TY - JOUR
T1 - Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation
AU - Schissler, A. Grant
AU - Piegorsch, Walter W.
AU - Lussier, Yves A.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is based upon work supported by the U.S. National Science Foundation under Grant No. 1228509 and by the U.S. National Institutes of Health under Grant No. R03ES027394.
Funding Information:
The results published here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. We gratefully acknowledge the kind and helpful input of Mr. Qike Li, Dr. Joanne Berghout, Dr. Ikbel Achour, Dr. Colleen Kenost, Dr. Haiquan Li, Dr. Nima Pouladi, Dr. Ryan Gutenkunst, and Dr. Joseph Watkins. In addition, thanks are due the Editor and two anonymous referees for insightful comments that greatly improved the quality of the manuscript. This work represents a portion of the first author?s Ph.D. dissertation from the University of Arizona Graduate Interdisciplinary Program in Statistics.
Publisher Copyright:
© The Author(s) 2017.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject (“N-of-1”) signals is a challenging goal. A previously developed global framework, N-of-1-pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient’s triple negative breast cancer data illustrates use of the methodology.
AB - Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject (“N-of-1”) signals is a challenging goal. A previously developed global framework, N-of-1-pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient’s triple negative breast cancer data illustrates use of the methodology.
KW - Gene expression data
KW - N-of-1
KW - RNA-seq
KW - affinity propagation clustering
KW - exemplar learning
KW - gene set
KW - inter-gene correlation
KW - precision medicine
KW - single-subject inference
KW - triple negative breast cancer
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U2 - 10.1177/0962280217712271
DO - 10.1177/0962280217712271
M3 - Article
C2 - 28552011
AN - SCOPUS:85043704719
SN - 0962-2802
VL - 27
SP - 3797
EP - 3813
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 12
ER -