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Motivation Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest-based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference. Results In this study, we propose a scalable, flexible approach called BiXGBoost to reconstruct GRNs. BiXGBoost is a bidirectional-based method by considering both candidate regulatory genes and target genes for a specific gene. Moreover, BiXGBoost utilizes time information efficiently and integrates XGBoost to evaluate the feature importance. Randomization and regularization are also applied in BiXGBoost to address the over-fitting problem. The results on DREAM4 and Escherichia coli datasets show the good performance of BiXGBoost on different scale of networks. Availability and implementation Our Python implementation of BiXGBoost is available at https://github.com/zrq0123/BiXGBoost. Supplementary information Supplementary data are available at Bioinformatics online.

The rapid development of single-cell transcriptome sequencing technology has provided us with a cell-level perspective to study biological problems. Identification of cell types is one of the fundamental issues in computational analysis of single-cell data. Due to the large amount of noise from single-cell technologies and high dimension of expression profiles, traditional clustering methods are not so applicable to solve it. To address the problem, we have designed an adaptive sparse subspace clustering method, called AdaptiveSSC, to identify cell types. AdaptiveSSC is based on the assumption that the expression of cells with the same type lies in the same subspace; one cell can be expressed as a linear combination of the other cells. Moreover, it uses a data-driven adaptive sparse constraint to construct the similarity matrix. The comparison results of 10 scRNA-seq datasets show that AdaptiveSSC outperforms original subspace clustering and other state-of-art methods in most cases. Moreover, the learned similarity matrix can also be integrated with a modified t-SNE to obtain an improved visualization result.

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