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Explore microbiome analysis techniques using various tools. Learn OTU clustering, diversity analysis, PCA, AMOVA, tree plotting, and visualization with Galaxy, Krona, and MEGAN6 for comprehensive 16S rRNA sequencing insights.
Learn to analyze cancer somatic mutations using MAFtools R package. Covers importing data, visualization techniques, driver detection, survival analysis, and comparing cancer cohorts for comprehensive mutation insights.
Learn techniques for handling missing data in R, including various imputation methods, using packages like mice, and evaluating algorithm performance with ggplot2.
Comprehensive walkthrough on using machine learning to identify TCGA biomarkers, covering data preparation, neural network construction, and gene set enrichment analysis.
Learn to integrate and compare scRNA-Seq datasets from different conditions using Seurat, covering data import, integration, differential expression analysis, and visualization techniques.
Learn to implement Linear Mixed Models in R using lme(). Understand statistical modeling concepts, fixed and random effects, and interpret results for complex data relationships.
Learn to combine multiple heatmaps in R using ComplexHeatmap. Master horizontal and vertical merging, customization, and annotations for effective data visualization in bioinformatics.
Learn to create intricate heatmaps in R using ComplexHeatmap package. Master setup, color customization, title modification, and data ordering for effective visualization of multidimensional genomic data.
Explore log2 fold-change, DESeq2 model, and differential expression analysis challenges. Learn the theory behind DESeq2 and its practical application in R for bioinformatics research.
Learn to analyze single-cell RNA-seq data using Seurat package, covering data import, QC, normalization, clustering, and marker identification. Gain insights into cell interactions and create visualizations.
Comprehensive guide to RNASeq analysis using open-source tools, covering FastQ to DEG identification with Rsubread and DESeq2. Includes step-by-step instructions and practical tips for bioinformatics researchers.
Aprenda análise de sobrevivência em dados de câncer usando R. Tutorial prático sobre pipeline de análise, uso de dados TCGA, expressão gênica e modelagem preditiva para pesquisa biomédica.
Explore recent advancements in deep learning for bioinformatics, covering BTINET, autoencoders, and deep belief networks. Gain insights into cutting-edge applications in genomics, drug discovery, and disease prediction.
Learn to perform essential statistical analyses in R, including correlation, linear modeling, t-tests, and heatmaps. Ideal for beginners seeking to leverage R's powerful command-line interface for data analysis.
Learn to automate report generation in Google Sheets using Apps Script. Create tables, graphs, and text replacements without add-ons, streamlining data processing from raw CDC EU information.
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