Journal: Frontiers in Genetics

Article Title: Identification of miRNA–mRNA–TFs Regulatory Network and Crucial Pathways Involved in Tetralogy of Fallot

doi: 10.3389/fgene.2020.00552

Figure Lengend Snippet: Microarray and RNA-seq datasets used in this study and their experimental design.

Article Snippet: 3 , GSE26125 , 5 , 16 , CodeLink Human Whole Genome Bioarray.

Techniques: Microarray

Journal: Environmental Health Perspectives

Article Title: Workgroup Report: Review of Genomics Data Based on Experience with Mock Submissions—View of the CDER Pharmacology Toxicology Nonclinical Pharmacogenomics Subcommittee

doi: 10.1289/ehp.8318

Figure Lengend Snippet: Format of content in the mock submissions.

Article Snippet: Microarray platform , Affymetrix GeneChip (rat genome U34 arrays) , Affymetrix GeneChip (human HGU95Av2 arrays) , CodeLink RU1 Expression BioArrays (rat).

Techniques: Microarray, Expressing

Journal: Nature Communications

Article Title: Identification of a human neonatal immune-metabolic network associated with bacterial infection

doi: 10.1038/ncomms5649

Figure Lengend Snippet: ( a ) Members of the network as a classifier of neonatal infection using leave-one-out cross-validation analysis using four independent machine-learning algorithms (red circle, random forest; green triangle, support vector machines; blue+, K nearest neighbour and black x, receiver operator characteristics). ( b ) Flow chart of biomarker classifier validation. Flow chart showing the samples used for classifier generation and the validation results. ( c ) Heat map showing hierarchical clustering of 18 infant samples (9 bacterially infected, 6 control, 3 virally infected) based on the 50 probes that were in common between the classifier gene set and the Affymetrix U219 platform. Hierarchical clustering was based on Euclidean distance. Control, blue; bacterial infected, red; viral infected, black. Classification of bacterial infection is indicated (0=non-infected, 1=infected). ( d ) Comparison of microarray-based classifier and expert assessment for classification of samples from patients with suspected infection. (Top) Comparison of expert assessment, CD69/FCGR1A and the 52-gene classifier on samples scored ‘high’ and ‘low’ likelihood of infection by expert assessment. (Middle) 52-gene classifier prediction and expert assessment of suspected sepsis cases (red=concordance of ‘infection’, blue=concordance of ‘control’, dark grey=discordance of microarray classifier and expert assessment). (Bottom left) Comparison of CD69/FCGR1A and 52-gene classifier of samples scored ‘medium’ likelihood of infection by expert assessment. Classification of bacterial infection is indicated (0=non-infected (pale blue), 1=infected (pink)). (Bottom right) Sensitivity, specificity and accuracy of CD69/FCGR1A and the 52-gene classifier against expert classification for suspected samples from d top. A heat map showing the 30 infant samples of suspected infection based on the 46 probes that were in common between the classifier gene set and the CodeLink platform is shown in . ( e ) Bar plots showing clinical criteria in suspected infection cases as judged by the 52-gene classifier and expert assessment. Days on antibiotics and neutrophil counts are shown for samples based on classification of control (pale blue) and bacterial infection (pink) showing median and standard error of the median. Days on antibiotics, classifier prediction: infected: n =15 (excluding 2 missing values); control: n =12 (excluding 1 missing value). Days on antibiotics, expert assessment: infected: n =5 (excluding 1 missing value); control: n =14 (excluding 1 missing value). Neutrophil count, classifier prediction: infected, n =17; control, n =13. Neutrophil count, expert assessment: infected: n =6; control: n =15.

Article Snippet: In addition to the Illumina microarray analysis, we examined a subset of 42 of these samples (18 infected, 24 controls) using CodeLink Whole Human Genome arrays, referred to as ‘platform test set’.

Techniques: Infection, Plasmid Preparation, Biomarker Assay, Microarray

Journal: PLoS Computational Biology

Article Title: Early Transcriptome Signatures from Immunized Mouse Dendritic Cells Predict Late Vaccine-Induced T-Cell Responses

doi: 10.1371/journal.pcbi.1004801

Figure Lengend Snippet: (i) For each pre-processed dataset, composed of microarray measures for mice injected with vector 1 (V1.1, V1.2 …) and control (C1.1, C1.2 …), one hundred datasets were created by bootstrapping samples among V and C. (ii) Ranked gene lists, according to the eBayes statistical comparison of vector and control conditions, were generated. (iii) Potential signatures were tested for enrichment on each of the 100 ranked gene lists by GSEA. The resulting NES matrix was then used to build the random forest model.

Article Snippet: The model was powerful enough to produce a relevant vector classification even when using whole mouse spleen and PBMCs, or even human PBMCs , and across 3 microarray platforms (CodeLink, Illumina and Affymetrix).

Techniques: Microarray, Injection, Plasmid Preparation, Control, Comparison, Generated