Generation and Analysis of Host Transcriptomics and Development of Host‐Response Based Signatures

This chapter focuses on laboratory techniques for generating transcriptomic data and bioinformatics approaches for analyzing the complex high-dimensional datasets, and highlights important considerations in transcriptomic study design, data sharing, and translation to a clinically useful test. Sample type is also a key consideration in study design. There should be a balance between feasibility and biological and clinical relevance. Sample size estimates and power analyses can be more challenging for high-dimensional transcriptomic data. RNA extraction should be carried out on RNase-free surfaces and using nuclease-free laboratory reagents and equipment. Differential expression analysis identifies genes that exhibit statistically significant expression changes in response to a particular condition, providing information on the host's transcriptional response.