Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology performed at the level of an individual cell, which can have a potential to understand cellular heterogeneity. However, scRNA-seq data are high-dimensional, noisy, and sparse data. Dimension reduction is an important step in downstream analysis of scRNA-seq. Therefore, several dimension reduction methods have been developed. We developed a strategy to evaluate the stability, accuracy, and computing cost of 10 dimensionality reduction methods using 30 simulation datasets and five real datasets. Additionally, we investigated the sensitivity of all the methods to hyperparameter tuning and gave users appropriate suggestions. We found that t-distributed stochastic neighbor embedding (t-SNE) yielded the best overall performance with the highest accuracy and computing cost. Meanwhile, uniform manifold approximation and projection (UMAP) exhibited the highest stability, as well as moderate accuracy and the second highest computing cost. UMAP well preserves the original cohesion and separation of cell populations. In addition, it is worth noting that users need to set the hyperparameters according to the specific situation before using the dimensionality reduction methods based on non-linear model and neural network.

Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs, and the functional deregulations of lncRNAs have been shown to be associated with the development and progression of BC. In this work, we conduct an integrative analysis on five re-annotated lncRNA expression datasets from the Gene Expression Omnibus (GEO) which included a total of 891 BC samples. We identified a five-lncRNA signature that was significantly associated with DFS in the training cohort of 327 patients. We found the five-lncRNA signature could effectively stratify patients in the training dataset into high- and low-risk groups with significantly different DFS (p = 3.29 × 10 , log-rank test). The five-lncRNA signature was effectively validated in four independent cohorts, and prognostic analysis results showed that the five-lncRNA signature was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Furthermore, GSEA suggested that the five-lncRNA signature was involved in BC metastasis-related pathways. Our findings indicate that these five lncRNAs may be implicated in BC pathogenesis, and further, these lncRNAs may potentially serve as novel candidate biomarkers for the identification of BC patients at high risk for tumor recurrence.

Extensive changes in DNA methylation have been observed in schizophrenia (SC) and bipolar disorder (BP), and may contribute to the pathogenesis of these disorders. Here, we performed genome-scale DNA methylation profiling using methylated DNA immunoprecipitation followed by sequencing (MeDIP-seq) on two brain regions (including frontal cortex and anterior cingulate) in 5 SC, 7 BP and 6 normal subjects. Comparing with normal controls, we identified substantial differentially methylated regions (DMRs) in these two brain regions of SC and BP. To our surprise, different brain regions show completely distinct distributions of DMRs across the genomes. In frontal cortex of both SC and BP subjects, we observed widespread hypomethylation as compared to normal controls, preferentially targeting the terminal ends of the chromosomes. In contrast, in anterior cingulate, both SC and BP subjects displayed extensive gain of methylation. Notably, in these two brain regions of SC and BP, only a few DMRs overlapped with promoters, whereas a greater proportion occurs in introns and intergenic regions. Functional enrichment analysis indicated that important psychiatric disorder-related biological processes such as neuron development, differentiation and projection may be altered by epigenetic changes located in the intronic regions. Transcriptome analysis revealed consistent dysfunctional processes with those determined by DMRs. Furthermore, DMRs in the same brain regions from SC and BP could successfully distinguish BP and/or SC from normal controls while differentially expressed genes could not. Overall, our results support a major role for brain-region-dependent aberrant DNA methylation in the pathogenesis of these two disorders.

LncRNAs have become rising stars in biology and medicine, due to their versatile functions in a wide range of important biological processes and active roles in various human cancers. Here, we developed a computational method based on the naïve Bayesian classifier method to identify cancer-related lncRNAs by integrating genome, regulome and transcriptome data, and identified 707 potential cancer-related lncRNAs. We demonstrated the performance of the method by ten-fold cross-validation, and found that integration of multi-omic data was necessary to identify cancer-related lncRNAs. We identified 707 potential cancer-related lncRNAs and our results showed that these lncRNAs tend to exhibit significant differential expression and differential DNA methylation in multiple cancer types, and prognosis effects in prostate cancer. We also found that these lncRNAs were more likely to be direct targets of TP53 family members than others. Moreover, based on 147 lncRNA knockdown data in mice, we validated that four of six mouse orthologous lncRNAs were significantly involved in many cancer-related processes, such as cell differentiation and the Wnt signaling pathway. Notably, one lncRNA, lnc-SNURF-1, which was found to be associated with TNF-mediated signaling pathways, was up-regulated in prostate cancer and the protein-coding genes affected by knockdown of the lncRNA were also significantly aberrant in prostate cancer patients, suggesting its probable importance in tumorigenesis. Taken together, our method underlines the power of integrating multi-omic data to uncover cancer-related lncRNAs.