课程
Differential Expression Analysis with limma in R
高级技能水平
更新时间 2024年8月
RProbability & Statistics4小时15 视频47 道练习3,900 XP8,122成就证明
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先决条件
Introduction to Statistics in R1
Differential Expression Analysis
To begin, you'll review the goals of differential expression analysis, manage gene expression data using R and Bioconductor, and run your first differential expression analysis with limma.
2
Flexible Models for Common Study Designs
In this chapter, you'll learn how to construct linear models to test for differential expression for common experimental designs.
3
Pre- and post-processing
Now that you've learned how to perform differential expression tests, next you'll learn how to normalize and filter the feature data, check for technical batch effects, and assess the results.
4
Case Study: Effect of Doxorubicin Treatment
In this final chapter, you'll use your new skills to perform an end-to-end differential expression analysis of a study that uses a factorial design to assess the impact of the cancer drug doxorubicin on the hearts of mice with different genetic backgrounds.
Differential Expression Analysis with limma in R
课程完成 加入超过19百万学习者，今天就开始Differential Expression Analysis with limma in R！
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