The SCTC Python package provides tools for calculating Single-Cell Transcriptional Complexity (SCTC) from scRNA-seq data. This metric helps to characterize cell developmental potential and infer single-cell pseudotime.
You can install the SCTC package using pip:
The complexity_index function computes the Cell Complexity Index (CCI) and Gene Complexity Index (GCI) for a given single-cell transcriptome dataset.
import sctc # Load your scRNA-seq gene expression matrix as a numpy array 'mcg' # mcg should have cells as rows and genes as columns cci, gci = sctc.complexity_index(mcg)
cci: CCI for each cell (normalized between 0 and 1).gci: GCI for each gene (normalized between 0 and 1).The complexity_order function calculates the Nth-complexity of cells and genes.
import sctc # Load your scRNA-seq gene expression matrix as a numpy array 'mcg' # mcg should have cells as rows and genes as columns kc, kg = sctc.complexity_order(mcg, nmax=30)
kc: Nth-complexity for each cell with N ranges from 0 to nmax (normalized between 0 and 1).kg: Nth-complexity for each gene with N ranges from 0 to nmax (normalized between 0 and 1).The convert_to_ranking function convert a 2D complexity array into a 2D ranking array.
The ranking_plot function visualize the rankings.
import sctc # Load a 2D array of complexities. ranking_lists = convert_to_ranking(complexity_array) fig, ax = ranking_plot(ranking_lists)
fig: matplotlib.figure.Figure.ax: matplotlib.axes._subplots.AxesSubplot.The gene_proximity function calculates gene proximity based on the gene expression matrix.
The gene_space function constructs a gene space network using a spanning tree approach.
import sctc # Load your scRNA-seq gene expression matrix as a numpy array 'mcg' # mcg should have cells as rows and genes as columns gene_proxim = sctc.gene_proximity(mcg) gene_space = sctc.gene_space(gene_proxim)
gene_space: An igraph object representing the maximum spanning tree.import scanpy as sc
import numpy as np
import igraph as ig
import sctc
import matplotlib.pyplot as plt
# Load scRNA-seq data and ensure the count matrix is a numpy array
adata = sc.read_h5ad('./data/hnd.h5ad')
if not isinstance(adata.X, np.ndarray):
adata.X = adata.X.toarray()
# Preprocessing
sc.pp.filter_cells(adata, min_genes=1)
sc.pp.filter_genes(adata, min_cells=1)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
# Calculate Cell Complexity Index (CCI) and Gene Complexity Index (GCI)
cci, gci = sctc.complexity_index(adata.X)
# Calculate Nth-order complexity of cells (kcn) and genes (kgn)
kcn, gcn = sctc.complexity_order(adata.X)
# Choosing a subset of cells for visualization of cell ranking
n_choice = 50
choice = np.random.choice(kcn.shape[1], n_choice, replace=False)
choice_kc = kcn[:, choice]
choice_cci = cci[choice]
# Converting complexity scores to rankings
N_list = list(range(0, 16, 2))
complexity_array = choice_kc[N_list]
complexity_array = np.vstack([complexity_array, choice_cci])
complexity_array = np.transpose(complexity_array)
ranking = sctc.convert_to_ranking(complexity_array)
# Creating a color map
cmap = plt.cm.get_cmap('Spectral', len(set(adata.obs['Day'])))
cmap = [cmap(i) for i in range(len(set(adata.obs['Day'])))]
cmap = dict(zip(adata.obs['Day'].unique(), cmap))
colors = [cmap[day] for day in adata.obs['Day'][choice]]
# Plotting the ranking
fig, ax = sctc.ranking_plot(ranking, colors, marker_size=20, line_width=2)
fig.set_size_inches(7, 19)
xticks = []
xticks.extend([str(i) for i in N_list])
xticks.append('CCI')
ax.set_xticks(range(len(xticks)))
ax.set_xticklabels(xticks)
ax.set_xlabel('N')
ax.set_ylabel('Index')
plt.savefig('./results/ranking.png')
# Selecting a subset of genes for visualization
selected_indices = np.random.choice(adata.n_vars, 1500, replace=False)
adata = adata[:, selected_indices].copy()
# Creating a gene space network
gene_proxim = sctc.gene_proximity(adata.X)
gene_space = sctc.gene_space(gene_proxim)
layout = gene_space.layout_fruchterman_reingold()
gene_space.vs['size'] = 8
gene_space.vs['color'] = 'green'
# Plotting the gene space network
plot = ig.plot(gene_space, layout=layout)
plot.save('./results/gene_space.png')
For more examples, please refer to the tutorials
Hai Lin, Huan Hu, Zhen Feng, Fei Xu, Jie Lyu, Xiang Li, Liyu Liu, Gen Yang, Jianwei Shuai, SCTC: inference of developmental potential from single-cell transcriptional complexity, Nucleic Acids Research, Volume 52, Issue 11, 24 June 2024, Pages 6114–6128, https://doi.org/10.1093/nar/gkae340
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