Single-cell RNA sequencing （scRNA-seq） is an emerging technology that enables high resolution detection of heterogeneities between ceils. One important application of scRNA-seq data is to detect differential expression （DE） of genes. Currently, some researchers still use DE analysis methods developed for bulk RNA-Seq data on single-cell data, and some new methods for scRNA-seq data have also been developed. Bulk and single-cell RNA-seq data have different characteristics. A systematic evaluation of the two types of methods on scRNA-seq data is needed. Results： In this study, we conducted a series of experiments on scRNA-seq data to quantitatively evaluate 14 popular DE analysis methods, including both of traditional methods developed for bulk RNA-seq data and new methods specifically designed for scRNA-seq data. We obtained observations and recommendations for the methods under different situations. Conclusions： DE analysis methods should be chosen for scRNA-seq data with great caution with regard to different situations of data. Different strategies should be taken for data with different sample sizes and/or different strengths of the expected signals. Several methods for scRNA-seq data show advantages in some aspects, and DEGSeq tends to outperform other methods with respect to consistency, reproducibility and accuracy of predictions on scRNA-seq data.
In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.
Recent studies have found many antisense non-coding transcripts at the opposite strand of some protein-coding genes.In yeast,it was reported that such antisense transcripts play regulatory roles for their partner genes by forming a feedback loop with the protein-coding genes.Since not all coding genes have accompanying antisense transcripts,it would be interesting to know whether there are sequence signatures in a coding gene that are decisive or associated with the existence of such antisense partners.We collected all the annotated antisense transcripts in the yeast Saccharomyces cerevisiae,analyzed sequence motifs around the genes with antisense partners,and classified genes with and without accompanying antisense transcripts by using machine learning methods.Some weak but statistically significant sequence features are detected,which indicates that there are sequence signatures around the protein-coding genes that may be decisive or indicative for the existence of accompanying antisense transcripts.
