You will get a comprehensive R code (800 lines) for microbiome 16S data analysis.
The journey began with a passion for data science and bioinformatics coupled with a commitment to streamlining the analysis of 16S rRNA meta-barcoding data. My expertise flourished in this domain, nurtured by hands-on experience gained within a hospital research laboratory. It was during this time that I pursued and completed a second-level vocational master's program in ioinformatics and fuFnctional Genomics, further solidifying my knowledge and skills in the field.
Unlock the power of microbiome 16S data with this comprehensive R code, meticulously designed for bioinformaticians, researchers, and data enthusiasts. This exclusive bundle provides a step-by-step analysis accompanied by an integrated results PDF, offering a visual representation of the complete data processing lifecycle.
You will get:
- complete R code to run an analysis starting from the raw OTU table, metadata, tree and taxonomy files;
- R code to run specifically correlation analysis between features and clinical metadata continuous variables;
- 2 PDF files that offer a visual representation for the R code with integrated example results.
You will embark on a journey starting from installing essential packages and loading libraries to importing data and creating the phyloseq object. Dive into the exploration of summary statistics, navigate through normalization via rarefaction, and delve into taxonomic relative abundance analysis, complete with statistical comparisons.
Correlate taxa relative abundances with continuous variables in metadata through richly detailed correlation heatmaps. Explore richness analysis featuring Shannon, observed OTUs, Simpson, and Chao1 indexes, accompanied by box plots and Kruskal-Wallis tests for group comparisons.
Immerse yourself in multivariate analysis utilizing Jaccard or Bray Curtis distance, culminating in NMDS ordination. The comprehensive analysis includes Permanova and permdisp tests, providing insights into homogeneity of dispersions, with Tukey multiple comparisons of means highlighting specific pairwise differences.
Conclude the analysis by delving into Differentially Abundant features across sample groups through LEfSe, pinpointing potential biomarkers. Employ a Supervised Machine Learning approach like Logistic Regression to unveil the most important features, effectively distinguishing between two classes such as healthy and diseased states.
This bundle leverages specialized microbiome analysis libraries, ensuring accuracy and reliability throughout the exploration.
Tailor the analysis to your specific parameters, and you'll swiftly commence interpreting results.
Equip yourself with a powerful toolkit that goes beyond analysis, providing an enriching and visually compelling experience for understanding 16S data.
You will get a comprehensive R code for microbiome 16S data analysis based on phyloseq, that allows you to independently execute tasks such as: from installing necessary packages to detailed exploration, normalization, and statistical comparisons; the code is tailored for in-depth analysis. Uncover taxonomic trends, explore correlations, and delve into metrics of alpha and beta diversity effortlessly.
Whether you're a professional or researcher working with bioinformatics, a PhD or graduate student pursuing studies in microbiology, bioinformatics or related fields, you will streamline your research processes and gain insights into microbiome data analysis. If you're an educator teaching bioinformatics or microbiome-related courses you may use this product as a teaching resource for practical applications:
- highlight community structures, identify key taxa and conduct statistical analyses;
- explore real-world applications of bioinformatics in microbiome studies;
- time-saving resource for students engaged in microbiome research projects, who want to develop a solid understanding of data analysis techniques and methods;
- bridge theoretical concepts with hands-on, practical applications.
