Construction of developmental tree from single-cell RNA-seq data using StagewiseNN

API

Import stagewiseNN as:

import swnn

Object for Management

See swnn.builder module for detailed information.

Builder(stage_order)

The wrapper object for conveniently building both the single-cell graph and the coarse-grained tree.

Data Processing

quick_preprocess_raw(adata[, target_sum, …])

Go through the data-analysis pipeline, including normalization, HVG selection, and z-scoring (centering and scaling)

normalize_default(adata[, target_sum, copy, …])

Normalizing datasets with default settings (total-counts normalization followed by log(x+1) transform).

groupwise_hvgs_freq(adata[, groupby, …])

Separately compute highly variable genes (HVGs) for each group, and count the frequencies of genes being selected as HVGs among those groups.

set_adata_hvgs(adata[, gene_list, …])

Setting the given (may be pre-computed) set of genes as highly variable, if copy is False, changes will be made to the input adata.

group_mean_adata(adata, groupby[, features, …])

Compute averaged feature-values for each group

wrapper_scale(adata[, zero_center, …])

Wrapper function for centering and scaling data matrix X in sc.AnnData, extended for within-batch cprocessing.

Make Graph

stagewise_knn(X, stage_lbs, stage_order[, …])

Build multipartite KNN-graph stage-by-stage.

adaptive_tree(adj_max, group_lbs[, …])

Adaptatively build the developmental tree from the stagewise-KNN graph.

Others

check_dirs(path)

set_precomputed_neighbors(adata, distances, …)

describe_dataframe(df, **kwargs)