Journal: bioRxiv

Article Title: Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without Affecting Cancer Treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

doi: 10.1101/2024.02.11.579390

Figure Lengend Snippet: Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.

Article Snippet: Our implemented t-distributed Stochastic Neighbor Embedding (t-SNE) toolbox that performs t-SNE calculations ( ) uses the results from the correlation matrix and presents the genes based on their correlation values in 3D using a built-in ‘ tsne ’ function implemented in MATLAB.

Techniques: RNA Sequencing Assay, Expressing, Extraction, Construct, Generated

Journal: Nature Biotechnology

Article Title: Genome-scale metabolic reconstruction of 7,302 human microorganisms for personalized medicine

doi: 10.1038/s41587-022-01628-0

Figure Lengend Snippet: a – d , Clustering through t -SNE of reaction presence across all pathways per reconstruction. Coordinates were statistically different across taxonomic units (Kruskal–Wallis test, P = 0.0001 in all cases). a , Members of the largest classes. b , Members of the largest families. c , Members of the Bacilli class by genus. d , Members of the Gammaproteobacteria class by genus. e – h , Features of all AGORA2 reconstructions across phyla: e , Number of reactions. f , Number of metabolites. g , Number of genes. h , growth rate in h −1 on aerobic Western diet.

Article Snippet: Clustering of taxa by reaction presence through t -distributed stochastic neighbor embedding ( t -SNE) was performed using the t -SNE implementation in MATLAB with Euclidean distance, barneshut set as the algorithm and perplexity set to 30.

Techniques: Western Blot