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2019 STAT115 Lect2.1 Microarrays
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2019 STAT115 Lect2.2 Quantile Normalization
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2019 STAT115 Lect2.3 RMA
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2019 STAT115 Lect2.4 RMA Demo
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2019 STAT115 Lect4.1 Hierarchical Clustering
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2019 STAT115 Lect4.2 K-means Clustering
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2019 STAT115 Lect4.3 Batch Effect Correction
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2019 STAT115 Lect4.4 Gene Ontology
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2019 STAT115 Lect4.5 Gene Set Enrichment Analyses
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2019 STAT115 Lect5.1 Unsupervised Learning
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2019 STAT115 Lect5.2 Principal Component Analysis
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2019 STAT115 Lect5.3 Logistic Regression
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2019 STAT115 Lect5.4 Supervised Learning
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2019 STAT115 Lect6.1 Sequencing
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2019 STAT115 Lect6.2 FASTQC
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2019 STAT115 Lect6.3 Blast and Suffix Tree / Array
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2019 STAT115 Lect6.4 Burrows-Wheeler Alignment
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2019 STAT115 Lect6.5 SAM, BAM, and BED Files
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2019 STAT115 Lect7.1 Intro to RNA-seq
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2019 STAT115 Lect7.2 RNA-seq QC
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2019 STAT115 Lect7.3 RNA-seq Expression Index
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2019 STAT115 Lect7.4 DESeq2
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2019 STAT115 Lect8.1 Intro to scRNA-seq
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2019 STAT115 Lect8.2 QC and Visualize scRNA-seq
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2019 STAT115 Lect8.3 Cluster and Annotate scRNA-seq
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2019 STAT115 Lect8.4 Differential Expression on scRNA-seq
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2019 STAT115 Lect9.1 scRNA-seq Continued
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2019 STAT115 Lect9.2 Gene Expression Module Summary
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2019 STAT115 Lect9.3 Expression Analyses Case Studies
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2019 STAT115 Lect10.1 Transcription regulation
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2019 STAT115 Lect10.2 Motif Finding Using Expectation Maximization
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2019 STAT115 Lect10.4 Gibbs Sampler Intuitions
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STAT115 Chapter 10.5 Motif Conservation and Modules
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2019 STAT115 Lect11.1 ChIP-seq
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2019 STAT115 Lect11.2 ChIP-seq Peak Calling and QC
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2019 STAT115 Lect11.3 ChIP-seq Motif Finding and Collaborating TFs
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2019 STAT115 Lect11.4 ChIP-seq Target Identification
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2019 STAT115 Lect12.1 Intro to Epigenetics
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2019 STAT115 Lect12.2 DNA Methylation
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2019 STAT115 Lect12.3 Analyses of DNA Methylome
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2019 STAT115 Lect12.4 Nucleosome Positioning
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2019 STAT115 Lect14.1 Chromatin Interaction HiC
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2019 STAT115 Lect14.2 3D Genome Structure
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2019 STAT115 Lect14.3 3D Genome Dynamics
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2019 STAT115 Lect14.4 Gene Regulatory Network
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2019 STAT115 Lect15.1 SNPs, LD and Haplotypes
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2019 STAT115 Lect15.2 Association Studies
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2019 STAT115 Lect15.3 Issues on GWAS Studies
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2019 STAT115 Lect15.4 eQTL Analyses
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2019 STAT115 Lect16.1 EWAS Annotate RSNPs
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2019 STAT115 Lect16.2 Module II Summary
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2019 STAT115 Lect16.3 Gene Regulation Case Studies
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2019 STAT115 Lect17.1 Intro to Hidden Markov Model
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2019 STAT115 Lect17.2 HMM Forward and Backward Procedures
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2019 STAT115 Lect17.3 HMM Viterbi Algorithm
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2019 STAT115 Lect18.1 Overview of Cancer Genetics
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2019 STAT115 Lect18.2 Tumor Mutation Calling
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2019 STAT115 Lect18.3 Annotating Tumor Mutations
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2019 STAT115 Lect18.4 Test Driver Mutations
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2019 STAT115 Lect19.1 Cancer Subtyping
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2019 STAT115 Lect19.1 Cancer Subtyping
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2019 STAT115 Lect19.2 Basic Survival Analysis
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2019 STAT115 Lect19.3 Cancer Mutation Types and Distributions
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2019 STAT115 Lect19.4 Cancer Genomics Resources
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2019 STAT115 Lect20.1 Cancer Targeted Therapy
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2019 STAT115 Lect20.2 Cancer Drug Resistance
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2019 STAT115 Lect20.3 Overcome Drug Resistance
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2019 STAT115 Lect20.4 Synthetic Lethality
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2019 STAT115 Lect21.1 Introduction of Cancer Immunotherapy
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2019 STAT115 Lect21.2 Neoantigen Presentation
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2019 STAT115 Lect21.3 Tumor Immune Infiltration
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2019 STAT115 Lect21.4 Tumor Immune Repertoires
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2019 STAT115 Lect22.1 Tumor Immune Cell Activation
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2019 STAT115 Lect22.3 CRISPR Screens
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2019 STAT115 Lect23.1 CRISPR Screen Analyses
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2019 STAT115 Lect23.2 Cancer Epigenetics and CIMP
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2019 STAT115 Lect23.3 HDAC and 5-AZA in Cancer
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2019 STAT115 Lect23.4 CRISPR Screen and Epigenetic Regulators of Cancer Drug Response
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2019 STAT115 Lect23.5 Personalized Combination Cancer Therapy
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2019 STAT115 Lect24.1 Module III Review
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2019 STAT115 Lect24.2 Course Summary
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2019 STAT115 Lect24.3 Final Review
Description:
Explore a comprehensive lecture video series from Harvard University's spring 2019 STAT115/215 and BIOSTAT/BST282 courses on statistical and computational methods for analyzing genomic data. Delve into topics ranging from microarray analysis and clustering techniques to RNA sequencing, epigenetics, and cancer genomics. Learn about cutting-edge bioinformatics tools and methodologies, including principal component analysis, hidden Markov models, and CRISPR screens. Gain insights into gene regulation, transcription factors, and chromatin interactions. Discover approaches for analyzing cancer mutations, subtypes, and immunotherapy. Access additional course materials through the provided Harvard Canvas link for a deeper understanding of computational biology and bioinformatics.

STAT115

Harvard University
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#Data Science #Bioinformatics #Mathematics #Statistics & Probability #Science #Biology #Epigenetics #Computer Science #Machine Learning #Clustering #RNA-Seq #Sequencing
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2019 STAT115 Lect2.1 Microarrays