Single-cell multi-omics integration for unpaired data by a siamese network with graph-based contrastive loss.

BACKGROUND: Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. RESULTS: By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. CONCLUSIONS: MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Response of Summer Maize Growth and Water Use to Different Irrigation Regimes

Summer maize crop development, yield, and water use characteristics under water deficit conditions at different growth stages were investigated in this study using different irrigation regime treatments at the seedling (S), jointing (J), tasseling (T), and grain filling stages (F) in 2018 and 2019 in China. Ten different irrigation treatments were set, including three-irrigation application intervals (JTFi, STFi, SJFi, SJTi), two-irrigation applications (STi, JTi, JFi), and single-irrigation applications (Ti, Ji). These were compared to the control treatment (CK), which had sufficient irrigation provided at four intervals (SJTFi). The results showed that compared to CK, a water deficit at the seedling and jointing stages had a greater effect on plant height, whereas a water deficit at the tasseling and filling stages had a greater effect on the leaf area index, and a continuous water deficit had an effect on the stem diameter of summer maize. Limitations in terms of the growth and development of summer maize increased with less frequent irrigation. As irrigation decreased, the grain yield decreased, and the water use efficiency increased, and a water deficit at the tasseling stage had the greatest effect on the yield and water use efficiency. The JTFi treatment was the optimal irrigation regime with a yield decline, and its water consumption was reduced by 16.9% (p < 0.05) on average. However, compared to CK, the water use efficiency of the JTFi treatment increased by 17.3% (p < 0.05). Moreover, the JTFi treatment had the smallest maize yield response factor value (Ky) of 0.16, and its comprehensive score was the second highest after CK. [ FROM AUTHOR] Copyright of Agronomy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)