qc.from.regionwise.df: Perform data quality check based on a dataframe containing...

In dfsp-spirit/nitools: Managing and Visualizing Brain Surface Data

Perform data quality check based on a dataframe containing aggregated region-wise data.

Description

Determine subjects that potentially failed segmentation, based on region-wise data. The data can be anything, but there must be one numerical value per subject per region.

Usage

qc.from.regionwise.df(
  rdf,
  z_threshold = 2.8,
  verbosity = 0L,
  num_bad_regions_allowed = 1L
)

Arguments

rdf	data.frame, the region data. The first column must contain the subject identifier, all other columns should contain numerical data for a single region. (Each row represents a subject.) This can be produced by calling group.agg.atlas.native or by parsing a text file produced by the FreeSurfer tool 'aparcstats2table' (see fsbrain:::qc.from.segstats.table for parsing code).
z_threshold	numerical, the cutoff value for considering a subject an outlier (in standard deviations).
verbosity	integer, controls the output to stdout. 0=off, 1=normal, 2=verbose.
num_bad_regions_allowed	integer, the number of regions in which subjects are allowed to be outliers without being reported as potentially failed segmentation

Value

named list with entries: 'failed_subjects': vector of character strings, the subject identifiers which potentially failed segmentation. 'mean_dists_z': distance to mean, in standard deviations, per subject per region. 'num_outlier_subjects_per_region': number of outlier subjects by region. 'metadata': named list of metadata, e.g., hemi, atlas and measure used to compute these QC results.

See Also

Other quality check functions: qc.for.group(), qc.from.segstats.table()

dfsp-spirit/nitools documentation built on Jan. 26, 2024, 8:24 p.m.