Video summary

GL4HS Capstone Lecture Metadata, Multi-Factor Analysis, and Statistical + Biological Significance

Main summary

Key takeaways

Educational

Main ideas / concepts covered

Course flow (Modules 2 → 3)

  • Today’s focus:
    • What teams need to do for Module 2
    • How that work connects to Module 3
  • Teams were working on team-building/networking, and should ensure:
    • At least one member per team has access to the course materials.

Access troubleshooting for Google Classroom

  • Instructors deleted and re-added users who couldn’t access the classroom.
  • If users still see “invitation not found”, they should:
    • Follow prior instructions (from Miss Clolio / “Miss Clilio had…”), including:
      • Clearing browsing data
      • Trying different browsers
  • If problems persist, students should contact instructors via:
    • Slack, or
    • A message containing “here.”

Module 1 → Module 2 (Why metadata matters)

  • Module 1 is a deep dive into metadata, not a quick look.
  • Students must research:
    • What tissues were investigated
    • What is unknown
    • Research directions
    • Papers already derived from the dataset
  • Purpose:
    1. Avoid proposing work someone else already did
    2. Build enough background to form a strong hypothesis for a research proposal

Why standardized preprocessing/pipelines matter

  • Data were pre-processed by a gene lab using a standardized RNA-seq pipeline for:
    • Quality control
    • Reproducibility
  • Key QC and normalization concepts:
    • RNA quality and assay quality determine data validity
    • The pipeline includes stopping points with quality control
    • Spike-ins (known RNA quantity/quality) help normalize data and detect library-prep inefficiencies
    • Enables cross-dataset comparison (common in open data repositories)

End-to-end analysis pipeline (what to verify before analysis)

The pipeline includes:

  • Hypothesis generation / experimental design
  • RNA purification + library preparation
  • Sequencing
  • Quality control
  • Trimming
  • Alignment
  • Quantification
  • Differential gene expression
  • Biological insight + visualization

Critical point:

  • Students must understand not only what was planned, but what actually happened.
    • Example: differences between animal cohorts due to unexpected events (e.g., a natural disaster / ground control changes).

Detailed methodology / step-by-step instructions (as presented)

A) Use OSDR metadata thoroughly (Module 1 emphasis)

In the OSDR experiment description, check:

  • Mission/acronym meaning (e.g., rodent research reference missions)
  • Mouse strain (varies in traits like bone loss susceptibility or immunocompromise)
  • Age (interpret in model-organism terms; mouse “old” ≈ 24 months+)

Use factors from metadata because they drive later notebook configurations.

Then click into metadata-linked items:

  • Organism pages
    • General biology + sequencing context
  • Project/study page
    • Mission background, center, duration in space, strain
  • Payload/protocol sections
    • Time points, baseline timing, housing density changes, supplements, cage conditions

Also note potential confounders that could explain unexpected genes:

  • Housing density differences
  • Seasickness/rough-conditions stress during spaceflight
  • Radiation details (type/dose; often shielded, but still potentially influential)

Review protocol sections for:

  • Sample collection + tissue preservation
  • Transcription protocols
  • Library construction
  • Sequencing method
  • Data processing steps

B) Quality control workflow using MultiQC reports (Module 2 emphasis)

In OSDR files/notebooks, use gene-lab processed RNA-seq outputs to find:

  • MultiQC “merge sequence data” quality control reports (HTML)

Steps:

  1. Select/download the multiqc report files for the dataset
  2. Open the downloaded HTML to view plots covering multiple samples:
    • Flight live/return samples vs ground controls vs young/old subsets

QC checks highlighted:

  • Read length distribution should match expected ~150
  • GC content should be tight/narrow
  • Per-base quality scores:
    • Expected to stay in the “green” region
    • Drop-off toward later positions can be normal
  • Adapter content:
    • Assess whether trimming is needed
  • Metrics can be visualized by configuring/plotting columns and exporting plots

C) Trim-related QC (pre vs post trimming)

After QC, verify trimming by:

  • Returning to OSDR protocol sections to see trimming parameters
  • Confirming pipeline details, such as:
    • Raw FASTQ filtered using Trim Galore powered by cutadapt
    • FastQC then MultiQC generation post-filtering
    • Trimming parameters used for the alignment-ready dataset

Compare:

  • Raw vs trimmed QC MultiQC reports
  • Changes in GC content / read length / % failed to estimate trim impact

D) Alignment and quantification (overview-level)

  • Alignment:
    • Follow the standardized pipeline parameters used by the gene lab team
    • In notebooks, alignment parameters are assumed (not emphasized as user-configurable)
  • Quantification:
    • Used to identify expressed genes and downstream expression changes

E) Multifactors + differential expression notebooks (Capstone notebooks)

  • Notebooks (e.g., notebook 6/7 in the basic course, and capstone notebooks 1/2) are dataset-specific.
  • Students must ensure:
    • The notebook’s OSD dataset number is correct
    • Chosen configurations match the dataset’s factor values in metadata

Recommended starting configuration:

  • Basic microgravity vs 1G comparison
    • Spaceflight vs ground control (or parabolic flight vs ground control, depending on dataset)

Other metadata factors that may exist:

  • Age (young vs old)
  • 1G by centrifugation (1G condition in space vs ground)
  • Live animal return vs terminated on ISS
  • Possibly sex, genotype, duration/time in space

Configuration workflow:

  • Copy notebooks and re-run for different factor-value comparisons
  • Use the notebook’s “define factors/configuration” sections
  • Optionally run multiple configurations across teammates, then compile results

Important caution:

  • Factor values must match metadata exactly (string matching is strict: spaces, capitalization, exact genotype names)
  • Units might appear on the website but not inside notebook factor/value lists—use notebook-listed values.

How to compare combined factors (multi-factor design):

  • Define a factor list with multiple factors
  • Create groups such as:
    • Group A = young + ground control
    • Group B = old + ground control

This enables comparisons like:

  • genotype effects under flight vs ground
  • age × condition combinations

Gene lists feed into:

  • Pathway analysis tools
    • Gene Set Enrichment Analysis (GSEA)
    • Over-representation analysis (including GO/ORA)

Instructor note (e.g., Dr. Calto / James):

  • Prefilled configs may work for some notebooks but not others.
  • One config (e.g., “config 5” type behavior) is guaranteed to work because its output was verified by executing the notebooks.

Statistics + significance + biological significance (conceptual lessons)

Statistical significance (p-values)

  • p-value = probability of observing the data if the null hypothesis is true (no real difference).
  • RNA-seq involves multiple testing:
    • Thousands of genes tested (e.g., ~30,000)
    • Even p = 0.01 can produce many false positives when repeated across tests

Therefore outputs include:

  • Adjusted p-values using multiple hypothesis correction:
    • Bonferroni correction
    • FDR / Benjamini-Hochberg-style correction

Biological significance

  • Statistical significance alone is not enough.
  • