EZcount: An all-in-one software for microRNA expression quantification from NGS sequencing data

SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares

BACKGROUND AND OBJECTIVES

Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is equivalent to introducing noise into the data of cell gene expression entries. The data is contaminated, which affects the performance of downstream analyses, including clustering, cell annotation, differential gene expression analysis, and so on. Therefore, it is a crucial work to accurately determine which zeros are due to dropout events and perform imputation operations on them.

METHODS

Considering the different confidence levels of different zeros in the gene expression matrix, this paper proposes a SinCWIm method for dropout events in scRNA-seq based on weighted alternating least squares (WALS). The method utilizes Pearson correlation coefficient and hierarchical clustering to quantify the confidence of zero entries. It is then combined with WALS for matrix decomposition. And the imputation result is made close to the actual number by outlier removal and data correction operations.

RESULTS

A total of eight single-cell sequencing datasets were used for comparative experiments to demonstrate the overall superiority of SinCWIm over state-of-the-art models. SinCWIm was applied to cluster the data to obtain an adjusted RAND index evaluation, and the Usoskin, Pollen and Bladder datasets scored 94.46%, 96.48% and 76.74%, respectively. In addition, significant improvements were made in the retention of differential expression genes and visualization.

CONCLUSIONS

SinCWIm provides a valuable imputation method for handling dropout events in single-cell sequencing data. In comparison to advanced methods, SinCWIm demonstrates excellent performance in clustering, visualization and other aspects. It is applicable to various single-cell sequencing datasets.

Dual-Channel Heterogeneous Graph Neural Network for Predicting microRNA-Mediated Drug Sensitivity

Many studies have confirmed that microRNAs (miRNAs) are mediated in the sensitivity of tumor cells to anticancer drugs. MiRNAs are emerging as a type of promising therapeutic targets to overcome drug resistance. However, there is limited attention paid to the computational prediction of the associations between miRNAs and drug sensitivity. In this work, we proposed a heterogeneous network-based representation learning method to predict miRNA-drug sensitivity associations (DGNNMDA). An miRNA-drug heterogeneous network was constructed by integrating miRNA similarity network, drug similarity network, and experimentally validated miRNA-drug sensitivity associations. Next, we developed a dual-channel heterogeneous graph neural network model to perform feature propagation among the homogeneous and heterogeneous nodes so that our method can learn expressive representations for miRNA and drug nodes. On two benchmark datasets, our method outperformed other seven competitive methods. We also verified the effectiveness of the feature propagations on homogeneous and heterogeneous nodes. Moreover, we have conducted two case studies to verify the reliability of our methods and tried to reveal the regulatory mechanism of miRNAs mediated in drug sensitivity. The source code and datasets are freely available at https://github.com/19990915fzy/DGNNMDA.