.select_genes_kwargs)) self.select_genes(adata, **self.select_genes_kwargs) def _append_gene_list(self, adata: AnnData) -> None: """Add genes to the feature gene list detected by the preprocessing steps. Args: adata: an AnnData object. """ if self.gene_append_list is not None: append_genes = adata.var.index.intersection(self.gene_append_list) adata.var.loc[append_genes, DKM.VAR_USE_FOR_PCA] = True main_info("appended %d extra genes as required..." % len(append_genes)) def _exclude_gene_list(self, adata: AnnData) -> None: """Remove genes from the feature gene list detected by the preprocessing steps. Args: adata: an AnnData object. """ if self.gene_exclude_list is not None: exclude_genes = adata.var.index.intersection(self.gene_exclude_list) adata.var.loc[exclude_genes, DKM.VAR_USE_FOR_PCA] = False main_info("excluded %d genes as required..." % len(exclude_genes)) def _force_gene_list(self, adata: AnnData) -> None: """Use the provided gene list as the feature gene list, overwrite the gene list detected by the preprocessing steps. Args: adata: an AnnData object. """ if self.force_gene_list is not None: adata.var.loc[:, DKM.VAR_USE_FOR_PCA] = False forced_genes = adata.var.index.intersection(self.force_gene_list) adata.var.loc[forced_genes, DKM.VAR_USE_FOR_PCA] = True main_info( "OVERWRITE all gene selection results above according to user gene list inputs. %d genes in use." % len(forced_genes) ) else: main_info("self.force_gene_list is None, skipping filtering by gene list...") def _normalize_selected_genes(self, adata: AnnData) -> None: """Normalize selected genes with method specified in the preprocessor's `normalize_selected_genes` Args: adata: an AnnData object. """ if not callable(self.normalize_selected_genes): main_info( "skipping normalize by selected genes as preprocessor normalize_selected_genes is not callable..." ) return main_info("normalizing selected genes...") self.normalize_selected_genes(adata, **self.normalize_selected_genes_kwargs) def _normalize_by_cells(self, adata: AnnData) -> None: """Performing cell-wise normalization based on method specified as the preprocessor's `normalize_by_cells`. Args: adata: an AnnData object. """ if not callable(self.normalize_by_cells): main_info("skipping normalize by cells as preprocessor normalize_by_cells is not callable...") return main_info("applying normalize by cells function...") self.normalize_by_cells(adata, **self.normalize_by_cells_function_kwargs) def _log1p(self, adata: AnnData) -> None: """Perform log1p on the data with args specified in the preprocessor's `log1p_kwargs`. Args: adata: an AnnData object. """ if self.use_log1p: if is_log1p_transformed_adata(adata): main_warning( "Your adata.X maybe log1p transformed before. If you are sure that your adata is not log1p transformed, please ignore this warning. Dynamo will do log1p transformation still." ) # TODO: the following line is for monocle recipe and later dynamics matrix recovery # refactor with dynamics module adata.uns["pp"]["norm_method"] = "log1p" main_info("applying log1p transformation on expression matrix data (adata.X)...") self.log1p(adata, **self.log1p_kwargs) def _pca(self, adata: AnnData) -> None: """Perform pca reduction with args specified in the preprocessor's `pca_kwargs`. Args: adata: an AnnData object. """ if self.pca: main_info("reducing dimension by PCA...") self.pca(adata, **self.pca_kwargs)
[docs] def config_monocle_recipe( self, adata: AnnData, n_top_genes: int = 2000, gene_selection_method: str = "SVR" ) -> None: """Automatically configure the preprocessor for monocle recipe. Args: adata: an AnnData object. n_top_genes: Number of top feature genes to select in the preprocessing step. Defaults to 2000. gene_selection_method: Which sorting method to be used to select genes. Defaults to "SVR". """ n_obs, n_genes = adata.n_obs, adata.n_vars n_cells = n_obs self.use_log1p = False self.filter_cells_by_outliers = monocle_filter_cells_by_outliers self.filter_cells_by_outliers_kwargs = { "filter_bool": None, "layer": "all", "min_expr_genes_s": min(50, 0.01 * n_genes), "min_expr_genes_u": min(25, 0.01 * n_genes), "min_expr_genes_p": min(2, 0.01 * n_genes), "max_expr_genes_s": np.inf, "max_expr_genes_u": np.inf, "max_expr_genes_p": np.inf, "shared_count": None, } self.filter_genes_by_outliers = monocle_filter_genes_by_outliers self.filter_genes_by_outliers_kwargs = { "filter_bool": None, "layer": "all", "min_cell_s": max(5, 0.01 * n_cells), "min_cell_u": max(5, 0.005 * n_cells), "min_cell_p": max(5, 0.005 * n_cells), "min_avg_exp_s": 0, "min_avg_exp_u": 0, "min_avg_exp_p": 0, "max_avg_exp": np.inf, "min_count_s": 0, "min_count_u": 0, "min_count_p": 0, "shared_count": 30, } self.select_genes = select_genes_by_dispersion_general self.select_genes_kwargs = { "recipe": "monocle", "monocle_kwargs": { "sort_by": gene_selection_method, "n_top_genes": n_top_genes, "keep_filtered": True, "SVRs_kwargs": { "min_expr_cells": 0, "min_expr_avg": 0, "max_expr_avg": np.inf, "svr_gamma": None, "winsorize": False, "winsor_perc": (1, 99.5), "sort_inverse": False, }, "only_bools": True, }, } self.normalize_selected_genes = None self.normalize_by_cells = normalize_cell_expr_by_size_factors # recipe monocle log1p all raw data in normalize_by_cells (dynamo version), so we do not need extra log1p transform. self.use_log1p = False self.pca = pca_monocle self.pca_kwargs = {"pca_key": "X_pca"}
