Analysis of transcriptomics data involves several steps:
Data Preprocessing: Quality control, normalization, and filtering of raw data. Differential Expression Analysis: Identifying genes that are differentially expressed between conditions or groups. Functional Annotation: Assigning biological meaning to genes via gene ontology and pathway analysis. Integration with Other Omics: Combining transcriptomics data with genomics, proteomics, and metabolomics data for a holistic view of biological systems. Machine Learning: Using algorithms to predict disease outcomes and identify patterns in large datasets.
