*Only the First Five rows are shown
shinySISPA is a product of the Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University (https://bbisr.winship.emory.edu).
This work is funded by the Leukemia and Lymphoma Society Translational Research Program Award (Jeanne Kowalski); Georgia Research Alliance Scientist Award (Jeanne Kowalski); a Team Science Seed Funding from the Winship Cancer Institute of Emory University (Lawrence H. Boise, Sagar Lonial, Michael R. Rossi); Biostatistics and Bioinformatics Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI [Award number P30CA138292, in part]. The content is solely the responsibility of the authors (Bhakti Dwivedi & Jeanne Kowalski) and does not necessarily represent the official views of the NIH.
Bioconductor R package for SISPA is available at the https://www.bioconductor.org/packages/SISPA.
Please cite the method as: Kowalski J, Dwivedi B, Newman S, Switchenko JM, Pauly R, Gutman DA, Arora J, Gandhi K, Ainslie K, Doho G, Qin Z, Moreno CS, Rossi MR, Vertino PM, Lonial S, Bernal-Mizrachi L, Boise LH. Gene integrated set profile analysis: a context-based approach for inferring biological endpoints. Nucleic Acids Research. 2016 Apr 20;44(7):e69. doi: 10.1093/nar/gkv1503. Epub 2016 Jan 29. PubMed PMID: 26826710; PubMed Central PMCID: PMC4838358.
shinySISPA is a web-based tool that is implemented using the Shiny R package and code is freely available to download from GitHub (https://github.com/BhaktiDwivedi/shinySISPA). The tool is written in R and is also available as a Bioconductor R package (https://www.bioconductor.org/packages/SISPA/).
Sample Integrated Set Profile Analysis (SISPA) is intended for the researchers who are interested in defining sample groups using a defined gene set with similar, a priori defined molecular profile (Kowalski et al., 2016). SISPA can perform single-, two-, or three-feature type of analysis. Here, we define a feature as a specific data type (e.g., expression, methylation, variant, or copy change data) and profile as a genomic change of either increase (up) or decrease (down) within a feature.
An example of single-feature analysis could be identifying samples with increased (or decreased) expression profile within the defined gene set signature, while a two-feature analysis could be based on a combination of any two data types, e.g., identifying samples that exhibit decreased gene expression and decreased copy change and so forth.