import argparse
import random
import time
from collections.abc import Iterable
from typing import List, Set, Tuple
import numpy as np
from ...logging import logger_manager as lm
from .MuSIC import MuSIC
np.random.seed(888)
random.seed(888)
# To allow for running by function definition (for more flexible option)
[docs]def define_spateo_argparse(**kwargs):
"""Defines and returns MPI and argparse objects for model fitting and interpretation.
Args:
kwargs: Keyword arguments for any of the argparse arguments defined below.
Parser arguments:
run_upstream: Flag to run the upstream target selection step. If True, will run the target selection step
adata_path: Path to AnnData object containing gene expression data. This or 'csv_path' must be given to run.
csv_path: Path to .csv file containing gene expression data. This or 'adata_path' must be given to run.
n_spatial_dim_csv: Number of spatial dimensions to the data provided to 'csv_path'. Defaults to 2.
spatial_subsample: Flag to subsample the data- at a big picture level, this will be done by dividing the tissue
into regions and subsampling from each of these regions. Recommended for large datasets (>5000 samples).
multiscale: Flag to create multiscale models. Currently, it is recommended to only create multiscale models
for Gaussian data.
multiscale_params_only: Flag to return additional metrics along with the coefficients for multiscale models (
specifying this argument sets Flag to True)
mod_type: The type of model that will be employed- this dictates how the data will be processed and
prepared. Options:
- "niche": Spatially-aware, uses categorical cell type labels as independent variables.
- "lr": Spatially-aware, essentially uses the combination of receptor expression in the "target" cell
and spatially lagged ligand expression in the neighboring cells as independent variables.
- "ligand": Spatially-aware, essentially uses ligand expression in the neighboring cells as
independent variables.
- "receptor": Uses receptor expression in the "target" cell as independent variables.
- "downstream": For the purposes of downstream analysis, used to model ligand expression as a
function of upstream regulators
include_unpaired_lr: Only if :attr:`mod_type` is "lr"- if True, will include individual ligands/complexes and
individual receptors in the design matrix if their cognate interacting partners cannot also be found.
cci_dir: Path to directory containing cell-cell interaction databases
species: Selects the cell-cell communication database the relevant ligands will be drawn from. Options:
"human", "mouse".
output_path: Full path name for the .csv file in which results will be saved. Make sure the parent directory
is empty- any existing files will be deleted. It is recommended to create a new folder to serve as the
output directory. This should be supplied of the form '/path/to/file.csv', where file.csv will store
coefficients. The name of the target will be appended at runtime.
custom_lig_path: Path to .txt file containing a custom list of ligands. Each ligand should have its own line
in the .txt file.
ligand: Alternative to the custom ligand path, can be used to provide a single ligand or a list of ligands (
separated by whitespace in the command line).
custom_rec_path: Path to .txt file containing a custom list of receptors. Each receptor should have its own
line in the .txt file.
receptor: Alternative to the custom receptor path, can be used to provide a single receptor or a list of
receptors (separated by whitespace in the command line).
custom_pathways_path: Path to .txt file containing a custom list of pathways. Each pathway should have its own
line in the .txt file.
pathway: Alternative to the custom pathway path, can be used to provide a single pathway or a list of pathways (
separated by whitespace in the command line).
targets_path: Path to .txt file containing a custom list of targets. Each target should have its own line in
the .txt file.
target: Alternative to the custom target path, can be used to provide a single target or a list of targets (
separated by whitespace in the command line).
init_betas_path: Optional path to a .json file or .csv file containing initial coefficient values for the model
for each target variable. If encoded in .json, keys should be target gene names, values should be numpy
arrays containing coefficients. If encoded in .csv, columns should be target gene names. Initial
coefficients should have shape [n_features, ].
normalize: Flag to perform library size normalization, to set total counts in each cell to the same
number (adjust for cell size). Will be set to True if provided.
smooth: Flag to correct for dropout effects by leveraging gene expression neighborhoods to smooth
expression. It is advisable not to do this if performing Poisson or negative binomial regression. Will
be set to True if provided.
log_transform: Flag for whether log-transformation should be applied to expression. It is advisable not to do
this if performing Poisson or negative binomial regression. Will be set to True if provided.
