Kenong Su
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View article: Additional file 3 of NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
Additional file 3 of NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity Open
Additional file 3: Supplementary tables: Table S1. Summary of the transcription factor (TF)-target databases for both human and mouse genomes. Table S2. Summary of the publicly available gene expression data sets for benchmarking TF-target…
View article: NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity Open
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription …
View article: A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation
A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation Open
View article: Cell type-specific DNA methylome signatures reveal epigenetic mechanisms for neuronal diversity and neurodevelopmental disorder
Cell type-specific DNA methylome signatures reveal epigenetic mechanisms for neuronal diversity and neurodevelopmental disorder Open
DNA methylation plays a critical function in establishing and maintaining cell identity in brain. Disruption of DNA methylation-related processes leads to diverse neurological disorders. However, the role of DNA methylation characteristics…
View article: NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity
NetAct: a computational platform to construct core transcription factor regulatory networks using gene activity Open
A major question in systems biology is how to identify the core gene regulatory circuit that governs the decision-making of a biological process. Here, we develop a computational platform, named NetAct, for constructing core transcription-…
View article: Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors
Leveraging information in spatial transcriptomics to predict super-resolution gene expression from histology images in tumors Open
Recent developments in spatial transcriptomics (ST) technologies have enabled the profiling of transcriptome-wide gene expression while retaining the location information of measured genes within tissues. Moreover, the corresponding high-r…
View article: Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction
Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction Open
View article: Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis
Pan-cancer analysis of pathway-based gene expression pattern at the individual level reveals biomarkers of clinical prognosis Open
Identifying biomarkers to predict the clinical outcomes of individual patients is a fundamental problem in clinical oncology. Multiple single-gene biomarkers have already been identified and used in clinics. However, multiple oncogenes or …
View article: Non-linear Normalization for Non-UMI Single Cell RNA-Seq
Non-linear Normalization for Non-UMI Single Cell RNA-Seq Open
Single cell RNA-seq data, like data from other sequencing technology, contain systematic technical noise. Such noise results from a combined effect of unequal efficiencies in the capturing and counting of mRNA molecules, such as extraction…
View article: Accurate feature selection improves single-cell RNA-seq cell clustering
Accurate feature selection improves single-cell RNA-seq cell clustering Open
Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as ‘features’), whose expre…
View article: Additional file 3 of Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction
Additional file 3 of Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction Open
Additional file 3. R data frame including all 29 experiment results experiment_results.RDS (an R RDS file).
View article: Additional file 2 of Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction
Additional file 2 of Evaluation of some aspects in supervised cell type identification for single-cell RNA-seq: classifier, feature selection, and reference construction Open
Additional file 2. Overview of experiments Excel Results_Summary_Table.xlsx (Excel file).
View article: Simulation, power evaluation and sample size recommendation for single-cell RNA-seq
Simulation, power evaluation and sample size recommendation for single-cell RNA-seq Open
Motivation Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size cal…
View article: Sliced Human Cortical Organoids for Modeling Distinct Cortical Layer Formation
Sliced Human Cortical Organoids for Modeling Distinct Cortical Layer Formation Open
View article: Pan-Cancer Analysis of Pathway-Based Gene Expression Pattern at the Individual Level Reveals Novel Biomarkers of Clinical Prognosis
Pan-Cancer Analysis of Pathway-Based Gene Expression Pattern at the Individual Level Reveals Novel Biomarkers of Clinical Prognosis Open