On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data

@inproceedings{Hicks2015OnTW,
  title={On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data},
  author={Stephanie C. Hicks and Mingxiang Teng and Rafael A. Irizarry},
  year={2015}
}
Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from five published studies and found that systematic errors can explain a substantial percentage of… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 47 CITATIONS, ESTIMATED 40% COVERAGE

117 Citations

0204020152016201720182019
Citations per Year
Semantic Scholar estimates that this publication has 117 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
SHOWING 1-10 OF 15 REFERENCES

A reanalysis of mouse ENCODE comparative gene expression data

  • Y. Gilad, O. Mizrahi-­‐Man
  • F1000Research 4,
  • 2015

Computational analysis of cell-­‐to-­‐cell heterogeneity in single-­‐cell RNA-­‐sequencing data reveals hidden subpopulations of cells

  • F Buettner
  • Nature biotechnology 33,
  • 2015

First principal component is strongly correlated with the proportion of detected. The principal components from the processed data available on GEO

  • Patel
  • Trapnell et al
  • 2014

Similar Papers

Loading similar papers…