renge 0.0.2

Infer gene regulatory networks from time-series single-cell CRISPR data.

RENGE

REgulatory Network inference using GEne perturbation data

RENGE infers gene regulatory networks (GRNs) from time-series single-cell CRISPR analysis data.

Install

pip install renge

renge will be installed within a few minutes.

Usage

Network inference

reg = Renge()
A = reg.estimate_hyperparams_and_fit(X, E)
A_qval = reg.calc_qval()

input
E : C x G pandas DataFrame of expression data. The expression data should be normalized and log-transformed.
X : C x (G+1) pandas DataFrame. The rows of X[:, :G] are one-hot vectors indicating the knocked out gene in each cell. The last column of X indicates the sampling time of each cell.

Here,
C : The number of cells.
G : The number of genes included in the GRN.

output
A : G x G pandas DataFrame. (i, j) element of A is a regulatory coefficient from gene j to gene i.
A_qval : G x G pandas DataFrame. (i, j) element of A_qval is a q-value for (i, j) element of A.

Prediction of expression changes after gene knockout

reg = Renge()
reg.estimate_hyperparams_and_fit(X_train, E_train)   # train the model
E_pred = reg.predict(X_pred)

input
E_train : C x G pandas DataFrame of expression data. The expression data should be normalized and log-transformed.
X_train : C x (G+1) pandas DataFrame. The rows of X_train[:, :G] are one-hot vectors indicating the knocked out gene in each cell. The last column of X_train indicates the sampling time of each cell.
X_pred : T x (G+1) pandas DataFrame. The rows of X[:, :G] are real-valued vectors indicating expression change of target gene of perturbation. For knockout or knockdown, values should be negative. The last column of X_pred indicates the time at which expressions are predicted

Here,
T : The number of timepoints where expressions are predicted.

output
E_pred : T x G pandas DataFrame of predicted expression.

Reference

Inference of gene regulatory networks using time-series single-cell RNA-seq data with CRISPR perturbations, Masato Ishikawa, Seiichi Sugino, Yoshie Masuda, Yusuke Tarumoto, Yusuke Seto, Nobuko Taniyama, Fumi Wagai, Yuhei Yamauchi, Yasuhiro Kojima, Hisanori Kiryu, Kosuke Yusa, Mototsugu Eiraku, Atsushi Mochizuki, bioRxiv 2023.08.23.554254; doi: https://doi.org/10.1101/2023.08.23.554254