Paper Info
Title
Application of Independent Component Analysis to Tumor Transcriptomes Reveals Specific and Reproducible Immune-Related Signals.
Abstract
Independent Component Analysis (ICA) can be used to model gene expression data as an action of a set of statistically independent hidden factors. The ICA analysis with a downstream component analysis was successfully applied to transcriptomic data previously in order to decompose bulk transcriptomic data into interpretable hidden factors. Some of these factors reflect the presence of an immune infiltrate in the tumor environment. However, no foremost studies focused on reproducibility of the ICA-based immune-related signal in the tumor transcriptome. In this work, we use ICA to detect immune signals in six independent transcriptomic datasets. We observe several strongly reproducible immune-related signals when ICA is applied in sufficiently high-dimensional space (close to one hundred). Interestingly, we can interpret these signals as cell-type specific signals reflecting a presence of T-cells, B-cells and myeloid cells, which are of high interest in the field of oncoimmunology. Further quantification of these signals in tumoral transcriptomes has a therapeutic potential.
Year
DOI
Venue
2018
10.1007/978-3-319-93764-9_46
Lecture Notes in Computer Science
Keywords
Field
DocType
Blind source separation,Unsupervised learning,Genomic data analysis,Cancer,Immunology
Myeloid,Biology,Transcriptome,Unsupervised learning,Immune system,Independent component analysis,Computational biology,Component analysis,Blind signal separation,Independence (probability theory)
Conference
Volume
ISSN
Citations 
10891
0302-9743
0
PageRank 
References 
Authors
0.34
4
5
Name
Order
Citations
PageRank
Urszula Czerwinska131.09
Laura Cantini200.34
Ulykbek Kairov361.21
Emmanuel Barillot4950165.00
Andrei Zinovyev528227.30