normalize_signaling: Flag to minmax scale the final ligand expression array (for :attr `mod_type` =
"ligand"), or the final ligand-receptor array (for :attr `mod_type` = "lr"). This is recommended to
associate downstream expression with rarer/less prevalent signaling mechanisms.
target_expr_threshold: Only used when automatically selecting targets- finds the L:R-downstream TFs and their
targets and searches for expression above a threshold proportion of cells to filter to a subset of
candidate target genes. This argument sets that proportion, and defaults to 0.05.
multicollinear_threshold: Variance inflation factor threshold used to filter out multicollinear features. A
value of 5 or 10 is recommended.
coords_key: Entry in :attr:`adata` .obsm that contains spatial coordinates. Defaults to "spatial".
group_key: Entry in :attr:`adata` .obs that contains cell type labels. Required for 'mod_type' = "niche".
group_subset: Subset of cell types to include in the model (provided as a whitespace-separated list in
command line). If given, will consider only cells of these types in modeling. Defaults to all cell types.
covariate_keys: Entries in :attr:`adata` .obs or :attr:`adata` .var that contain covariates to include
in the model. Can be provided as a whitespace-separated list in the command line. Numerical covariates
should be minmax scaled between 0 and 1.
total_counts_key: Entry in :attr:`adata` .obs that contains total counts for each cell. Required if subsetting
by total counts. Defaults to "total_counts".
total_counts_threshold: Threshold for total counts to subset cells by- cells with total counts greater than
this threshold will be retained.
bw: Bandwidth for kernel density estimation. Consists of either a distance value or N for the number of
nearest neighbors, depending on :attr:`bw_fixed`
minbw: For use in automated bandwidth selection- the lower-bound bandwidth to test.
maxbw: For use in automated bandwidth selection- the upper-bound bandwidth to test.
bw_fixed: Flag to use a fixed bandwidth (True) or to automatically select a bandwidth (False). This should be
True if the input to/values to test for :attr:`bw` are distance values, and False if they are numbers of
neighbors.
exclude_self: Flag to exclude the target cell from the neighborhood when computing spatial weights. Note that
if True and :attr:`bw` is defined by the number of neighbors, your desired bw should be 1 + the number of
neighbors you want to include.
kernel: Type of kernel function used to weight observations when computing spatial weights and fitting the
model; one of "bisquare", "exponential", "gaussian", "quadratic", "triangular" or "uniform".
distance_membrane_bound: In model setup, distance threshold to consider cells as neighbors for membrane-bound
ligands. If provided, will take priority over :attr 'n_neighbors_membrane_bound'.
distance_secreted: In model setup, distance threshold to consider cells as neighbors for secreted or ECM
ligands. If provided, will take priority over :attr 'n_neighbors_secreted'.
n_neighbors_membrane_bound: For :attr:`mod_type` "ligand" or "lr"- ligand expression will be taken from the
neighboring cells- this defines the number of cells to use for membrane-bound ligands. Defaults to 8.
n_neighbors_secreted: For :attr:`mod_type` "ligand" or "lr"- ligand expression will be taken from the
neighboring cells- this defines the number of cells to use for secreted or ECM ligands.
distr: Distribution family for the dependent variable; one of "gaussian", "poisson", "nb"
fit_intercept: Flag to fit an intercept term in the model. Will be set to True if provided.
tolerance: Convergence tolerance for IWLS
max_iter: Maximum number of iterations for IWLS
patience: When checking various values for the bandwidth, this is the number of iterations to wait for
without the score changing before stopping. Defaults to 5.
ridge_lambda: Sets the strength of the regularization, between 0 and 1. The higher values typically will
result in more features removed.
search_bw: For downstream analysis; specifies the bandwidth to search for senders/receivers. Recommended to
set equal to the bandwidth of a fitted model.
top_k_receivers: For downstream analysis, specifically when constructing vector fields of signaling effects.
