SOCR ≫ DSPA ≫ DSPA3 Topics ≫

How to read this chapter. Sections 1.1–1.5 set up why the book exists and what responsible practice requires. Sections 1.6–1.12 build the computational foundation everything else stands on. If you already write R daily, skim 1.6–1.11, but do work through §1.6.6 (Reproducibility), §1.6.7 (Data access), and §1.12 (Simulation), the book’s later chapters assume the conventions established there.

1 Learning objectives

After completing this chapter you will be able to:

  1. Describe the defining characteristics of complex biomedical and health data, and identify which of them a given study exhibits.
  2. Distinguish prediction, explanation, and causal inference, and state which one a given research question requires.
  3. Apply FAIR and research-ethics principles to a concrete dataset, including computing a quantitative re-identification risk.
  4. Configure a reproducible R environment: project structure, dependency pinning, seed discipline, and session recording.
  5. Manipulate R’s core data structures, atomic vectors, lists, data frames, matrices, and arrays, and predict the result of type coercion.
  6. Import, reshape, and export tabular data using tidy-data operations, and explain why long ↔︎ wide reshaping is invertible.
  7. Simulate synthetic data matching the marginal characteristics of an observed dataset, and assess the fidelity of the simulation.
  8. Evaluate a quantitative claim for provenance, deciding whether a published table, benchmark, or trend line supports the conclusion drawn from it.

Estimated time: 4–6 hours including exercises. Prerequisites: none. Some prior exposure to any programming language helps but is not assumed. Review the DSPA prerequisites and self-assessment pretest if you want to calibrate.

2 What this book is for

DSPA3 is the third edition of the textbook built around the HS650: Data Science and Predictive Analytics course at the University of Michigan (DSPA1 · DSPA2). It aims to give you a working understanding of the challenges, an appreciation of the opportunities, and a methodological foundation for designing, collecting, managing, processing, interrogating, analyzing, and interpreting complex health and biomedical data.

  • Vision, enable active learning by joining driving motivational challenges to mathematical foundations, computational statistics, and modern scientific inference.
  • Values, effective, reliable, reproducible, and transformative data-driven discovery in support of open science.
  • Strategic priorities, learners develop scientific intuition, computational skill, and data-wrangling ability sufficient to attack real biomedical and health data problems. Instructors receive documented, runnable code implementing everything from atomic data filters to end-to-end predictive analytics pipelines.

2.1 What is expected of you

  • You do not need to satisfy every prerequisite, master every mathematical foundation, or arrive as an experienced programmer.
  • You do not need to read linearly. Several suggested pathways through the topics map are sufficient for most readers.
  • The material will challenge you. When it does: engage with peers, search, read the referenced sources, and post in the discussion forum. The difficulty is the point, a skill that costs nothing to acquire is worth what it cost.
  • Some chapters will be too easy for you. Skim them, but attempt the practice problems anyway; they are calibrated to expose gaps that reading does not.
  • All code is available as an electronic supplement, and every example in this book is designed to run as printed.
  • These materials can be improved. Errors, typos, better code, interesting datasets, send them to DSPA.info @ umich.edu. Corrections are incorporated into subsequent revisions with attribution.

2.2 Chapter 1 Live Webapps

The DSPA3 Labs include a number of interactive webapps, which are build as portable, self-contained, and browser accessible applications. These apps demonstrate various concepts discussed in this chapter, including:

3 Driving problems

Every method in this book exists because someone needed to answer a question they could not answer by looking at the data directly. This section introduces the problems that recur throughout DSPA3.

The rightmost column is a forward map. It tells you where each dataset returns, so you can trace a single scientific problem across multiple analytic treatments, usually the fastest way to understand what a method actually buys you.

3.1 Neurodegeneration and dementia

Alzheimer’s disease. Can we relate observed clinical phenotypes to expected behavior? Can we prognosticate cognitive decline 3–12 months forward from imaging and clinical assessment? Can we recover meaningful subject clusters from harmonized multi-source data?

