Single cell RNA-sequencing: Methods and Data Analysis

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Related lectures (32)

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

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Explores tools and models for Next-Generation Sequencing data analysis, covering DNA sequencing technologies, data analysis pipelines, and statistical models.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Methods to Identify Transcriptomic Types

Covers methods to identify transcriptomic types and classify bipolar cells using computational techniques and molecular profiling.

K-means Algorithm

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.

Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Clusters and Communities

Explores clustering, community detection, K-means, GMM, modularity, and the Louvain method.

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

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