Specifies the number of nearest neighbors to consider when computing signaling effect vectors.
filter_targets: For downstream analysis, specifically :func `infer_effect_direction`; if True, will subset to
only the targets that were predicted well by the model.
filter_target_threshold: For downstream analysis, specifically :func `infer_effect_direction`; specifies the
threshold Pearson coefficient for target subsetting. Only used if `filter_targets` is True.
diff_sending_or_receiving: For downstream analyses, specifically :func
`sender_receiver_effect_deg_detection`; specifies whether to compute differential expression of genes in
cells with high or low sending effect potential ('sending cells') or high or low receiving effect
potential ('receiving cells').
target_for_downstream: A string or a list (provided as a whitespace-separated list in the command line) of
target genes for :func `get_effect_potential`, :func `get_pathway_potential` and :func
`calc_and_group_sender_receiver_effect_degs` (provide only one target), as well as :func
`compute_cell_type_coupling` (can provide multiple targets).
ligand_for_downstream: For downstream analyses; used for :func `get_effect_potential` and :func
`calc_and_group_sender_receiver_effect_degs`, used to specify the ligand gene to consider with respect
to the target.
receptor_for_downstream: For downstream analyses; used for :func `get_effect_potential` and :func
`calc_and_group_sender_receiver_effect_degs`, used to specify the receptor gene to consider with respect
to the target.
pathway_for_downstream: For downstream analyses; used for :func `get_pathway_potential` and :func
`calc_and_group_sender_receiver_effect_degs`, used to specify the pathway to consider with respect to
the target.
sender_ct_for_downstream: For downstream analyses; used for :func `get_effect_potential` and :func
`calc_and_group_sender_receiver_effect_degs`, used to specify the cell type to consider as a sender.
receiver_ct_for_downstream: For downstream analyses; used for :func `get_effect_potential` and :func
`calc_and_group_sender_receiver_effect_degs`, used to specify the cell type to consider as a receiver.
n_components: Used for :func `CCI_sender_deg_detection` and :func `CCI_receiver_deg_detection`;
determines the dimensionality of the space to embed into using UMAP.
cci_degs_model_interactions: Used for :func `CCI_sender_deg_detection`; if True, will consider transcription
factor interactions with cofactors and other transcription factors, with these interactions combined into
features. If False, will use each cofactor independently in the prediction.
no_cell_type_markers: Used for :func `CCI_receiver_deg_detection`; if True, will exclude cell type markers
from the set of genes for which to compare to sent/received signal.
compute_pathway_effect: Used for :func `inferred_effect_direction`; if True, will summarize the effects of all
ligands/ligand-receptor interactions in a pathway.
Returns:
parser: Argparse object defining important arguments for model fitting and interpretation
args_list: If argparse object is returned from a function, the parser must read in arguments in the form of a
list- this return contains that processed list.
"""
logger = lm.get_main_logger()
arg_dict = {
"-run_upstream": {
"action": "store_true",
},
"-adata_path": {
"type": str,
},
"-csv_path": {
"type": str,
"help": "Can be used to provide a .csv file, containing gene expression data or any other kind of data. "
"Assumes the first few columns contain spatial coordinates (depending on input to 'n_spatial_dim_csv') and "
"then "
"dependent variable values, in that order.",
},
"-n_spatial_dim_csv": {
"type": int,
"default": 2,
"help": "If using a .csv file, specifies the number of spatial dimensions. Default is 2.",
},
"-spatial_subsample": {
"action": "store_true",
"help": "Recommended for large datasets (>5000 samples), otherwise model fitting is quite slow.",
},
"-mod_type": {
"type": str,
"default": "niche",
},
"-include_unpaired_lr": {
"action": "store_true",
"help": "Only if 'mod_type' is 'lr'- if True, will include individual ligands/complexes and individual "
"receptors/complexes together with L:R pairs if their cognate interacting partners cannot also be "
"found.",
},
"-cci_dir": {"type": str},
"-species": {"type": str, "default": "human"},
"-output_path": {
"type": str,
"default": "./output/stgwr_results.csv",
"help": "Path to output file. Make sure the parent directory is empty- "
"any existing files will be deleted. It is recommended to create "
"a new folder to serve as the output directory. This should be "
"supplied of the form '/path/to/file.csv', where file.csv will "
