In this tutorial, we dive into single-cell RNA sequencing data analysis using Monocle 3, guiding you through the process of single-cell trajectory analysis.
Rather than purifying cells into discrete states experimentally, Monocle 3 uses an algorithm to learn the sequence of gene expression changes each cell undergoes as part of dynamic biological processes.
We leverage Compu-Flair’s search engine to efficiently find reliable data analysis pipeline for our bioinformatics needs. Here’s a breakdown of the steps covered in the video:
Pipeline Selection: Using Compu-Flair to find and select the most relevant analysis pipeline.
Metadata Examination: Reviewing the metadata of our single-cell dataset.
Pre-processing: Pre-processing the data, including batch corrections, using the first 50 dimensions.
Dimensionality Reduction: Performing PCA and plotting initial results.
Gene Expression Visualization: Plotting and analyzing the expression of specific genes.
Clustering and Graph Learning: Clustering cells, learning developmental trajectories, and plotting the results.
Pseudo-time Ordering: Ordering cells in pseudo-time and selecting root nodes through a graphical user interface.
Branch Analysis: Subsetting cells based on branches in the developmental trajectory.
3D Visualization: Creating a three-dimensional visualization of pseudo-time-ordered cells.
Throughout the tutorial, we demonstrate how to use ChatAI for quick answers and code explanations, enhancing our analysis capabilities. This video is perfect for researchers and bioinformaticians looking to apply Monocle 3 for insightful single-cell trajectory analysis.
CompuFlair Search Engine for Bioinformatics Pipelines:
www.compu-flair.com
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#SingleCellRNASeq #Monocle3 #CompuFlair #Bioinformatics #DataAnalysis #Tutorial #SingleCellAnalysis #GeneExpression #PseudoTime #3DVisualization
18 июн 2024
