Single cell RNA-sequencing: Methods and Data Analysis

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Genomic Data Analysis: Techniques and Applications

Covers gene expression measurement techniques and analysis methods in genomics.

Genomic Data Analysis: Clustering and Survival

Explores genomic data clustering, survival analysis, gene identification, and statistical significance in cancer research.

Kernel K-means Clustering

Explores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.

Single-cell and Spatially Resolved RNA-seq

Explores single-cell RNA-seq, tissue dissociation, capture methods, and differential gene expression analysis.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

DNA: PCR and Sequencing Techniques

Explores DNA engineering through PCR, Sanger sequencing, the Human Genome Project, recombinant DNA, bacterial DNA manipulation, and CRISPR/Cas9.

Bulk Transcriptome Analysis: RNA-seq Workflow and Gene Expression Analysis

Explains transcriptomics, RNA-seq workflow, and gene expression analysis for disease biomarkers.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Cluster Analysis: Methods and Applications

Explores cluster analysis methods and applications in genomic data analysis, covering classification, gene expression clustering, visualization, distance metrics, and clustering algorithms.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.