"store coefficients. The name of the target will be appended at runtime.",
},
"-custom_lig_path": {"type": str},
"-ligand": {
"nargs": "+",
"type": str,
"help": "Alternative to the custom ligand path, can be used to provide a custom list of ligands.",
},
"-custom_rec_path": {"type": str},
"-receptor": {
"nargs": "+",
"type": str,
"help": "Alternative to the custom receptor path, can be used to provide a custom list of receptors.",
},
"-custom_pathways_path": {"type": str},
"-pathway": {
"nargs": "+",
"type": str,
"help": "Alternative to the custom pathway path, can be used to provide a custom list of pathways.",
},
"-targets_path": {"type": str},
"-target": {
"nargs": "+",
"type": str,
"help": "Alternative to the custom target path, can be used to provide a custom list of target molecules.",
},
"-init_betas_path": {"type": str},
"-normalize": {
"action": "store_true",
},
"-smooth": {
"action": "store_true",
},
"-log_transform": {
"action": "store_true",
},
"-normalize_signaling": {
"action": "store_true",
"help": "For ligand, receptor or L:R models, normalize computed signaling values. This should be used to "
"find signaling effects that may be mediated by rarer signals.",
},
"-target_expr_threshold": {
"default": 0.05,
"type": float,
"help": "For automated selection, the threshold proportion of cells for which transcript "
"needs to be expressed in to be selected as a target of interest. Not used if 'targets_path' is not None.",
},
"-multicollinear_threshold": {
"type": float,
"help": "Used only if `mod_type` is 'slice'. If this argument is provided, independent variables that are "
"highly correlated will be filtered out based on variance inflation factor threshold. A value "
"of 5 or 10 is recommended. This can be useful in reducing computation time.",
},
"-coords_key": {"default": "spatial", "type": str},
"-group_key": {
"default": "cell_type",
"type": str,
"help": "Key to entry in .obs containing cell type or other category labels. Required if 'mod_type' is "
"'niche'.",
},
"-group_subset": {
"nargs": "+",
"type": str,
"help": "If provided, only cells with labels that correspond to these group(s) will be used as prediction "
"targets. Will search in key corresponding to the input to arg 'cell_type' if given.",
},
"-covariate_keys": {
"nargs": "+",
"type": str,
"help": "Any number of keys to entry in .obs or .var_names of an "
"AnnData object. Values here will be added to"
"the model as covariates.",
},
"-total_counts_key": {
"default": "total_counts",
"type": str,
"help": "Key to entry in .obs containing total counts per cell. Will be used if 'total_counts_threshold' "
"is provided.",
},
"-total_counts_threshold": {
"default": 0.0,
"type": float,
"help": "If provided, cells with total counts below this threshold will be removed in preprocessing.",
},
"-bw": {
"type": float,
},
"-minbw": {
"type": float,
},
"-maxbw": {
"type": float,
},
"-bw_fixed": {
"action": "store_true",
"help": "If this argument is provided, the bandwidth will be interpreted as a distance during kernel "
"operations. If not, it will be interpreted as the number of nearest neighbors.",
},
"-exclude_self": {
"action": "store_true",
"help": "When computing spatial weights, do not count the cell itself as a neighbor. Recommended to set to "
"True for the CCI models because the independent variable array is also spatially-dependent.",
},
"-kernel": {
"default": "bisquare",
"type": str,
"help": "Kernel to use when computing spatial weights and when fitting the model.",
},
"-use_expression_neighbors": {
"action": "store_true",
"help": "The default for finding spatial neighborhoods for the modeling process is to use neighbors in "
"physical space. If this argument is provided, expression will instead be used to find neighbors.",
},
"-distance_membrane_bound": {
"type": float,
"help": "In model setup, distance threshold to consider cells as neighbors for membrane-bound ligands. If"
"provided, will take priority over 'n_neighbors_membrane_bound'.",
},
"-distance_secreted": {
"type": float,
"help": "In model setup, distance threshold to consider cells as neighbors for secreted or ECM ligands. If"
"provided, will take priority over 'n_neighbors_secreted'.",
},
"-n_neighbors_membrane_bound": {
"default": 6,
"type": int,
"help": "Only used if `mod_type` is 'niche', to define the number of neighbors to consider for each "
"cell when defining the independent variable array for membrane-bound ligands. Will also be used to "
"define the number of neighbors to consider for lagged ligand expression.",
},
"-n_neighbors_secreted": {
"default": 25,
"type": int,
"help": "Only used if `mod_type` is 'niche', to define the number of neighbors to consider for each "
