RSQLite

RSQLite provides an interface between the SQLite database engine and the R programming environment to store, retrieve, and manipulate large-scale genomic datasets for downstream bioinformatics analyses.

Key Features:

• Database integration: Embeds the SQLite engine within R and implements the DBI interface to enable SQL-based storage and querying of data inside R.
• Genomic data handling: Facilitates efficient storage, retrieval, and manipulation of large-scale genomic datasets for analysis workflows.
• Compatibility with Bioconductor packages: Interoperates with R packages such as ggplot2 and org.Hs.eg.db for visualization and gene annotation tasks.
• Support for downstream analyses: Enables workflows that include identification of differentially expressed genes (DEGs), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway associations, protein–protein interaction (PPI) network construction, multivariate Cox regression, Kaplan–Meier survival analysis, and receiver operating characteristic (ROC) curve generation.

Scientific Applications:

• Smoking-associated lung adenocarcinoma analysis: Applied to analyze 433 smoking-associated lung adenocarcinoma samples and 75 non-smoking counterparts from The Cancer Genome Atlas (TCGA) to identify molecular differences.
• KEGG pathway analysis: Employed to perform KEGG pathway analyses for interpretation of disease-related biological pathways.
• Biomarker discovery and prognosis evaluation: Used to identify key biomarkers and assess clinical significance via multivariate Cox regression, Kaplan–Meier survival curves, and ROC curves for five-year overall survival.

Methodology:

Using TCGA data (433 smoking-associated lung adenocarcinoma samples and 75 non-smoking counterparts), RSQLite was used to identify differentially expressed genes (DEGs), associate DEGs with Gene Ontology (GO) functions and KEGG pathways, construct a protein–protein interaction network, identify key biomarkers, and perform multivariate Cox regression, Kaplan–Meier survival analysis, and ROC curve evaluation.