The 'Signature Dist' visualization shows the fraction of signatures within individual samples. It can also be used to show the fraction of signatures within several cancer types.
Signature Dist Data (CSV file)
The uploaded CSV file must match the required format as specified below.
The input is mutation signature compositions in batch of samples using NMF algorithm, e.g., decipherMutationalSignatures.
Check the official demo input here.
The first line of the file should be a header that contains column names as keys. The header should follow the following format:
Observationstakes the values of the names of signatures.
T01is the name of an individual sample. The number of keys is not limited.
Each row in the file is the measure of a specified signature in each individual sample. Note that this measure does not have to be normalized to 100%.
There are three types of interactions: Highlights, Scrolling and Download.
The bin being pointed to will be highlighted.
When the samples cannot be fitted in one page, a scroller will appear in the bottom and user can drag the scroller to view different samples.
One SVG file will be generated when the 'Download' button is clicked. The SVG file only captures the current view determined by the scroller.
The sidebar provides options to manage files and reorder samples.
- Manage Files: checklist of CSV files uploaded previously, delete or download the CSV files.
- Upload: upload Signature Dist CSV file. Note that the duplicated file name will be alerted and given a random postfix.
- Choose: choose files uploaded previously. Note that this function is ONLY available to registered users (each account has certain storage).
- File Sets: NOT available to this page.
In settings, user can reorder samples by ascending or descending order of the sample name or the fraction of a certain signature.
Manual version=1.1, written by Miss. LI Shiying and Dr. JIA Wenlong on 2020-02-06.
- SZPIECH, Z. A., STRAULI, N. B., WHITE, K. A., RUIZ, D. G., JACOBSON, M. P., BARBER, D. L. and HERNANDEZ, R. D. (2017). Prominent features of the amino acid mutation landscape in cancer. PLoS One, 12(8):e0183273. (PMID: 28837668, See Figure 2)
- Zhang, L., Zhou, Y., Cheng, C., Cui, H., Cheng, L., Kong, P., ... & Wang, F. (2015). Genomic analyses reveal mutational signatures and frequently altered genes in esophageal squamous cell carcinoma. The American Journal of Human Genetics, 96(4), 597-611. (PMID: 25839328, See Figure 1B)
- Li, X., Wu, W. K., Xing, R., Wong, S. H., Liu, Y., Fang, X., ... & Zhou, Y. (2016). Distinct subtypes of gastric cancer defined by molecular characterization include novel mutational signatures with prognostic capability. Cancer research, 76(7), 1724-1732. (PMID: 26857262, See Figure 1C)
- Fujimoto, A., Furuta, M., Totoki, Y., Tsunoda, T., Kato, M., Shiraishi, Y., ... & Gotoh, K. (2016). Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer. Nature genetics, 48(5), 500. (PMID: 27064257, See Figure 2b)
- Chang, J., Tan, W., Ling, Z., Xi, R., Shao, M., Chen, M., ... & Xia, Y. (2017). Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations. Nature communications, 8, 15290. (PMID: 28548104, See Figure 1c)