def execute(self):
kwargs = {
"n_jobs":
int(self.args["--threads"]),
"processed":
self.args["--processed"],
"channel_attr":
self.args["--channel"],
"black_list":
self.args["--black-list"],
"subcluster":
False,
"select_singlets":
self.args["--select-singlets"],
"remap_singlets":
self.args["--remap-singlets"],
"subset_singlets":
self.args["--subset-singlets"],
"genome":
self.args["--genome"],
"focus":
self.split_string(self.args["--focus"]),
"append":
self.args["--append"],
"output_h5ad":
self.args["--output-h5ad"],
"output_loom":
self.args["--output-loom"],
"min_genes":
self.convert_to_int(self.args["--min-genes"]),
"max_genes":
self.convert_to_int(self.args["--max-genes"]),
"min_umis":
self.convert_to_int(self.args["--min-umis"]),
"max_umis":
self.convert_to_int(self.args["--max-umis"]),
"mito_prefix":
self.args["--mito-prefix"],
"percent_mito":
self.convert_to_float(self.args["--percent-mito"]),
"percent_cells":
float(self.args["--gene-percent-cells"]),
"output_filt":
self.args["<output_name>"]
if self.args["--output-filtration-results"] else None,
"plot_filt":
self.args["<output_name>"]
if self.args["--plot-filtration-results"] else None,
"plot_filt_figsize":
self.args["--plot-filtration-figsize"],
"min_genes_before_filt":
int(self.args["--min-genes-before-filtration"]),
"norm_count":
float(self.args["--counts-per-cell-after"]),
"select_hvf":
not self.args["--no-select-hvf"],
"hvf_flavor":
self.args["--select-hvf-flavor"],
"hvf_ngenes":
int(self.args["--select-hvf-ngenes"])
if self.args["--select-hvf-ngenes"] != "None" else None,
"plot_hvf":
self.args["<output_name>"] if self.args["--plot-hvf"] else None,
"batch_correction":
self.args["--correct-batch-effect"],
"correction_method":
self.args["--correction-method"],
"group_attribute":
self.args["--batch-group-by"],
"harmony_nclusters":
self.convert_to_int(self.args["--harmony-nclusters"]),
"random_state":
int(self.args["--random-state"]),
"temp_folder":
self.args["--temp-folder"],
"calc_sigscore":
self.args["--calc-signature-scores"],
"pca_n":
int(self.args["--pca-n"]),
"K":
int(self.args["--knn-K"]),
"full_speed":
self.args["--knn-full-speed"],
"kBET":
self.args["--kBET"],
"kBET_batch":
self.args["--kBET-batch"],
"kBET_alpha":
float(self.args["--kBET-alpha"]),
"kBET_K":
int(self.args["--kBET-K"]),
"diffmap":
self.args["--diffmap"],
"diffmap_ndc":
int(self.args["--diffmap-ndc"]),
"diffmap_maxt":
int(self.args["--diffmap-maxt"]),
"diffmap_solver":
self.args["--diffmap-solver"],
"pseudotime":
self.split_string(self.args["--calculate-pseudotime"]),
"louvain":
self.args["--louvain"],
"louvain_resolution":
float(self.args["--louvain-resolution"]),
"louvain_class_label":
self.args["--louvain-class-label"],
"leiden":
self.args["--leiden"],
"leiden_resolution":
float(self.args["--leiden-resolution"]),
"leiden_niter":
int(self.args["--leiden-niter"]),
"leiden_class_label":
self.args["--leiden-class-label"],
"spectral_louvain":
self.args["--spectral-louvain"],
"spectral_louvain_basis":
self.args["--spectral-louvain-basis"],
"spectral_louvain_nclusters":
int(self.args["--spectral-louvain-nclusters"]),
"spectral_louvain_nclusters2":
int(self.args["--spectral-louvain-nclusters2"]),
"spectral_louvain_ninit":
int(self.args["--spectral-louvain-ninit"]),
"spectral_louvain_resolution":
float(self.args["--spectral-louvain-resolution"]),
"spectral_louvain_class_label":
self.args["--spectral-louvain-class-label"],
"spectral_leiden":
self.args["--spectral-leiden"],
"spectral_leiden_basis":
self.args["--spectral-leiden-basis"],
"spectral_leiden_nclusters":
int(self.args["--spectral-leiden-nclusters"]),
"spectral_leiden_nclusters2":
int(self.args["--spectral-leiden-nclusters2"]),
"spectral_leiden_ninit":
int(self.args["--spectral-leiden-ninit"]),
"spectral_leiden_resolution":
