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MULTIPLE TESTING VIA FDR FOR LARGE SCALE IMAGING DATA.

Chunming Zhang1, Jianqing Fan, Tao Yu

  • 1Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA.

Annals of Statistics
|June 7, 2011
PubMed
Summary
This summary is machine-generated.

The conventional False Discovery Rate (FDR) procedure struggles with spatial imaging data. A new FDR(L) method uses local p-value aggregation to improve signal detection and reduce false positives.

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Area of Science:

  • Statistical methods
  • Neuroimaging analysis
  • Spatial statistics

Background:

  • Multiple testing procedures are crucial for analyzing large-scale spatial imaging data.
  • Spatial signals in such data are typically sparse yet clustered.
  • Conventional False Discovery Rate (FDR) procedures may exhibit a lack of identification phenomenon (LIP), diminishing detection power, especially when spatial information is ignored.

Purpose of the Study:

  • To quantify the extent of LIP in conventional FDR procedures for spatial data.
  • To propose a novel multiple comparison procedure, FDR(L), that incorporates spatial information.
  • To enhance the detection sensitivity and specificity of spatial signal detection in imaging data.

Main Methods:

  • Introduction of a scalar metric to characterize the LIP of FDR procedures.
  • Development of the FDR(L) procedure using local aggregation of neighboring p-values.
  • Theoretical analysis of FDR(L) properties under weak dependence assumptions.
  • Simulation studies and application to a real brain fMRI dataset.

Main Results:

  • The conventional FDR procedure can lack detection ability due to ignoring spatial structure.
  • The proposed FDR(L) procedure effectively alleviates the LIP.
  • FDR(L) facilitates the use of more stringent control levels without compromising detection.
  • Simulation results demonstrate improved detection sensitivity with minimal loss in specificity for FDR(L).

Conclusions:

  • The FDR(L) procedure offers a computationally simple and effective approach for spatial signal detection in large-scale imaging data.
  • Incorporating spatial information via local p-value aggregation significantly enhances the performance of FDR procedures.
  • FDR(L) provides a valuable alternative for analyzing spatially structured data, as evidenced by its application to fMRI data.