API

Pegasus can also be used as a python package. Import pegasus by:

import pegasus as pg

Read and Write

read_input(input_file[, file_type, mode, …])	Load data into memory.
write_output(data, output_file[, file_type, …])	Write data back to disk.
aggregate_matrices(csv_file[, restrictions, …])	Aggregate channel-specific count matrices into one big count matrix.

Analysis Tools

Preprocess

qc_metrics(data[, select_singlets, …])	Generate Quality Control (QC) metrics regarding cell barcodes on the dataset.
get_filter_stats(data[, min_genes_before_filt])	Calculate filtration stats on cell barcodes.
filter_data(data[, focus_list])	Filter data based on qc_metrics calculated in pg.qc_metrics.
identify_robust_genes(data[, percent_cells])	Identify robust genes as candidates for HVG selection and remove genes that are not expressed in any cells.
log_norm(data[, norm_count, backup_matrix])	Normalization, and then apply natural logarithm to the data.
highly_variable_features(data[, …])	Highly variable features (HVF) selection.
select_features(data[, features, …])	Subset the features and store the resulting matrix in dense format in data.uns with ‘_tmp_fmat_’ prefix, with the option of standardization and truncating based on max_value.
pca(data[, n_components, features, …])	Perform Principle Component Analysis (PCA) to the data.
nmf(data[, n_components, features, space, …])	Perform Nonnegative Matrix Factorization (NMF) to the data using Frobenius norm.
regress_out(data, attrs[, rep])	Regress out effects due to specific observational attributes.

Batch Correction

set_group_attribute(data, attribute_string)	Set group attributes used in batch correction.
correct_batch(data[, features])	Batch correction on data using Location-Scale (L/S) Adjustment method.
run_harmony(data[, batch, rep, n_jobs, …])	Batch correction on PCs using Harmony.
run_scanorama(data[, batch, n_components, …])	Batch correction using Scanorama.
integrative_nmf(data[, batch, n_components, …])	Perform Integrative Nonnegative Matrix Factorization (iNMF) [Yang16] for data integration.

Nearest Neighbors

neighbors(data[, K, rep, n_jobs, …])	Compute k nearest neighbors and affinity matrix, which will be used for diffmap and graph-based community detection algorithms.
get_neighbors(data[, K, rep, n_jobs, …])	Find K nearest neighbors for each data point and return the indices and distances arrays.
calc_kBET(data, attr[, rep, K, alpha, …])	Calculate the kBET metric of the data regarding a specific sample attribute and embedding.
calc_kSIM(data, attr[, rep, K, min_rate, …])	Calculate the kSIM metric of the data regarding a specific sample attribute and embedding.

Diffusion Map

diffmap(data[, n_components, rep, solver, …])	Calculate Diffusion Map.
calc_pseudotime(data, roots)	Calculate Pseudotime based on Diffusion Map.
infer_path(data, cluster, clust_id, path_name)	Inference on path of a cluster.

Cluster Algorithms

cluster(data[, algo, rep, resolution, …])	Cluster the data using the chosen algorithm.
louvain(data[, rep, resolution, n_clust, …])	Cluster the cells using Louvain algorithm.
leiden(data[, rep, resolution, n_clust, …])	Cluster the data using Leiden algorithm.
spectral_louvain(data[, rep, resolution, …])	Cluster the data using Spectral Louvain algorithm.
spectral_leiden(data[, rep, resolution, …])	Cluster the data using Spectral Leiden algorithm.

Visualization Algorithms

tsne(data[, rep, n_jobs, n_components, …])	Calculate t-SNE embedding of cells using the FIt-SNE package.
umap(data[, rep, n_components, n_neighbors, …])	Calculate UMAP embedding of cells.
fle(data[, file_name, n_jobs, rep, K, …])	Construct the Force-directed (FLE) graph.
net_umap(data[, rep, n_jobs, n_components, …])	Calculate Net-UMAP embedding of cells.
net_fle(data[, file_name, n_jobs, rep, K, …])	Construct Net-Force-directed (FLE) graph.

Doublet Detection

infer_doublets(data[, channel_attr, …])	Infer doublets by first calculating Scrublet-like [Wolock18] doublet scores and then smartly determining an appropriate doublet score cutoff [Li20-2] .
mark_doublets(data[, demux_attr, dbl_clusts])	Convert doublet prediction into doublet annotations that Pegasus can recognize.

Gene Module Score

calc_signature_score(data, signatures[, …])

Calculate signature / gene module score.

Differential Expression Analysis

de_analysis(data, cluster[, condition, …])	Perform Differential Expression (DE) Analysis on data.
markers(data[, head, de_key, alpha])	Extract DE results into a human readable structure.
write_results_to_excel(results, output_file)	Write DE analysis results into Excel workbook.

Annotate clusters

infer_cell_types(data, markers[, de_test, …])	Infer putative cell types for each cluster using legacy markers.
annotate(data, name, based_on, anno_dict)	Add annotation to the data object as a categorical variable.

Plotting

scatter(data[, attrs, basis, matkey, …])	Generate scatter plots for different attributes
scatter_groups(data, attr, groupby[, basis, …])	Generate scatter plots of attribute ‘attr’ for each category in attribute ‘group’.
compo_plot(data, groupby, condition[, …])	Generate a composition plot, which shows the percentage of cells from each condition for every cluster.
violin(data, attrs, groupby[, hue, matkey, …])	Generate a stacked violin plot.
heatmap(data, attrs, groupby[, matkey, …])	Generate a heatmap.
dotplot(data, genes, groupby[, …])	Generate a dot plot.
dendrogram(data, groupby[, rep, genes, …])	Generate a dendrogram on hierarchical clustering result.
hvfplot(data[, top_n, panel_size, …])	Generate highly variable feature plot.
qcviolin(data, plot_type[, …])	Plot quality control statistics (before filtration vs.
ridgeplot(data, features[, donor_attr, …])	Generate ridge plots, up to 8 features can be shown in one figure.
volcano(data, cluster_id[, de_key, de_test, …])	Generate Volcano plots (-log10 p value vs.

Demultiplexing

estimate_background_probs(hashing_data[, …])	For cell-hashing data, estimate antibody background probability using KMeans algorithm.
demultiplex(rna_data, hashing_data[, …])	Demultiplexing cell/nucleus-hashing data, using the estimated antibody background probability calculated in demuxEM.estimate_background_probs.
attach_demux_results(input_rna_file, rna_data)	Write demultiplexing results into raw gene expression matrix.

Miscellaneous

search_genes(data, gene_list[, rec_key, measure])	Extract and display gene expressions for each cluster.
search_de_genes(data, gene_list[, rec_key, …])	Extract and display differential expression analysis results of markers for each cluster.
find_outlier_clusters(data, cluster_attr, …)	Using MWU test to detect if any cluster is an outlier regarding one of the qc attributes: n_genes, n_counts, percent_mito.
find_markers(data, label_attr[, de_key, …])	Find markers using gradient boosting method.