float(self.args["--spectral-leiden-resolution"]),
"spectral_leiden_class_label":
self.args["--spectral-leiden-class-label"],
"tsne":
self.args["--tsne"],
"tsne_perplexity":
float(self.args["--tsne-perplexity"]),
"tsne_init":
self.args["--tsne-initialization"],
"umap":
self.args["--umap"],
"umap_K":
int(self.args["--umap-K"]),
"umap_min_dist":
float(self.args["--umap-min-dist"]),
"umap_spread":
float(self.args["--umap-spread"]),
"fle":
self.args["--fle"],
"fle_K":
int(self.args["--fle-K"]),
"fle_target_change_per_node":
float(self.args["--fle-target-change-per-node"]),
"fle_target_steps":
int(self.args["--fle-target-steps"]),
"fle_memory":
int(self.args["--fle-memory"]),
"net_ds_frac":
float(self.args["--net-down-sample-fraction"]),
"net_ds_K":
int(self.args["--net-down-sample-K"]),
"net_ds_alpha":
float(self.args["--net-down-sample-alpha"]),
"net_l2":
float(self.args["--net-regressor-L2-penalty"]),
"net_umap":
self.args["--net-umap"],
"net_umap_polish_learing_rate":
float(self.args["--net-umap-polish-learning-rate"]),
"net_umap_polish_nepochs":
int(self.args["--net-umap-polish-nepochs"]),
"net_umap_basis":
self.args["--net-umap-out-basis"],
"net_fle":
self.args["--net-fle"],
"net_fle_polish_target_steps":
int(self.args["--net-fle-polish-target-steps"]),
"net_fle_basis":
self.args["--net-fle-out-basis"],
"infer_doublets":
self.args["--infer-doublets"],
"expected_doublet_rate":
self.convert_to_float(self.args["--expected-doublet-rate"]),
"dbl_cluster_attr":
self.args["--dbl-cluster-attr"],
"citeseq":
self.args["--citeseq"],
"citeseq_umap":
self.args["--citeseq-umap"],
"citeseq_umap_exclude":
self.split_string(self.args["--citeseq-umap-exclude"]),
}
import logging
logger = logging.getLogger("pegasus")
fh = logging.FileHandler("{}.log".format(self.args["<output_name>"]))
fh.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
logger.addHandler(fh)
run_pipeline(self.args["<input_file>"], self.args["<output_name>"],
**kwargs)
def execute(self):
kwargs = {
"processed":
True,
"subcluster":
True,
"cite_seq":
False,
"select_singlets":
False,
"subset_selections":
self.args["--subset-selection"],
"n_jobs":
int(self.args["--threads"]),
"genome":
None,
"channel_attr":
None,
"black_list":
None,
"batch_correction":
self.args["--correct-batch-effect"],
"group_attribute":
self.args["--batch-group-by"],
"output_loom":
self.args["--output-loom"],
"select_hvf":
not self.args["--no-select-hvf"],
"hvf_flavor":
self.args["--select-hvf-flavor"],
"hvf_ngenes":
int(self.args["--select-hvf-ngenes"])
if self.args["--select-hvf-ngenes"] != "None" else None,
"plot_hvf":
self.args["<output_name>"] if self.args["--plot-hvf"] else None,
"random_state":
int(self.args["--random-state"]),
"temp_folder":
self.args["--temp-folder"],
"nPC":
int(self.args["--nPC"]),
"K":
int(self.args["--knn-K"]),
"full_speed":
self.args["--knn-full-speed"],
"kBET":
self.args["--kBET"],
"kBET_batch":
self.args["--kBET-batch"],
"kBET_alpha":
float(self.args["--kBET-alpha"]),
"kBET_K":
int(self.args["--kBET-K"]),
"diffmap":
self.args["--diffmap"],
"diffmap_ndc":
int(self.args["--diffmap-ndc"]),
"diffmap_maxt":
int(self.args["--diffmap-maxt"]),
"diffmap_solver":
self.args["--diffmap-solver"],
"diffmap_to_3d":
self.args["--diffmap-to-3d"],
"pseudotime":
self.split_string(self.args["--calculate-pseudotime"]),
"louvain":
self.args["--louvain"],
"louvain_resolution":
float(self.args["--louvain-resolution"]),
"louvain_class_label":
self.args["--louvain-class-label"],
"leiden":
self.args["--leiden"],
"leiden_resolution":
float(self.args["--leiden-resolution"]),
"leiden_niter":
int(self.args["--leiden-niter"]),
"leiden_class_label":
self.args["--leiden-class-label"],
"spectral_louvain":
self.args["--spectral-louvain"],
"spectral_louvain_basis":
self.args["--spectral-louvain-basis"],
