Forebrain

Tutorial on Forebrain dataset

Installation

install from GitHub

git clone git://github.com/jsxlei/SCALE.git
cd SCALE
python setup.py install

Get started with downloaded scATAC-seq data Forebrain [Download]

Run SCALE (command line)

SCALE.py -d Forebrain/data.txt -k 8 --impute

or

SCALE.py -d Forebrain/data.txt -k 8 --binary

input_dir is Forebrain
all results are saved in default output dir output/

Results (python environment)

Load required packages

import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
from matplotlib import pyplot as plt
import seaborn as sns

from scale.plot import plot_embedding, plot_heatmap

visualization

t-SNE embedding is saved in tsne.txt and tsne.pdf labeled by cluster assignments.

clustering results

clustering results are saved in cluster_assignments.txt

y = pd.read_csv('output/cluster_assignments.txt', sep='\t', index_col=0, header=None)[1].values

feature

latent feature are saved in feature.txt, we can plot this feature:

feature = pd.read_csv('output/feature.txt', sep='\t', index_col=0, header=None)
plot_heatmap(feature.T, y, 
             figsize=(8, 3), cmap='RdBu_r', vmax=8, vmin=-8, center=0,
             ylabel='Feature dimension', yticklabels=np.arange(10)+1, 
             cax_title='Feature value', legend_font=6, ncol=1,
             bbox_to_anchor=(1.1, 1.1), position=(0.92, 0.15, .08, .04))

imputation

imputed data are saved in imputed_data.txt

imputed = pd.read_csv('output/imputed_data.txt', sep='\t', index_col=0)

or binary_imputed folder

from scale.dataset import *
count, peak, barcode = read_mtx('output/binary_imputed')
imputed = pd.DataFrame(count.toarray().T, index=peak, columns=barcode)

imputed results improved identification of motifs by chromVAR.
left figure is the deviations score of significant motifs(adj_p_value of variability < 0.05).
right figure is the t-SNE plot using the motifs heatmap.

cluster-specific peaks

We provide an entropy-based method tto calculate cluster specificity for each peak across cluters.

from scale.specifity import cluster_specific, mat_specificity_score

score_mat = mat_specificity_score(imputed, y)
peak_index, peak_labels = cluster_specific(score_mat, np.unique(y), top=200)

plot_heatmap(imputed.iloc[peak_index], y=y, row_labels=peak_labels, ncol=3, cmap='Reds', 
             vmax=1, row_cluster=False, legend_font=6, cax_title='Peak Value',
             figsize=(8, 10), bbox_to_anchor=(0.4, 1.2), position=(0.8, 0.76, 0.1, 0.015))