Biology subjects

Chang, W.

Publications and source records attributed to Chang, W..

4 recordsLinked to original sources

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗

bioRxiv2018

Derivation of a simple postoperative delirium incidence and severity prediction model

BackgroundDelirium is an important postoperative complication, yet a simple and effective delirium prediction model remains elusive. We hypothesized that the combination of the National Surgical Quality Improvement Program (NSQIP) risk calculator for serious complications (NSQIP-SC) or risk of death (NSQIP-D), and cognitive tests of executive function (Trail Making Test A and B [TMTA, TMTB]), could provide a parsimonious model to predict postoperative delirium incidence or severity.\n\nMethodsData were collected from 100 adults ([≥]65yo) undergoing major non-cardiac surgery. In addition to NSQIP-SC, NSQIP-D, TMTA and TMTB, we collected participant age, sex, ASA score, tobacco use, type of surgery, depression, Framingham risk score, and preoperative blood pressure. Delirium was diagnosed with the Confusion Assessment Method (CAM), and the Delirium Rating Scale-R-98 (DRS) was used to assess symptom severity. LASSO and Best Subsets logistic and linear regression were employed in line with TRIPOD guidelines.\n\nResultsThree participants were excluded due to intraoperative deaths (2) and alcohol withdrawal (1). Ninety-seven participants with a mean age of 71.68{+/-}4.55, 55% male (31/97 CAM+, 32%) and a mean Peak DRS of 21.5{+/-}6.40 were analyzed. Of the variables included, only NSQIP-SC and TMTB were identified to be predictors of postoperative delirium incidence (p<0.001, AUROC 0.81, 95% CI: 0.72, 0.90) and severity (p<0.001, Adj. R2: 0.30).\n\nConclusionsIn this cohort, preoperative NSQIP-SC and TMTB were identified as predictors of postoperative delirium incidence and severity. Future studies should verify whether this two-factor model could be used for accurate delirium prediction.

clinical trials↗

bioRxiv2018

ICTD: Inference of cell types and deconvolution -- a next-generation deconvolution method for accurate assess cell population and activities in tumor microenvironment.

We developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

bioinformatics↗

bioRxiv2018

Large-scale gene losses underlie the genome evolution of parasitic plant Cuscuta australis

Dodders (Cuscuta spp., Convolvulaceae) are globally distributed root- and leafless parasitic plants that parasitize a wide range of hosts. The physiology, ecology, and evolution of these obligate parasites are still poorly understood. A high-quality reference genome (size 266.74 Mb and contig N50 of 3.63 Mb) of Cuscuta australis was assembled. Our analyses reveal that Cuscuta experienced accelerated evolution, and Cuscuta and the convolvulaceous morning glory (Ipomoea) shared a common whole-genome triplication event before their divergence. Importantly, C. australis genome harbors only 19805 protein-coding genes, and 11.7% of the conserved orthologs in autotrophic plants are lost in C. australis. Many of these gene loss events likely result from the plants parasitic lifestyle and large changes in its body plan. Moreover, comparison of the gene expression patterns in Cuscuta prehaustoria/haustoria and various tissues of closely related autotrophic plants suggests that Cuscuta haustorium genes largely evolved from roots. The C. australis genome provides important resources for studying the evolution of parasitism, regressive evolution, and evo-devo in plant parasites.

genomics↗