"spectral_louvain_nclusters":
int(self.args["--spectral-louvain-nclusters"]),
"spectral_louvain_ninit":
int(self.args["--spectral-louvain-ninit"]),
"spectral_louvain_resolution":
float(self.args["--spectral-louvain-resolution"]),
"spectral_louvain_class_label":
self.args["--spectral-louvain-class-label"],
"spectral_leiden":
self.args["--spectral-leiden"],
"spectral_leiden_basis":
self.args["--spectral-leiden-basis"],
"spectral_leiden_nclusters":
int(self.args["--spectral-leiden-nclusters"]),
"spectral_leiden_ninit":
int(self.args["--spectral-leiden-ninit"]),
"spectral_leiden_resolution":
float(self.args["--spectral-leiden-resolution"]),
"spectral_leiden_class_label":
self.args["--spectral-leiden-class-label"],
"tsne":
self.args["--tsne"],
"fitsne":
self.args["--fitsne"],
"tsne_perplexity":
float(self.args["--tsne-perplexity"]),
"umap":
self.args["--umap"],
"umap_K":
int(self.args["--umap-K"]),
"umap_min_dist":
float(self.args["--umap-min-dist"]),
"umap_spread":
float(self.args["--umap-spread"]),
"fle":
self.args["--fle"],
"fle_K":
int(self.args["--fle-K"]),
"fle_target_change_per_node":
float(self.args["--fle-target-change-per-node"]),
"fle_target_steps":
int(self.args["--fle-target-steps"]),
"fle_memory":
int(self.args["--fle-memory"]),
"net_ds_frac":
float(self.args["--net-down-sample-fraction"]),
"net_ds_K":
int(self.args["--net-down-sample-K"]),
"net_ds_alpha":
float(self.args["--net-down-sample-alpha"]),
"net_l2":
float(self.args["--net-regressor-L2-penalty"]),
"net_tsne":
self.args["--net-tsne"],
"net_tsne_polish_learing_frac":
float(self.args["--net-tsne-polish-learning-frac"]),
"net_tsne_polish_niter":
int(self.args["--net-tsne-polish-niter"]),
"net_tsne_basis":
self.args["--net-tsne-out-basis"],
"net_umap":
self.args["--net-umap"],
"net_umap_polish_learing_rate":
float(self.args["--net-umap-polish-learning-rate"]),
"net_umap_polish_nepochs":
int(self.args["--net-umap-polish-nepochs"]),
"net_umap_basis":
self.args["--net-umap-out-basis"],
"net_fle":
self.args["--net-fle"],
"net_fle_polish_target_steps":
int(self.args["--net-fle-polish-target-steps"]),
"net_fle_basis":
self.args["--net-fle-out-basis"],
}
import logging
logger = logging.getLogger("pegasus")
fh = logging.FileHandler("{}.log".format(self.args["<output_name>"]))
fh.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
fh.setFormatter(formatter)
logger.addHandler(fh)
run_pipeline(self.args["<input_file>"], self.args["<output_name>"],
**kwargs)
def execute(self):
kwargs = {
"n_jobs": int(self.args["--threads"]),
"processed": self.args["--processed"],
"genome": self.args["--genome"],
"channel_attr": self.args["--channel"],
"black_list": self.args["--black-list"],
"subcluster": False,
"min_genes_on_raw": int(self.args["--min-genes-on-raw"]),
"select_singlets": self.args["--select-singlets"],
"cite_seq": self.args["--cite-seq"],
"cite_seq_capping": float(self.args["--cite-seq-capping"]),
"output_filt": self.args["<output_name>"]
if self.args["--output-filtration-results"]
else None,
"plot_filt": self.args["<output_name>"]
if self.args["--plot-filtration-results"]
else None,
"plot_filt_figsize": self.args["--plot-filtration-figsize"],
"seurat_compatible": self.args["--output-seurat-compatible"],
"output_loom": self.args["--output-loom"],
"min_genes": int(self.args["--min-genes"]),
"max_genes": int(self.args["--max-genes"]),
"min_umis": int(self.args["--min-umis"]),
"max_umis": int(self.args["--max-umis"]),
"mito_prefix": self.args["--mito-prefix"],
"percent_mito": float(self.args["--percent-mito"]),
"percent_cells": float(self.args["--gene-percent-cells"]),
"norm_count": float(self.args["--counts-per-cell-after"]),
"select_hvf": not self.args["--no-select-hvf"],
"hvf_flavor": self.args["--select-hvf-flavor"],
"hvf_ngenes": int(self.args["--select-hvf-ngenes"])