[docs] def preprocess_adata_monocle( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """Preprocess the AnnData object based on Monocle style preprocessing recipe. Args: adata: an AnnData object. tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type of the data. If not provided, would be inferred from the data. Defaults to None. """ main_info("Running preprocessing pipeline...") temp_logger = LoggerManager.gen_logger("preprocessor-monocle") temp_logger.log_time() self.standardize_adata(adata, tkey, experiment_type) self._filter_cells_by_outliers(adata) self._filter_genes_by_outliers(adata) self._filter_cells_by_outliers(adata) self._select_genes(adata) # gene selection has been completed above. Now we need to append/delete/force selected gene list required by users. self._append_gene_list(adata) self._exclude_gene_list(adata) self._force_gene_list(adata) self._normalize_selected_genes(adata) self._normalize_by_cells(adata) self._log1p(adata) self._pca(adata) temp_logger.finish_progress(progress_name="preprocess")
[docs] def config_seurat_recipe(self, adata: AnnData) -> None: """Automatically configure the preprocessor for using the seurat style recipe. Args: adata: an AnnData object. """ self.config_monocle_recipe(adata) self.select_genes = select_genes_by_dispersion_general self.select_genes_kwargs = {"recipe": "seurat", "n_top_genes": 2000} self.normalize_by_cells_function_kwargs = {"skip_log": True} self.pca_kwargs = {"pca_key": "X_pca"} self.filter_genes_by_outliers_kwargs = {"shared_count": 20} self.use_log1p = True self.log1p_kwargs = {"layers": ["X"]}
[docs] def preprocess_adata_seurat( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """The preprocess pipeline in Seurat based on dispersion, implemented by dynamo authors. Stuart and Butler et al. Comprehensive Integration of Single-Cell Data. Cell (2019) Butler et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol Args: adata: an AnnData object tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type of the data. If not provided, would be inferred from the data. Defaults to None. """ temp_logger = LoggerManager.gen_logger("preprocessor-seurat") temp_logger.log_time() main_info("Applying Seurat recipe preprocessing...") self.standardize_adata(adata, tkey, experiment_type) self._filter_genes_by_outliers(adata) self._normalize_by_cells(adata) self._select_genes(adata) self._log1p(adata) self._pca(adata) temp_logger.finish_progress(progress_name="preprocess by seurat recipe")
[docs] def config_sctransform_recipe(self, adata: AnnData) -> None: """Automatically configure the preprocessor for using the sctransform style recipe. Args: adata: an AnnData object. """ self.use_log1p = False raw_layers = DKM.get_raw_data_layers(adata) self.filter_cells_by_outliers_kwargs = {"keep_filtered": False} self.filter_genes_by_outliers_kwargs = { "inplace": True, "min_cell_s": 5, "min_count_s": 1, "min_cell_u": 5, "min_count_u": 1, } self.select_genes_kwargs = {"inplace": True} self.sctransform_kwargs = {"layers": raw_layers, "n_top_genes": 2000} self.pca_kwargs = {"pca_key": "X_pca", "n_pca_components": 50}
[docs] def preprocess_adata_sctransform( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """Python implementation of https://github.com/satijalab/sctransform. Hao and Hao et al. Integrated analysis of multimodal single-cell data. Cell (2021) Args: adata: an AnnData object tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type of the data. If not provided, would be inferred from the data. Defaults to None. """ temp_logger = LoggerManager.gen_logger("preprocessor-sctransform") temp_logger.log_time() main_info("Applying Sctransform recipe preprocessing...") self.standardize_adata(adata, tkey, experiment_type) self._filter_cells_by_outliers(adata) self._filter_genes_by_outliers(adata) self._select_genes(adata) # TODO: if inplace in select_genes is True, the following subset is unnecessary. adata._inplace_subset_var(adata.var["use_for_pca"]) self.sctransform(adata, **self.sctransform_kwargs) self._pca(adata) temp_logger.finish_progress(progress_name="preprocess by sctransform recipe")