"cell when defining the independent variable array for secreted or ECM ligands. Will also be used to "
"define the number of neighbors to consider for lagged ligand expression.",
},
"-distr": {"default": "gaussian", "type": str},
"-fit_intercept": {"action": "store_true"},
"-tolerance": {"default": 1e-3, "type": float},
"-max_iter": {"default": 500, "type": int},
"-patience": {"default": 5, "type": int},
"-ridge_lambda": {"default": 0.3, "type": float},
# Downstream arguments:
"-search_bw": {
"type": float,
"help": "Used for downstream analyses; specifies the bandwidth to search for "
"senders/receivers. Recommended to set equal to the bandwidth of a fitted "
"model.",
},
"-top_k_receivers": {
"default": 10,
"type": int,
"help": "Used for downstream analyses, specifically for :func `define_effect_vf`; specifies the number of "
"nearest neighbors to consider when computing signaling effect vectors.",
},
"-filter_targets": {
"action": "store_true",
"help": "Used for downstream analyses, specifically :func `inferred_effect_direction`; if True, "
"will subset to only the targets that were predicted well by the model.",
},
"-filter_target_threshold": {
"default": 0.65,
"type": float,
"help": "Used for downstream analyses, specifically :func `infer_effect_direction`; specifies the threshold "
"Pearson coefficient for target subsetting. Only used if `filter_targets` is True.",
},
"-diff_sending_or_receiving": {
"default": "sending",
"type": str,
"help": "Used for downstream analyses, specifically :func `sender_receiver_effect_deg_detection`; specifies "
"whether to compute differential expression of genes in cells with high or low sending effect potential "
"('sending cells') or high or low receiving effect potential ('receiving cells').",
},
"-target_for_downstream": {
"nargs": "+",
"type": str,
"help": "Used for :func `get_effect_potential`, :func `get_pathway_potential` and :func "
"`calc_and_group_sender_receiver_effect_degs` (provide only one target), as well as :func "
"`compute_cell_type_coupling` (can provide multiple targets). Used to specify the target "
"gene(s) to analyze with these functions.",
},
"-ligand_for_downstream": {
"type": str,
"help": "Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the ligand gene to consider with respect to the target.",
},
"-receptor_for_downstream": {
"type": str,
"help": "Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the receptor gene to consider with respect to the target.",
},
"-pathway_for_downstream": {
"type": str,
"help": "Used for :func `get_pathway_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the pathway to consider with respect to the target.",
},
"-sender_ct_for_downstream": {
"type": str,
"help": "Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the cell type to consider as a sender.",
},
"-receiver_ct_for_downstream": {
"type": str,
"help": "Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the cell type to consider as a receiver.",
},
"-n_components": {
"default": 20,
"type": int,
"help": "Used for :func `CCI_sender_deg_detection` and :func `CCI_receiver_deg_detection`; determines the "
"dimensionality of the space to embed into using UMAP.",
},
"-cci_degs_model_interactions": {
"action": "store_true",
"help": "Used for :func `CCI_sender_deg_detection`; if True, will consider transcription factor "
"interactions with cofactors and other transcription factors, with these interactions combined into "
"features. If False, will use each cofactor independently in the prediction.",
},
"-no_cell_type_markers": {
"action": "store_true",
"help": "Used for :func `CCI_receiver_deg_detection`; if True, will exclude cell type "
"markers from the set of genes for which to compare to sent/received signal.",
},
"-compute_pathway_effect": {
"action": "store_true",
"help": "Used for :func `inferred_effect_direction`; if True, will summarize the effects of all "
"ligands/ligand-receptor interactions in a pathway.",
},
}
# Update kwargs to match the argparse signature:
kwargs = {f"-{key}": value for key, value in kwargs.items()}
# Iterate over the keyword items to validate the form of the given arguments:
for key, value in kwargs.items():
if key not in arg_dict.keys():
raise ValueError(f"Argument {key} not recognized. Please check the documentation for valid arguments.")
arg_info = arg_dict[key]
arg_type = arg_info.get("type")
if arg_type is not None:
if not isinstance(value, arg_type):
raise TypeError(f"Argument {key} must be of type {arg_type}.")
if arg_info.get("action") == "store_true" and not isinstance(value, bool):
raise TypeError(f"Argument {key} must be of type bool.")
