PubMed · 14970453
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Richard A Shimkets. 2004. Technical considerations in quantitating gene expression.. https://doi.org/10.1385/1-59259-751-3%3A1
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STALARD (selective target amplification for low-abundance RNA detection) enables isoform-level quantification of low-abundance RNAs using conventional laboratory equipment. Here, we describe steps for RNA isolation, primer design, reverse transcription, selective target amplification, and downstream analysis. The protocol couples selective pre-amplification with a quantitative reverse-transcription PCR (RT-qPCR) readout and optional nanopore sequencing. Using 1 μg input RNA and 12 pre-amplification cycles, STALARD reduces Cq values by approximately 10-12 cycles, bringing the target into a reliably quantifiable range. For complete details on the use and execution of this protocol, please refer to Jeong et al.1.
Principal component analysis (PCA) has been widely used in multivariate data analysis to reduce the dimensionality of the data in order to simplify subsequent analysis and allow for summarization of the data in a parsimonious manner. It has become a useful tool in microarray data analysis. For a typical microarray data set, it is often difficult to compare the overall gene expression difference between observations from different groups or conduct the classification based on a very large number of genes. In this paper, we propose a gene selection method based on the strategy proposed by Krzanowski. We demonstrate the effectiveness of this procedure using a cancer gene expression data set and compare it with several other gene selection strategies. It turns out that the proposed method selects the best gene subset for preserving the original data structure.
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