SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer.

Gene co-expression analysis of single-cell transcriptomes, aiming to define functional relationships between genes, is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified.

Production of Aromatic Hydrocarbons from Co-Hydropyrolysis of Biomass Components and HDPE with Application of Modified HZSM-5 Catalyst.

Experiments were conducted in this study on the co-hydropyrolysis of three components of biomass (cellulose, hemicellulose, and lignin) and HDPE by using SR-Pd/Trap-HZ-5 as catalyst. To control the variable, we use the same experiment conditions in co-hydropyrolysis: Si/Al ratio of 50, Pd load 1 %, catalyst to reactant ratio of 1 : 10, 1 MPa, 400 °C, reaction time 1 h. Use XRD, TEM, BET, and NH-TPD to confirm catalyst successful synthesis; use pine sawdust (PW) co-hydropyrolysis with HDPE to analyse catalytic activity; and use GC/MS to characterize the chemical composition of the bio-oil from the co-hydropyrolysis of biomass components and HDPE.

Rice co-expression network analysis identifies gene modules associated with agronomic traits.

Identifying trait-associated genes is critical for rice (Oryza sativa) improvement, which usually relies on map-based cloning, quantitative trait locus analysis, or genome-wide association studies. Here we show that trait-associated genes tend to form modules within rice gene co-expression networks, a feature that can be exploited to discover additional trait-associated genes using reverse genetics. We constructed a rice gene co-expression network based on the graphical Gaussian model using 8,456 RNA-seq transcriptomes, which assembled into 1,286 gene co-expression modules functioning in diverse pathways.

An Arabidopsis expression predictor enables inference of transcriptional regulators for gene modules.

The regulation of gene expression by transcription factors (TFs) has been studied for a long time, but no model that can accurately predict transcriptome profiles based on TF activities currently exists. Here, we developed a computational approach, named EXPLICIT (Expression Prediction via Log-linear Combination of Transcription Factors), to construct a universal predictor for Arabidopsis to predict the expression of 29 182 non-TF genes using 1678 TFs. When applied to RNA-Seq samples from diverse tissues, EXPLICIT generated accurate predicted transcriptomes correlating well with actual expression, with an average correlation coefficient of 0.