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Scale and intent of the experiment
The scale of an experiment encompasses both the number of samples and the number of genes to be analysed (9).
The number of samples is often a trade off between:
- the number of replicates required to produce statistically robust results
- the ease of obtaining the samples
- the budget for the project
The scale and intent of the experiment will influence the methods used in the study:
- Real-time PCR analysis is well suited to studies that aim to analyse a small number of genes in a small number of samples (for example, the change in expression of ten genes between two conditions)
- Microarrays are a high-throughput technique to monitor the expression of all the genes already known in a given genome. They have been vastly used to study gene expression before the introduction of RNA-sequencing (RNA-seq, see following point) in the first half of the 2010s; while RNA-seq has replaced microarrays for most transcriptional profiling studies, DNA arrays are still used in diagnostics for SNP detections (SNP array)
- RNA-sequencing (RNA-seq) using next generation sequencing (NGS) is the current standard for high-throughput gene expression studies. By sequencing all the transcripts present in the samples (cell, tissue, whole organism, soil samples, etc), RNA-seq allows for in-depth analyses and is best suited for discovery based projects such as those that aim to identify new transcripts, study non-coding RNAs, map transcription start sites or characterise the precise location of epigenetic modifications.
Other considerations
For each type of experiment there will be further considerations including:
- the technology (Taqman vs Sybr green PCR; one vs two colour microarrays, paired-end vs single-end sequencing, short-read vs long-read sequencing)
- the platform (Affymetrix vs Agilent for microarrays, Illumina vs Oxford Nanopore vs PacBio for sequencing, etc)
- the methods that are used to prepare the samples (such as sample conservation, DNA/RNA extraction, and library preparation)