[docs] def config_pearson_residuals_recipe(self, adata: AnnData) -> None: """Automatically configure the preprocessor for using the Pearson residuals style recipe. Args: adata: an AnnData object. """ self.filter_cells_by_outliers = None self.filter_genes_by_outliers = None self.normalize_by_cells = None self.select_genes = select_genes_by_pearson_residuals self.select_genes_kwargs = {"n_top_genes": 2000} self.normalize_selected_genes = normalize_layers_pearson_residuals # select layers in adata to be normalized normalize_layers = DKM.get_raw_data_layers(adata) self.normalize_selected_genes_kwargs = {"layers": normalize_layers, "copy": False} self.pca_kwargs = {"pca_key": "X_pca", "n_pca_components": 50} self.use_log1p = False
[docs] def preprocess_adata_pearson_residuals( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """A pipeline proposed in Pearson residuals (Lause, Berens & Kobak, 2021). Lause, J., Berens, P. & Kobak, D. Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data. Genome Biol 22, 258 (2021). https://doi.org/10.1186/s13059-021-02451-7 Args: adata: an AnnData object tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type of the data. If not provided, would be inferred from the data. Defaults to None. """ temp_logger = LoggerManager.gen_logger("preprocessor-sctransform") temp_logger.log_time() self.standardize_adata(adata, tkey, experiment_type) self._select_genes(adata) self._normalize_selected_genes(adata) self._pca(adata) temp_logger.finish_progress(progress_name="preprocess by pearson residual recipe")
[docs] def config_monocle_pearson_residuals_recipe(self, adata: AnnData) -> None: """Automatically configure the preprocessor for using the Monocle-Pearson-residuals style recipe. Useful when you want to use Pearson residual to obtain feature genes and perform PCA but also using the standard size-factor normalization and log1p analyses to normalize data for RNA velocity and vector field analyses. Args: adata: an AnnData object. """ self.config_monocle_recipe(adata) # self.filter_cells_by_outliers = None # self.filter_genes_by_outliers = None self.normalize_by_cells = normalize_cell_expr_by_size_factors self.select_genes = select_genes_by_pearson_residuals self.select_genes_kwargs = {"n_top_genes": 2000} self.normalize_selected_genes = normalize_layers_pearson_residuals self.normalize_selected_genes_kwargs = {"layers": ["X"], "copy": False} self.pca_kwargs = {"pca_key": "X_pca", "n_pca_components": 50} self.use_log1p = False
[docs] def preprocess_adata_monocle_pearson_residuals( self, adata: AnnData, tkey: Optional[str] = None, experiment_type: Optional[str] = None ) -> None: """A combined pipeline of monocle and pearson_residuals. Results after running pearson_residuals can contain negative values, an undesired feature for later RNA velocity analysis. This function combine pearson_residual and monocle recipes so that it uses Pearson residual to obtain feature genes and perform PCA but also uses monocle recipe to generate X_spliced, X_unspliced, X_new, X_total or other data values for RNA velocity and downstream vector field analyses. Args: adata: an AnnData object tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. experiment_type: the experiment type of the data. If not provided, would be inferred from the data. Defaults to None. """ temp_logger = LoggerManager.gen_logger("preprocessor-monocle-pearson-residual") temp_logger.log_time() self.standardize_adata(adata, tkey, experiment_type) self._select_genes(adata) X_copy = adata.X.copy() self._normalize_by_cells(adata) adata.X = X_copy self._normalize_selected_genes(adata) # use monocle to pprocess adata # self.config_monocle_recipe(adata_copy) # self.pca = None # do not do pca in this monocle # self.preprocess_adata_monocle(adata_copy) # for layer in adata_copy.layers: # if DKM.is_layer_X_key(layer): # adata.layers[layer] = adata. self.pca(adata, **self.pca_kwargs) temp_logger.finish_progress(progress_name="preprocess by monocle pearson residual recipe")
[docs] def preprocess_adata( self, adata: AnnData, recipe: Literal[ "monocle", "seurat", "sctransform", "pearson_residuals", "monocle_pearson_residuals" ] = "monocle", tkey: Optional[str] = None, ) -> None: """Preprocess the AnnData object with the recipe specified. Args: adata: An AnnData object. recipe: The recipe used to preprocess the data. Defaults to "monocle". tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None. Raises: NotImplementedError: the recipe is invalid. """ if recipe == "monocle": self.config_monocle_recipe(adata) self.preprocess_adata_monocle(adata, tkey=tkey) elif recipe == "seurat": self.config_seurat_recipe(adata) self.preprocess_adata_seurat(adata, tkey=tkey) elif recipe == "sctransform": self.config_sctransform_recipe(adata) self.preprocess_adata_sctransform(adata, tkey=tkey) elif recipe == "pearson_residuals": self.config_pearson_residuals_recipe(adata) self.preprocess_adata_pearson_residuals(adata, tkey=tkey) elif recipe == "monocle_pearson_residuals": self.config_monocle_pearson_residuals_recipe(adata) self.preprocess_adata_monocle_pearson_residuals(adata, tkey=tkey) else: raise NotImplementedError("preprocess recipe chosen not implemented: %s" % (recipe))