Single-cell RNA-seq facilitates the understanding of cell types and states and the revealing of the cellular heterogeneity in developmental processes and disease mechanisms. However, the dropout events in single-cell RNA-seq data, in which genes are not detected due to technical noise or limited sequencing depth, seriously affect downstream analyses. Imputation is an effective way to relieve the impact of dropout events. However, the current methods may introduce new noise or modify the high expression values in the imputation process and their performance may be lower than expected when dealing with data with a high dropout rate, facing with different types of data, and aiming at various downstream analyses. We propose a two-stage imputation algorithm, scTsI, for single-cell RNA-seq data. In the first stage, scTsI imputes the zero values using the information of neighboring cells and genes. In the second stage, scTsI transforms the expression matrix into a vector, performs row transformation, and adjusts the imputed values through ridge regression and leveraging bulk RNA-seq data as a constraint. scTsI ensures that the original highly expressed values are unchanged, avoids introducing new noise, and allows sparse matrix input to accelerate imputation. We conduct experiments on a variety of simulated and real data with different dropout rates and compare scTsI with the commonly used imputation methods. The results show that scTsI can restore gene expression and maintain cell-cell similarity across different data dimensions and dropout rates. scTsI can also improve the performance of data visualization, clustering, and cell trajectory inference.

MOTIVATION: Utilizing both purebred and crossbred data in animal genetics is widely recognized as an optimal strategy for enhancing the predictive accuracy of breeding values. Practically, the different genetic background among several purebred populations and their crossbred offspring populations limits the application of traditional prediction methods. Several studies endeavor to predict the crossbred performance via the partial relationship, which divides the data into distinct sub-populations based on the common genetic background, such as one single purebred population and its corresponding crossbred descendant. However, this strategy makes prediction inaccurate due to ignoring half of the parental information of crossbreed animals. Furthermore, dominance effects, although playing a significant role in crossbreeding systems, cannot be modeled under such a prediction model. RESULTS: To overcome this weakness, we developed a novel multi-breed single-step model using metafounders to assess ancestral relationships across diverse breeds under a unified framework. We proposed to use multi-breed dominance combined relationship matrices to model additive and dominance effects simultaneously. Our method provides a straightforward way to evaluate the heterosis of crossbreeds and the breeding values of purebred parents efficiently and accurately. We performed simulation and real data analyses to verify the potential of our proposed method. Our proposed model improved prediction accuracy under all scenarios considered compared to commonly used methods. AVAILABILITY AND IMPLEMENTATION: The software for implementing our method is available at https://github.com/CAU-TeamLiuJF/MAGE.

This paper proposed a multiple-mode animal behavior analysis system integrating a wireless bio-potential recorder and two 120fps video streams. A 16-channel analog-front-end (AFE) featuring a chopper low noise amplifier (LNA) and a 12-bit successive approximation analog-to-digital converter (SAR ADC) is designed as the sensor interface. Bluetooth Low Energy (BLE) based in-the-air protocol is implemented for wireless data transmission. A precise synchronization method is proposed featuring a millisecond level synchronization accuracy between the video frames and the acquired bio-potential. The compact wireless bio-potential recorder features a size of 2 × 2.5 × 1cm and a battery life of 9 hours. In-vivo test has been performed on rats with long-term implantable electrodes. The proposed system successfully recorded the electroneurogram (ENG) signal from sciatic nerves and electromyography (EMG) signal from leg muscles. Also video-based gait analysis was performed and provided the labels to train an EMG-based gait phase classifier.