MOTIVATION: Understanding cell differentiation and development dynamics is key for single-cell transcriptome analysis. Current cell differentiation trajectory inference algorithms face challenges such as high dimensionality, noise, and a need for users to possess certain biological information about the datasets to effectively utilize the algorithms. Here, we introduce Trajectory Inference with Cell-Cell Interaction (TICCI), a novel way to address these challenges by integrating intercellular communication information. In recognizing crucial intercellular communication during development, TICCI proposes Cell-Cell Interactions (CCI) at single-cell resolution. We posit that cells exhibiting higher gene expression similarity patterns are more likely to exchange information via biomolecular mediators. RESULTS: TICCI is initiated by constructing a cell-neighborhood matrix using edge weights composed of intercellular similarity and CCI information. Louvain partitioning identifies trajectory branches, attenuating noise, while single-cell entropy (scEntropy) is used to assess differentiation status. The Chu-Liu algorithm constructs a directed least-square model to identify trajectory branches, and an improved diffusion fitted time algorithm computes cell-fitted time in nonconnected topologies. TICCI validation on single-cell RNA sequencing (scRNA-seq) datasets confirms the accuracy of cell trajectories, aligning with genealogical branching and gene markers. Verification using extrinsic information labels demonstrates CCI information utility in enhancing accurate trajectory inference. A comparative analysis establishes TICCI proficiency in accurate temporal ordering. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available for download at https://github.com/mine41/TICCI, implemented in R (version 4.32) and Python (version 3.7.16) and supported on MS Windows. Authors ensure that the software is available for a full two years following publication.

UNLABELLED: Thousands of human long non-coding RNAs (lncRNAs) have been identified in cancers and played important roles in a wide range of tumorigenesis. However, the functions of vast majority of human lncRNAs are still elusive. Emerging studies revealed that the expression level of majority lncRNAs shows discordant expression pattern with their protein-coding gene neighbors in various model organisms. Therefore, it may be useful to infer lncRNAs' potential biological function in cancer development by more comprehensive functional views of co-expressed cancer genes beyond mere physical proximity of genes. To this aim, we performed thorough searches and analyses of the interactions between lncRNA and non-neighboring cancer genes and provide a comprehensive co-expression data resource, LnCaNet. In current version, LnCaNet contains the pre-computed 8 494 907 significant co-expression pairs of 9641 lncRNAs and 2544 well-classified cancer genes in 2922 matched TCGA samples. In detail, we integrated 10 cancer gene lists from public database and calculate the co-expression with all the lncRNAs in 11 TCGA cancer types separately. Based on the resulted 110 co-expression networks, we identified 17 common regulatory pairs related to extracellular space shared in 11 cancers. We expect LnCaNet will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction. AVAILABILITY AND IMPLEMENTATION: http://lncanet.bioinfo-minzhao.org/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets.

Background It has been demonstrated that, at the network level, the transcriptional regulation by transcription factors (TFs) and post-transcriptional regulation by microRNAs (miRNAs) are tightly coupled. Aberrant expression of these bio-molecules is linked to several diseases, including lung cancer. In this study, we pursued a regulatory networkbased approach mediated by TFs and miRNAs for a comprehensive investigation of gene regulation patterns in lung cancer.

Copy number variation (CNV) is a prevalent form of critical genetic variation that leads to an abnormal number of copies of large genomic regions in a cell. Microarray-based comparative genome hybridization (arrayCGH) or genotyping arrays have been standard technologies to detect large regions subject to copy number changes in genomes until most recently high-resolution sequence data can be analyzed by next-generation sequencing (NGS). During the last several years, NGS-based analysis has been widely applied to identify CNVs in both healthy and diseased individuals. Correspondingly, the strong demand for NGS-based CNV analyses has fuelled development of numerous computational methods and tools for CNV detection. In this article, we review the recent advances in computational methods pertaining to CNV detection using whole genome and whole exome sequencing data. Additionally, we discuss their strengths and weaknesses and suggest directions for future development.