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Modern Applied Statistics With S

1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Classification and diagnosis of diabetes

Pancreatic cancer remains one of the most lethal of malignancies and a major health burden. We performed whole-genome sequencing and copy number variation (CNV) analysis of 100 pancreatic ductal adenocarcinomas (PDACs). Chromosomal rearrangements leading to gene disruption were prevalent, affecting genes known to be important in pancreatic cancer (TP53, SMAD4, CDKN2A, ARID1A and ROBO2) and new candidate drivers of pancreatic carcinogenesis (KDM6A and PREX2). Patterns of structural variation (variation in chromosomal structure) classified PDACs into 4 subtypes with potential clinical utility: the subtypes were termed stable, locally rearranged, scattered and unstable. A significant proportion harboured focal amplifications, many of which contained druggable oncogenes (ERBB2, MET, FGFR1, CDK6, PIK3R3 and PIK3CA), but at low individual patient prevalence. Genomic instability co-segregated with inactivation of DNA maintenance genes (BRCA1, BRCA2 or PALB2) and a mutational signature of DNA damage repair deficiency. Of 8 patients who received platinum therapy, 4 of 5 individuals with these measures of defective DNA maintenance responded.

Whole genomes redefine the mutational landscape of pancreatic cancer.

Understanding immune memory to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for improving diagnostics and vaccines and for assessing the likely future course of the COVID-19 pandemic. We analyzed multiple compartments of circulating immune memory to SARS-CoV-2 in 254 samples from 188 COVID-19 cases, including 43 samples at ≥6 months after infection. Immunoglobulin G (IgG) to the spike protein was relatively stable over 6+ months. Spike-specific memory B cells were more abundant at 6 months than at 1 month after symptom onset. SARS-CoV-2-specific CD4+ T cells and CD8+ T cells declined with a half-life of 3 to 5 months. By studying antibody, memory B cell, CD4+ T cell, and CD8+ T cell memory to SARS-CoV-2 in an integrated manner, we observed that each component of SARS-CoV-2 immune memory exhibited distinct kinetics.

Immunological memory to SARS-CoV-2 assessed for up to 8 months after infection.

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

In high-throughput data, dynamic correlation between genes, i.e. changing correlation patterns under different biological conditions, can reveal important regulatory mechanisms. Given the complex nature of dynamic correlation, and the underlying conditions for dynamic correlation may not manifest into clinical observations, it is difficult to recover such signal from the data. Current methods seek underlying conditions for dynamic correlation by using certain observed genes as surrogates, which may not faithfully represent true latent conditions. In this study we develop a new method that directly identifies strong latent signals that regulate the dynamic correlation of many pairs of genes, named DCA: Dynamic Correlation Analysis. At the center of the method is a new metric for the identification of gene pairs that are highly likely to be dynamically correlated, without knowing the underlying conditions of the dynamic correlation. We validate the performance of the method with extensive simulations. In real data analysis, the method reveals novel latent factors with clear biological meaning, bringing new insights into the data.

DCA: Dynamic Correlation Analysis

Single-Cell RNA sequencing (scRNA-seq) technology measures the expression of thousands of genes at the cellular level. Analyzing single cell transcriptome allows the identification of heterogeneous cell groups, cellular-level regulations, and the trajectory of cell development. An important aspect in the analyses of scRNA-seq data is the clustering of cells, which is hampered by issues such as high dimensionality, cell type imbalance, redundancy, and dropout. Given cells of each type are functionally consistent, incorporating biological relations between genes may improve the clustering results. Here, we develop a deep embedded clustering method, G3DC, that incorporates a graph loss based on existing gene network, together with a reconstruction loss to achieve both discriminative and informative embedding. The involvement of the gene network strengthens clustering performance, while helping the selection of functionally coherent genes that contribute to the clustering results. In addition, this method is well adapted to the sparse and zero-inflated scRNA-seq data with the ℓ2,1-norm involved. Extensive experiments have shown that G3DC offers high clustering accuracy with regard to agreement with true cell types, outperforming other leading single-cell clustering methods. In addition, G3DC selects biologically relevant genes that contribute to the clustering, providing insight into biological functionality that differentiate cell groups.

/pdf/g3dc-a-gene-graph-guided-selective-deep-clustering-method-2kypmk3y.pdf

G3DC: a Gene-Graph-Guided selective Deep Clustering method for single cell RNA-seq data

Introduction to Self-assembly
Protein Properties that Promote Self-assembly


Historical Outline
Chemical Structures
Symmetry
Symmetry Elements
Symmetric Contacts in Proteins
Multiple Symmetry Elements


Naturally Occurring Symmetric Complexes
Protein Oligomers
Icosahedral Viral Capsids
Clathrin
Filaments
S-layers
Protein Crystals


Designed Self-assembling Protein Complexes
Coiled-coil Extensions
Domain Swapping
Symmetric Construction
Polyvalency


Observing Protein Assemblies
Transmission Electron Microscopy
Cryoelectron Microscopy
Atomic Force Microscopy
X-ray and Electron Diffraction


Applications
Patents
Outlook and Perspectives


Keywords:

actin filament;
atomic force microscopy;
clathrin;
cryoelectron microscopy;
crystals;
cytoskeleton;
electron diffraction;
electron microscopy;
group generators;
intermediate filament;
microtubules;
myosin filament;
protein cage;
protein complex;
protein design;
protein engineering;
protein filament;
protein layer;
protein material;
protein oligomer;
protein shell;
S-layers;
self-assembly;
space groups;
symmetry;
transmission electron microscopy;
virus structure;
X-ray diffraction

Self‐assembling Symmetric Protein Materials

Use of High-Resolution Metabolomics to Identify Potential Metabolic Pathways Associated with Traffic-Related Air Pollutants

4468 Objectives: Seven salivary oral cancer transcriptome biomarkers (SOCTB: SAT1, S100P, OAZ1, IL1B, IL8, H3F3A and DUSP1) have been identified to distinguish oral cancer from normal controls by relative quantification. However, technological variables inherent in DNA-based standard quantitative RT-PCR, as well as differential RNA degradation and RNA input created baseline shifts that prevented use of a cut-off threshold between experiments to segregate oral cancer from controls. The purpose of this study is to further validate the 7 biomarkers and to establish a genuine absolute quantitative PCR using RNA-based standard quantification, and using saliva internal control gene to normalize input RNA quantity, to make salivary RNA biomarker measurement more accurate and reproducible and solve the base-line shifting problem. Methods: For detecting each of the SOCTB, technology variance was explored by utilizing genuine absolute quantification. Primers for specific amplification of each of 7 SOCTB mRNAs with T 7 promoter sequence fused to 5’ end were designed and used to generate recombinant RNA(recRNA). Quantitation of recRNA was accurately measured with Nanodrop bioanalyser, and serial dilution was used to generate standard. Each set of standard was quantified by real time quantitativePCR (qPCR) parallel with saliva RNA samples. RNA input and degradation was explored by normalization to saliva internal reference (SIR) genes. WilCOXon test and ROC analysis were used to judge the quality of makers. Results: qPCR data has shown that in 4 independent detection trials, the 7 SOCTB gene expression levels are significantly elevated (> 1.5 fold, p

Tianwei Yu

Papers

DCA: Dynamic Correlation Analysis

G3DC: a Gene-Graph-Guided selective Deep Clustering method for single cell RNA-seq data

Self‐assembling Symmetric Protein Materials

Use of High-Resolution Metabolomics to Identify Potential Metabolic Pathways Associated with Traffic-Related Air Pollutants

Salivary oral cancer transcriptome biomarkers (SOCTB) for clinical detection