382 Online graphical interface for bulk to single-cell transcriptomics

Abstract

Objectives/Goals: We aim to develop an intuitive interface to understand the possible relationships between data from different RNA-Seq technologies. It can help novice users and educators to understand, analyze, explain, and visualize such datasets from diverse platforms, all without the need for additional software installations or strong programming expertise. Methods/Study Population: An online interactive interface is developed, integrating robust algorithms for three distinct types of analyses: DESeq2 for bulk RNA-seq, CIBERSORTx for deconvolution, and Seurat for single-cell analysis, with plans to include more algorithms. It allows a demo mode for training using the sample datasets and option for tailored analysis using user’s partially processed data. The interface provides capability to process bulk RNA-seq data from raw counts or a differential gene list. Further, deconvolution analysis for bulk RNA-seq data can be done using raw counts and single-cell data analysis can be performed using processed sequence reads, organized into three key files: barcodes, matrix, and features. Users also have an option to download the results as a zipped file, for samples from human and mouse studies. Results/Anticipated Results: Users with an active internet connection can access the interface from any major web browser. They can adjust parameters – such as genome type, cutoff thresholds, and batch effect correction – according to their specific needs. Bulk RNA-seq results are presented in the form of volcano plots, heat-maps, clusters, gene expression across samples, DEGs, and enrichment plots from KEGG and GO analyses. Deconvolution analysis can be performed using either the “LM22” signature matrix (for human leukocyte cell types) or Derm22 (for skin-specific cell types). The single-cell workflow provides results including quality control metrics, UMAP clustering, gene expression plots/tables, and cluster annotation using CellTypist. Comprehensive details on methods and tutorials are available in the GitHub repository. Discussion/Significance of Impact: Although multiple workflows are available to process bulk and single cell RNA-Seq data along with deconvolution methods to bridge the gap between the two, this is the first online interface to provide the capability to explore and analyze data from all three approaches in one place, without requiring strong computational expertise or resources.