Glycomic Data Visualization Interactive Tool

Introduction

The Glycomic Data Visualization Interactive Tool is a set of R Shiny-based applications designed for the visual exploration of glycomic data in cancer research. It enables researchers to analyse glycomic and glycosylation-related transcriptomic datasets from colorectal cancer (CRC) and acute myeloid leukemia (AML) cell lines, as well as glycomic data from CRC tumour tissues. By integrating multiple datasets, this tool provides an intuitive interface for identifying patterns and relationships across glycosylation traits and transcriptomic profiles. The datasets included are:

• Cell Line Glycomics: N-glycomic, O-glycomic, and glycosphingolipid (GSL) glycomic data from 26 CRC cell lines and 21 AML cell lines, generated using porous graphitized carbon nanoliquid chromatography-MS/MS. For integration, glycosylation traits and motifs identified within the datasets were summed, generating composite, integrated glycosylation traits.
• Cell Line Transcriptomics: Transcriptomic data (RNA-seq) in Transcripts Per Million (TPM) and metadata from CRC and AML cell lines, retrieved from the Cancer Dependency Map (DepMap).
• Colorectal Cancer Glycomics: N- and O-glycomic data obtained from paired primary tumours and adjacent normal colon mucosa tissues from 12 CRC patients, liver metastases from 6 additional CRC patients, and stromal regions of cancers with high stromal content. The data was generated using laser capture microdissection (LCM) of formalin-fixed paraffin-embedded (FFPE) tissues, followed by porous graphitized carbon liquid chromatography-MS/MS analysis. The tool complements the classical analysis, especially in the early stages of research when hypotheses are being generated. Researchers can selectively visualize and filter combinations of glycosylation traits, identify trends across different biological contexts, and integrate glycomic and transcriptomic data for a more comprehensive understanding of cancer biology.

Installation, Configuration, and Dependencies

Note: In its current form, the tool can be run locally using R and the R Shiny package. Since it will be hosted on the department’s external server, this section will be finalized after deployment.

How to use

The application consists of three distinct tools, each corresponding to a specific dataset: two for cell lines (glycomics and transcriptomics) and one for CRC tissue glycomics. Users can select and explore a dataset via the side menu. Once the dataset is chosen, users can filter the data based on specific attributes and decide whether to display raw or scaled values. The filtered data is then visualised using a geometric tile plot that shows the distribution of glycan traits and glycosylation-related genes across samples. The scaling uses min-max normalisation—adjusting each column to a range of [0, 1]—to ensure a consistent scale across the dataset. Since each dataset contains different types of information, the available input options vary accordingly:

1. Cell Lines Glycomics:

• Data: N-, O-, and GSL glycomic data from CRC and AML cell lines.
• Filter options: Glycosylation class (N, O, GSL), cancer type (AML or CRC), specific glycan traits, and the cell line.

2. Cell Lines Transcriptomics

• Data: RNA-seq (TPM) data for CRC and AML cell lines.
• Filter options: Glycosyltransferase class, cancer type (AML or CRC), specific glycosyltransferase gene, and the cell line.

3. Colorectal Cancer Glycomics:

• Data: N- and O-glycomic data from CRC tissues.
• Filter options: Tissue source (primary tumour or metastasis), tissue type (normal, tumour or stroma), glycosylation class (N or O), specific glycan traits and the patient codes.

Troubleshooting

• Known Issues
  • To complete
• Workarounds
  • To complete

Contact Information

For inquiries, questions, or to report issues, please contact:

• [Name]
• Email:
• Institution: