An intuitive and user-friendly web application to explore Raman maps
RamApp is a web application that aims to provide a user-friendly solution allowing researchers from various backgrounds to easily and successfully explore hyperspectral data, with a focus on Raman imaging
Import data obtained from different sources, including the most diffused commercial software
RamApp covers the most-used tools and techniques for Raman maps: from cropping and smoothing to spikes detection and baseline correction
Analyse Raman maps using statistical data analysis methods such as clustering, PCA, multivariate curve resolution (MCR) and N-FINDR
Create, as individual or stacked images, intensity maps with a high level of customization
Export publication-quality images or raw data for further analysis
Interactively explore and visualise the data and the results of each processing step with a fast and easy work environment
Once uploaded, your data will be stored in a safe and personal space
Access the app from any browser and operating system without requiring a local installation
RamApp was recently presented at the 12th International Conference on Advanced Vibrational Spectroscopy.
We were thrilled to attend a conference in Dublin to show our most recent app development.
Here are the main people who contributed to the development of RamApp.
We are currently in the process of writing a paper that will detail the development and functionality of our application. In the meanwhile, you can directly cite our website as a reference.
Vanna, R., Masella, A., Bazzarelli, M., Ronchi, P., Lenferink, A.T.M., Tresoldi, C., Morasso, C., Bedoni, M., Cerullo, G., Polli, D., et al. (2024). High-resolution Raman imaging of >300 cells from human patients affected by nine different leukemia subtypes: a global clustering approach. Preprint at ChemRxiv, 10.26434/chemrxiv-2024-xpzhc
Karlo, J., Gupta, A., and Singh, S.P. (2024). In situ monitoring of the Shikimate pathway: A combinatorial approach of Raman reverse stable isotope probing and hyperspectral imaging. Preprint at ChemRxiv, 10.26434/chemrxiv-2024-gt124
Vernuccio, F., Vanna, R., Ceconello, C., Bresci, A., Manetti, F., Sorrentino, S., Ghislanzoni, S., Lambertucci, F., Motiño, O., Martins, I., et al. (2023). Full-Spectrum CARS Microscopy of Cells and Tissues with Ultrashort White-Light Continuum Pulses. J. Phys. Chem. B 127, 4733–4745. 10.1021/acs.jpcb.3c01443
Ardini, B., Bassi, A., Candeo, A., Genco, A., Trovatello, C., Liu, F., Zhu, X., Valentini, G., Cerullo, G., Vanna, R., et al. (2023). High-throughput multimodal wide-field Fourier-transform Raman microscope. Optica, OPTICA 10, 663–670. 10.1364/OPTICA.488860
Ghislanzoni, S., Kang, J.W., Bresci, A., Masella, A., Kobayashi-Kirschvink, K.J., Polli, D., Bongarzone, I., and So, P.T.C. (2023). Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells. Biosensors 13, 973. 10.3390/bios13110973
Don't hesitate to contact us if you have more questions
Start exploring the potential of RamApp now.
