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MA plot
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MA plots in next generation sequencing !
MD BABU MIA, PHD
#Video Tutorial link: https://youtu.be/ujzSm2Jc5zA
# Introduction
MA plots visualize the relationship between log2 fold change (M values) and mean of normalized counts (A values) for differential expression analysis results. First introduced for microarray interpretation, MA plots effectively translate to RNA sequencing paradigms including bulk and single-cell techniques.
By revealing overall patterns between genes measured effect sizes and average expression, MA plots help assess normalization adequacy, search for biases, and identify highly influential outlier genes meriting further investigation. Although powerful on their own, MA plots meaningfully supplement volcano plots and heatmap representations to provide multifaceted graphical data exploration.
# Mathematical Rationale
# Derivation of Axes
The x-axis (A) in MA plots represents the mean of normalized counts across all samples for each gene. Mathematically, this is computed as:
A = (Mean of Treatment Samples + Mean of Control Samples) / 2
The y-axis (M) captures the log2 transformed fold change between experimental conditions. This can be derived as:
M = Log2(Mean of Treatment Samples / Mean of Control Samples)
Thus, the axes convey complementary statistics illuminating genes' expression distributions.
# Expected Distributions
Under a null hypothesis of no differential expression, most genes' log2 fold changes should center tightly around 0. However, genes with higher absolute M values exhibit greater differential expression between conditions. An ideal MA plot will show the majority of genes along M=0 and outliers stretching towards the graph peripheries.
# Real Data Analysis Using DESeq2
Watch the video tutorial on YouTube.
# Discussion and Interpretation
Watch the video tutorial on YouTube.
# Conclusions
In the bulk RNA-seq data analysis, MA plots offer a powerful means of identifying genes that exhibit significant expression changes under different experimental conditions, thereby providing insights into the biological mechanisms at play.
References:
Love, M.I., Huber, W. and Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology, 15(12), pp.1-21.
Robinson, M.D., McCarthy, D.J. and Smyth, G.K., 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. bioinformatics, 26(1), pp.139-140.
Ritchie, M.E., Phipson, B., Wu, D.I., Hu, Y., Law, C.W., Shi, W. and Smyth, G.K., 2015. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research, 43(7), pp.e47-e47.