# Check for iterable to allow input to be set, list, tuple, etc. Also a single string is fine. Note that
# currently all arguments that allow this option take string inputs, so there is not an explicit need to
# check for typing matches:
if arg_info.get("nargs") is not None:
if not isinstance(value, Iterable):
raise TypeError(f"Argument {key} must be an iterable.")
element_type = type(next(iter(value)))
if not all(isinstance(element, element_type) for element in value):
raise TypeError(f"Argument {key} must be an iterable containing values of type {element_type}.")
# Initialize parser:
parser = argparse.ArgumentParser(description="MuSIC arguments", allow_abbrev=False)
# Use arg_dict to populate the parser:
for arg, arg_info in arg_dict.items():
parser.add_argument(arg, **arg_info)
# Compile arguments list:
all_args = []
for key, value in kwargs.items():
# Check if key is valid:
if key not in arg_dict.keys():
logger.info(
f"Argument {key} not recognized and will be skipped and not included in the final processed " f"list."
)
continue
all_args.append(key)
# If Boolean, don't do anything- giving the argument will store it as True and so appending the key is enough:
if isinstance(value, bool):
continue
# Check for arguments that allow multiple inputs:
arg_info = arg_dict[key]
if arg_info.get("nargs") is not None:
if isinstance(value, (List, Tuple, Set)):
all_args.extend(map(str, value))
all_args.append(str(value))
return parser, all_args
if __name__ == "__main__":
# Run from the command line:
[docs] parser = argparse.ArgumentParser(description="MuSIC arguments", allow_abbrev=False)
parser.add_argument("-run_upstream", action="store_true")
parser.add_argument("-adata_path", type=str)
parser.add_argument(
"-csv_path",
type=str,
help="Can be used to provide a .csv file, containing gene expression data or any other kind of data. "
"Assumes the first three columns contain x- and y-coordinates and then dependent variable "
"values, in that order.",
)
parser.add_argument(
"-n_spatial_dim_csv",
type=int,
default=2,
help="If using a .csv file, specifies the number of spatial dimensions. Default is 2.",
)
parser.add_argument(
"-spatial_subsample",
action="store_true",
help="Recommended for large datasets (>5000 samples), otherwise model fitting is quite slow.",
)
parser.add_argument("-mod_type", type=str, default="niche")
parser.add_argument("-include_unpaired_lr", action="store_true")
parser.add_argument("-cci_dir", type=str)
parser.add_argument("-species", type=str, default="human")
parser.add_argument(
"-output_path",
default="./output/stgwr_results.csv",
type=str,
help="Path to output file. Make sure the parent directory is empty- "
"any existing files will be deleted. It is recommended to create "
"a new folder to serve as the output directory. This should be "
"supplied of the form '/path/to/file.csv', where file.csv will "
"store coefficients. The name of the target will be appended at runtime.",
)
parser.add_argument("-custom_lig_path", type=str)
parser.add_argument(
"-ligand",
nargs="+",
type=str,
help="Alternative to the custom ligand path, can be used to provide a custom list of ligands.",
)
parser.add_argument("-custom_rec_path", type=str)
parser.add_argument(
"-receptor",
nargs="+",
type=str,
help="Alternative to the custom receptor path, can be used to provide a custom list of receptors.",
)
parser.add_argument("-custom_pathways_path", type=str)
parser.add_argument(
"-pathway",
nargs="+",
type=str,
help="Alternative to the custom pathway path, can be used to provide a custom list of pathways.",
)
parser.add_argument("-targets_path", type=str)
parser.add_argument(
"-target",
nargs="+",
type=str,
help="Alternative to the custom target path, can be used to provide a custom list of target molecules.",
)
parser.add_argument("-init_betas_path", type=str)
parser.add_argument("-normalize", action="store_true")
parser.add_argument("-smooth", action="store_true")
parser.add_argument("-log_transform", action="store_true")
parser.add_argument(
"-normalize_signaling",
action="store_true",
help="For ligand, receptor or L:R models, "
"normalize computed signaling values. This should be used to find signaling effects that may be mediated by "
