New Algorithm for Analyzing Primate Brain Expression Data
Author Information
Author(s): Yuan Yuan, Chen Yi-Ping Phoebe, Ni Shengyu, Xu Augix Guohua, Tang Lin, Vingron Martin, Somel Mehmet, Khaitovich Philipp
Primary Institution: Key Laboratory for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences
Hypothesis
Can the modified dynamic time warping algorithm (DTW-S) reveal novel biological features of human brain development?
Conclusion
The DTW-S algorithm provides accurate time shift estimates that uncover new insights into the biological processes of human brain development.
Supporting Evidence
- The DTW-S algorithm allows for the estimation of time shifts in gene expression data.
- 482 out of 1183 genes tested showed significant time shifts.
- The method revealed distinct patterns of gene expression changes between species.
- Time shift estimates were consistent with previous findings on human brain development.
- Genes were classified into four phylo-ontogenetic categories based on their time shift patterns.
Takeaway
The researchers created a new tool to compare brain development in humans and primates, helping us understand how our brains grow differently.
Methodology
The study used a modified dynamic time warping algorithm to analyze gene expression time series data from primate brains.
Limitations
The algorithm may not be suitable for genes with substantially different temporal expression behavior.
Participant Demographics
The study included gene expression data from humans, chimpanzees, and rhesus macaques.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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