Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings

@inproceedings{Cai2018IdentifyingDE,
  title={Identifying differentially expressed genes from cross-site integrated data based on relative expression orderings},
  author={Hao Cai and Xiangyu Li and Jing Li and Qirui Liang and Weicheng Zheng and Qingzhou Guan and Zheng Guo and Xianlong Wang},
  booktitle={International journal of biological sciences},
  year={2018}
}
It is a basic task in high-throughput gene expression profiling studies to identify differentially expressed genes (DEGs) between two phenotypes. But the weakly differential expression signals between two phenotypes are hardly detectable with limited sample sizes. To solve this problem, many researchers tried to combine multiple independent datasets using meta-analysis or batch effect adjustment algorithms. However, these algorithms may distort true biological differences between two phenotypes… CONTINUE READING

Figures, Tables, and Topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 59 REFERENCES

NCBI GEO: archive for functional genomics data sets—update

  • Nucleic Acids Research
  • 2012
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL