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Here we present Looper, a computational method to analyze longitudinally gathered datasets and identify gene pairs that form looping trajectories when plotted in the space described by these phases.
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These studies also require multiple control groups and diagnostic subcategories of patients that are ideally gathered longitudinally over the course of disease progression.
Data was gathered pre, post, and longitudinally.
Performance scores were gathered at repeated time points longitudinally on students who were also nested within classes; therefore, a hierarchical or multilevel model (HLM) was required to account for lack of independence between scores.
However, clinical data were collected longitudinally in specialized mood-disorder clinics where patients are followed up systematically and at regular intervals, increasing the statistical accuracy of gathered information.
Compared with priors gathered from images of different individuals, the subject-specific prior exhibits smaller anatomical variability with the to-be-segmented image, and thus provides more accurate and longitudinally consistent segmentation results.
Descriptive statistics were gathered.
TYW gathered the data.
CV gathered the material.
Written consent was gathered.
AM gathered the data.
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