"rarer signals.",
)
parser.add_argument(
"-target_expr_threshold",
default=0.05,
type=float,
help="For automated selection, the threshold proportion of cells for which transcript "
"needs to be expressed in to be selected as a target of interest. Not used if 'targets_path' is not None.",
)
parser.add_argument(
"-multicollinear_threshold",
type=float,
help="Used only if `mod_type` is 'slice'. If this argument is provided, independent variables that are highly "
"correlated will be filtered out based on variance inflation factor threshold. A value of 5 or 10 is "
"recommended. This can be useful in reducing computation time.",
)
parser.add_argument("-coords_key", default="spatial", type=str)
parser.add_argument(
"-group_key",
default="cell_type",
type=str,
help="Key to entry in .obs containing cell type or other category labels.",
)
parser.add_argument(
"-group_subset",
nargs="+",
type=str,
help="If provided, only cells with labels that correspond to these group(s) will be used as prediction "
"targets. Will search in key corresponding to the input to arg 'cell_type' if given.",
)
parser.add_argument(
"-covariate_keys",
nargs="+",
type=str,
help="Any number of keys to entry in .obs or .var_names of an "
"AnnData object. Values here will be added to"
"the model as covariates.",
)
parser.add_argument(
"-total_counts_key",
default="total_counts",
type=str,
help="Key to entry in .obs containing total counts per cell.",
)
parser.add_argument(
"-total_counts_threshold",
default=0.0,
type=float,
help="If provided, cells with total counts below this threshold will be removed.",
)
parser.add_argument("-bw")
parser.add_argument("-minbw")
parser.add_argument("-maxbw")
parser.add_argument(
"-bw_fixed",
action="store_true",
help="If this argument is provided, the bandwidth will be "
"interpreted as a distance during kernel operations. If not, it will be interpreted "
"as the number of nearest neighbors.",
)
parser.add_argument(
"-exclude_self",
action="store_true",
help="When computing spatial weights, do not count the "
"cell itself as a neighbor. Recommended to set to "
"True for the CCI models because the independent "
"variable array is also spatially-dependent.",
)
parser.add_argument(
"-kernel",
default="bisquare",
type=str,
help="Kernel to use when computing spatial weights and fitting the model.",
)
parser.add_argument(
"-distance_membrane_bound",
type=float,
help="In model setup, distance threshold to consider cells as neighbors for membrane-bound ligands. If"
"provided, will take priority over 'n_neighbors_membrane_bound'.",
)
parser.add_argument(
"-distance_secreted",
type=float,
help="In model setup, distance threshold to consider cells as neighbors for secreted or ECM ligands. If"
"provided, will take priority over 'n_neighbors_secreted'.",
)
parser.add_argument(
"-n_neighbors_membrane_bound",
default=8,
type=int,
help="Only used if `mod_type` is 'niche', to define the number of neighbors "
"to consider for each cell when defining the independent variable array for "
"membrane-bound ligands.",
)
parser.add_argument(
"-n_neighbors_secreted",
default=25,
type=int,
help="Only used if `mod_type` is 'niche', to define the number of neighbors "
"to consider for each cell when defining the independent variable array for "
"secreted or ECM ligands.",
)
parser.add_argument(
"-use_expression_neighbors",
action="store_true",
help="The default for finding spatial neighborhoods for the modeling process is to use neighbors in "
"physical space. If this argument is provided, expression will be used instead to find neighbors.",
)
parser.add_argument("-distr", default="gaussian", type=str)
parser.add_argument("-fit_intercept", action="store_true")
# parser.add_argument("-include_offset", action="store_true")
parser.add_argument(
"-no_hurdle",
action="store_true",
help="If True, do not implement spatially-weighted hurdle model- will only perform generalized linear "
"modeling.",
)
parser.add_argument("-tolerance", default=1e-3, type=float)
parser.add_argument("-max_iter", default=500, type=int)
parser.add_argument("-patience", default=5, type=int)