Source Modalities & scale Recurs in
ADNI · NACC Clinical (demographics, cognitive assessment); imaging (sMRI, fMRI, DTI, PiB/FDG PET); genetics (Illumina SNP); chemical biomarkers (labs, proteomics). Cohort sizes typically \(200 \le N \le 1200\) Dimensionality reduction, feature selection

A study of structural neuroimaging in AD illustrates the difficulty concretely: 808 ADNI subjects (200 AD, 383 MCI, 225 normal controls), sMRI parcellated with BrainParser, 80 neuroimaging biomarkers extracted by shape-analysis workflow, and 80 SNPs associated with those biomarkers via a pipeline implementation of PLINK. The correlations observed between genetic and imaging phenotypes suggest that AD/MCI/NC differences are best examined through joint models of morphometric, imaging, and genotypic data, not through any single modality.

Parkinson’s disease. Can we predict clinical diagnosis from available data, with and without the UPDRS assessment that currently defines the diagnosis? Can derived imaging and genetic biomarkers model progression well enough to support clinical decisions?

Source Modalities & scale Recurs in
PPMI Demographics; clinical (physical, verbal learning, neurological, UPSIT olfactory, vitals, MDS-UPDRS, ADL, MoCA, GDS-15); sMRI; genetics (Illumina ImmunoChip, 196,524 variants; NeuroX, ~240,000 exonic variants; APOE e2/e3/e4). Three cohorts: de-novo PD \(N=263\), SWEDD \(N=40\), controls \(N=127\). Visits at screening, baseline, 12, 24, 48 months Simulation §1.12, dimensionality reduction, feature selection, longitudinal analysis

Amyotrophic lateral sclerosis. Which variables jointly predict ALS progression (ALSFRS, muscle function)? Can adverse events be predicted from phenotype plus 0–3 month clinical change?

Source Modalities & scale Recurs in
PRO-ACT >100 clinical variables; 8,635 patients in the training set, 2,424 with valid gold-standard ALSFRS slopes. Demographics, ALSFRS, adverse events, onset_delta, onset_site, riluzole use Feature selection, the primary ALS case study

3.2 Substance use and withdrawal

Is the Risk for Alcohol Withdrawal Syndrome (RAWS) screen valid and reliable for predicting withdrawal among adult medical inpatients? What AUDIT-C cutoff optimizes prediction? Should items be added or removed?

Source Modalities & scale Recurs in
MAWS / UMHS EHR · WHO Global Health Observatory AUDIT-C scores (see Bush et al., 1998), including any current use (item 1), total AUDIT-C > 8, and prior withdrawal history. ~1,000 positive cases/year among ~10,000 adult inpatients Classification

3.3 Genomics and high-throughput computing

Genetic forensics, the 2013–2016 Ebola outbreak. The HHMI BioInteractive disease-detective activity tracks Ebola virus spread through Sierra Leone using the fact that most of a genome is shared within a species, more so among related individuals. DNA profiling exploits the residual differences, particularly in noncoding regions, where variation is less exposed to selection. Short tandem repeats (STRs) are 2–5 base motifs repeated a variable number of times at defined loci, flanked by non-variable regions. An allele is named either by repeat count or by total length; the STR below is “6” (six GATA repeats) or equivalently 70 bp including flanks.

Next-generation sequencing. Whole-genome and exome sequencing carry the clues needed to identify genes behind Mendelian disorders, and population-genetic extensions push toward complex traits. The Graphical Pipeline for Computational Genomics (GPCG) implements alignment, quality control, SNP analysis, CNV identification, annotation, and visualization as composable workflow steps.

Neuroimaging-genetics. A computational infrastructure for high-throughput neuroimaging-genetics supports aggregation, harmonization, processing, and interpretation across imaging, genetics, clinical, and cognitive sources. Workflows are portable XML objects that carry execution instructions from a client to distributed pipeline servers, an early and durable example of computational provenance as a first-class artifact.

3.4 Interactive exploration

The SOCR Brain Visualization App preloads sMRI, ROI labels, and fiber tract models for a normal brain, and accepts drag-and-drop of your own stereotactic data.