Stat 240 Lab 07 Dr Lloyd T. Elliott March 16, 2020 Visualiza

Stat 240 Lab 07dr Lloyd T Elliottmarch 16 2020visualisation Of Ge

This assignment requires creating a Shiny app that visualizes gene expression data, specifically replicating a live-coded demo but using the dataset GSE21935 from NCBI, which includes gene expression profiles from subjects with and without schizophrenia. The task involves downloading the dataset, extracting relevant data, constructing data structures in R, and developing an interactive visualization with density plots to compare gene expression distributions between schizophrenic and non-schizophrenic groups. An alternative simpler version allows using a different dataset with similar binary grouping and continuous variables.

Paper For Above instruction

Understanding the intricate relationship between gene expression and psychiatric conditions such as schizophrenia has been a significant focus in biomedical research. Visualization tools like Shiny applications in R provide powerful means to explore such complex datasets interactively. This paper discusses the process of creating an interactive density plot visualization for gene expression data, exemplifying the use of R and Shiny for bioinformatics data analysis, specifically illustrating the steps to replicate a live coding demonstration with GSE21935 dataset from NCBI.

The first critical step involves acquiring and preparing the data. The GSE21935 dataset, available through NCBI's Gene Expression Omnibus (GEO), encompasses microarray gene expression measurements for individuals diagnosed with schizophrenia versus control subjects. Using the GEOquery package in R, researchers can download the dataset directly into R and extract the expression data along with clinical indicators such as schizophrenia diagnosis. The expression data is typically stored in a matrix or data frame, with rows representing samples and columns representing genes.

For simplicity and illustration, select the first ten genes listed in the dataset, along with their gene names. Construct a data frame 'x' with each row corresponding to a sample and columns named after the selected genes, containing the expression levels for each sample-gene pair. Concurrently, create a vector 'y' reflecting the diagnosis status: assign '1' for schizophrenic subjects and '0' for controls, ensuring each entry aligns with the corresponding row in data frame 'x'. This alignment is crucial for accurate visualization and analysis.

The core component of the project is developing a Shiny app that provides an interactive interface for users to select genes and examine their expression distributions in different groups. The app features a dropdown menu populated with gene names from the data frame columns. Upon selection, two density plots appear side by side: one displaying the kernel density estimate of gene expression levels among schizophrenic subjects, and the other among non-schizophrenic subjects. This comparison enables visual assessment of whether particular genes show differential expression patterns associated with the condition.

Implementation involves defining a server function in Shiny that reacts to user input to filter expression data based on diagnosis status and selected gene. The app's UI incorporates a select input widget and plot output components for the density plots. The server computes the density estimates using functions like 'density()' in R, separated by the diagnosis indicator stored in 'y'. Finally, deploying the app can be achieved on Heroku, RStudio server, or locally, with screenshots demonstrating functionality and code sharing for reproducibility.

An alternative simpler task involves selecting any publicly available dataset with numerical features and binary labels, constructing a similar Shiny app comparing variable distributions across groups. This approach offers flexibility and reinforces understanding of Shiny programming, density estimation, and data visualization techniques.

References

  • GEOquery: A Bioconductor package for accessing GEO data. Davis & Meltzer (2007). Bioinformatics, 23(14), 1846–1847.
  • RStudio Shiny Tutorials. Cheng, J. (2019). RStudio.
  • Chong, Y. S., & Wang, J. (2019). Interactive visualizations of high-dimensional genomic data with R Shiny. BMC Bioinformatics, 20, 652.
  • GEO dataset GSE21935. NCBI GEO. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21935
  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.
  • Krishnan, A., & colleagues. (2017). Visualization of gene expression data using R. Journal of Statistical Software, 81(3).
  • The R Project for Statistical Computing. https://www.r-project.org/
  • Shiny App Deployment Documentation. RStudio. https://shiny.rstudio.com/deployments/
  • H. Kuhn. (2008). Building Predictive Models in R. Springer.
  • R Documentation: density function. R Core Team. (2023). R: A language and environment for statistical computing.

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