Tag: PCA
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How to Analyze RNAseq Data for Absolute Beginners Part 3: From Count Table to DEGs – Best Practices
Video Tutorial As we move forward in our RNAseq analysis journey, we’ll be transitioning from the Linux environment to R, a powerful and versatile statistical analysis tool. R is not only a programming language but also a platform widely used in data science, statistical computing, and predictive modeling. Tech giants like Microsoft, Meta, Google, Amazon,…
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Recent Posts
- How To Analyze ATAC-seq Data For Absolute Beginners Part 3: Footprinting Analysis
- How To Analyze ATAC-seq Data For Absolute Beginners Part 2: Differential Binding Analysis Using DiffBind
- How To Analyze ATAC-seq Data For Absolute Beginners Part 1: From FASTQ To Peaks
- How To Analyze ChIP-seq Data For Absolute Beginners Part 5: Mastering Reproducibility With IDR Analysis
Tags
Adapter Trimming Alternative Splicing Analysis ATAC-seq BAM ChIP-seq chromatin accessibility Conda Environment Setting Count DESeq2 Differential Expression edgeR FASTQ gene expression Gene Expression Quantification HOMER HPC Isoform limma MACS2 miRNA miRNA-seq Normalization PCA peak calling R Reads Mapping RNAseq analysis RNAseq analysis for beginners RStudio Transcript