if self.args["--select-hvf-ngenes"] != "None"
else None,
"plot_hvf": self.args["<output_name>"] if self.args["--plot-hvf"] else None,
"batch_correction": self.args["--correct-batch-effect"],
"group_attribute": self.args["--batch-group-by"],
"random_state": int(self.args["--random-state"]),
"temp_folder": self.args["--temp-folder"],
"nPC": int(self.args["--nPC"]),
"K": int(self.args["--knn-K"]),
"full_speed": self.args["--knn-full-speed"],
"kBET": self.args["--kBET"],
"kBET_batch": self.args["--kBET-batch"],
"kBET_alpha": float(self.args["--kBET-alpha"]),
"kBET_K": int(self.args["--kBET-K"]),
"diffmap": self.args["--diffmap"],
"diffmap_ndc": int(self.args["--diffmap-ndc"]),
"diffmap_maxt": int(self.args["--diffmap-maxt"]),
"diffmap_solver": self.args["--diffmap-solver"],
"diffmap_to_3d": self.args["--diffmap-to-3d"],
"pseudotime": self.split_string(self.args["--calculate-pseudotime"]),
"louvain": self.args["--louvain"],
"louvain_resolution": float(self.args["--louvain-resolution"]),
"louvain_class_label": self.args["--louvain-class-label"],
"leiden": self.args["--leiden"],
"leiden_resolution": float(self.args["--leiden-resolution"]),
"leiden_niter": int(self.args["--leiden-niter"]),
"leiden_class_label": self.args["--leiden-class-label"],
"spectral_louvain": self.args["--spectral-louvain"],
"spectral_louvain_basis": self.args["--spectral-louvain-basis"],
"spectral_louvain_nclusters": int(
self.args["--spectral-louvain-nclusters"]
),
"spectral_louvain_nclusters2": int(
self.args["--spectral-louvain-nclusters2"]
),
"spectral_louvain_ninit": int(self.args["--spectral-louvain-ninit"]),
"spectral_louvain_resolution": float(
self.args["--spectral-louvain-resolution"]
),
"spectral_louvain_class_label": self.args["--spectral-louvain-class-label"],
"spectral_leiden": self.args["--spectral-leiden"],
"spectral_leiden_basis": self.args["--spectral-leiden-basis"],
"spectral_leiden_nclusters": int(self.args["--spectral-leiden-nclusters"]),
"spectral_leiden_nclusters2": int(self.args["--spectral-leiden-nclusters2"]),
"spectral_leiden_ninit": int(self.args["--spectral-leiden-ninit"]),
"spectral_leiden_resolution": float(
self.args["--spectral-leiden-resolution"]
),
"spectral_leiden_class_label": self.args["--spectral-leiden-class-label"],
"tsne": self.args["--tsne"],
"fitsne": self.args["--fitsne"],
"tsne_perplexity": float(self.args["--tsne-perplexity"]),
"umap": self.args["--umap"],
"umap_K": int(self.args["--umap-K"]),
"umap_min_dist": float(self.args["--umap-min-dist"]),
"umap_spread": float(self.args["--umap-spread"]),
"fle": self.args["--fle"],
"fle_K": int(self.args["--fle-K"]),
"fle_target_change_per_node": float(
self.args["--fle-target-change-per-node"]
),
"fle_target_steps": int(self.args["--fle-target-steps"]),
"fle_memory": int(self.args["--fle-memory"]),
"net_ds_frac": float(self.args["--net-down-sample-fraction"]),
"net_ds_K": int(self.args["--net-down-sample-K"]),
"net_ds_alpha": float(self.args["--net-down-sample-alpha"]),
"net_l2": float(self.args["--net-regressor-L2-penalty"]),
"net_tsne": self.args["--net-tsne"],
"net_tsne_polish_learing_frac": float(
self.args["--net-tsne-polish-learning-frac"]
),
"net_tsne_polish_niter": int(self.args["--net-tsne-polish-niter"]),
"net_tsne_basis": self.args["--net-tsne-out-basis"],
"net_umap": self.args["--net-umap"],
"net_umap_polish_learing_rate": float(
self.args["--net-umap-polish-learning-rate"]
),
"net_umap_polish_nepochs": int(self.args["--net-umap-polish-nepochs"]),
"net_umap_basis": self.args["--net-umap-out-basis"],
"net_fle": self.args["--net-fle"],
"net_fle_polish_target_steps": int(
self.args["--net-fle-polish-target-steps"]
),
"net_fle_basis": self.args["--net-fle-out-basis"],
}
run_pipeline(self.args["<input_file>"], self.args["<output_name>"], **kwargs)