RNA sequencing provides quantitative data on arbitrary transcript abundance, while microarrays only detect predefined transcripts

Microarrays rely on hybridization to complementary probes, while RNA sequencing uses direct sequencing of cDNA

Both RNA sequencing and microarrays generate  raw data that, analyzed with the proper bioinformatics tools, provide information about arbitrary sequences on the sample. 

Microarrays can detect novel transcripts not included in the probe design, whereas RNA sequencing cannot

RNA sequencing has a higher dynamic range for detecting transcripts compared to microarrays

GSEA evaluates the collective behavior of a group of genes, rather than analyzing genes individually

GSEA can reveal biological processes and pathways that may not be evident from individual gene-level analysis.

GSEA identifies whether predefined gene sets (e.g., pathways) are significantly enriched in the ranked list of genes from an experiment

GSEA cannot be applied to RNASeq studies

GSEA requires setting a hard threshold for defining differentially expressed genes before performing the analysis

RNA-Seq cannot detect alternative splicing because it only measures total gene expression

RNA-Seq detects alternative splicing events by mapping sequencing reads to the genome and identifying splice junctions.

RNA-Seq reveals alternative splicing by directly sequencing proteins produced by spliced transcripts

RNA-Seq identifies alternative splicing exclusively in protein-coding genes

Functional analysis directly measures the protein activity levels in the cell

Pathway enrichment analysis detects overrepresented pathways, such as metabolic or signaling pathways, in the dataset.

Functional analysis predicts secondary structures of RNA molecules to determine their stability

Gene Ontology (GO) analysis identifies biological processes, molecular functions, and cellular components associated with differentially expressed genes

Differential expression analysis is used to group genes with similar expression patterns and infer their functions indirectly

Functional analysis applies to a specific gene expression across all the sample of patients