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.
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
VL - 27
SP - 3797
EP - 3813
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 12
ER -