parser.add_argument("-ridge_lambda", default=0.3, type=float)
parser.add_argument(
"-chunks",
default=1,
type=int,
help="For use if `multiscale` is True- increase the number of parallel processes. Can be used to help prevent"
"memory from running out, otherwise keep as low as possible.",
)
# Options for downstream analysis:
parser.add_argument(
"-search_bw",
help="Used for downstream analyses; specifies the bandwidth to search for "
"senders/receivers. Recommended to set equal to the bandwidth of a fitted "
"model.",
)
parser.add_argument(
"-top_k_receivers",
default=10,
type=int,
help="Used for downstream analyses, specifically for :func `define_effect_vf`; specifies the number of "
"nearest neighbors to consider when computing signaling effect vectors.",
)
parser.add_argument(
"-filter_targets",
action="store_true",
help="Used for downstream analyses, specifically :func `infer_effect_direction`; if True, will subset to only "
"the targets that were predicted well by the model.",
)
parser.add_argument(
"-filter_target_threshold",
default=0.65,
type=float,
help="Used for downstream analyses, specifically :func `infer_effect_direction`; specifies the threshold "
"Pearson coefficient for target subsetting. Only used if `filter_targets` is True.",
)
parser.add_argument(
"-diff_sending_or_receiving",
default="sending",
type=str,
help="Used for downstream analyses, specifically :func `sender_receiver_effect_deg_detection`; specifies "
"whether to compute differential expression of genes in cells with high or low sending effect potential "
"('sending cells') or high or low receiving effect potential ('receiving cells').",
)
parser.add_argument(
"-target_for_downstream",
nargs="+",
type=str,
help="Used for :func `get_effect_potential`, :func `get_pathway_potential` and :func "
"`calc_and_group_sender_receiver_effect_degs` (provide only one target), as well as :func "
"`compute_cell_type_coupling` (can provide multiple targets). Used to specify the target "
"gene(s) to analyze with these functions.",
)
parser.add_argument(
"-ligand_for_downstream",
type=str,
help="Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the ligand gene to consider with respect to the target.",
)
parser.add_argument(
"-receptor_for_downstream",
type=str,
help="Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the receptor gene to consider with respect to the target.",
)
parser.add_argument(
"-pathway_for_downstream",
type=str,
help="Used for :func `get_pathway_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the pathway to consider with respect to the target.",
)
parser.add_argument(
"-sender_ct_for_downstream",
type=str,
help="Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the cell type to consider as a sender.",
)
parser.add_argument(
"-receiver_ct_for_downstream",
type=str,
help="Used for :func `get_effect_potential` and :func `calc_and_group_sender_receiver_effect_degs`, "
"used to specify the cell type to consider as a receiver.",
)
parser.add_argument(
"-n_components",
type=int,
help="Used for :func `CCI_sender_deg_detection` and :func `CCI_receiver_deg_detection`; determines the "
"dimensionality of the space to embed into using UMAP.",
)
parser.add_argument(
"-cci_degs_model_interactions",
type=bool,
help="Used for :func `CCI_sender_deg_detection`; if True, will consider transcription factor "
"interactions with cofactors and other transcription factors. If False, will use only "
"transcription factor expression for prediction.",
)
parser.add_argument(
"-no_cell_type_markers",
action="store_true",
help="Used for :func `CCI_receiver_deg_detection`; if True, will exclude cell type markers "
"from the set of genes for which to compare to sent/received signal.",
)
parser.add_argument(
"-compute_pathway_effect",
action="store_true",
help="Used for :func `inferred_effect_direction`; if True, will summarize the effects of all "
"ligands/ligand-receptor interactions in a pathway.",
)
t1 = time.time()
swr_model = MuSIC(parser)
swr_model._set_up_model()
swr_model.fit()
swr_model.predict_and_save()
t_last = time.time()
print("Total Time Elapsed:", np.round(t_last - t1, 2), "seconds")
print("-" * 60)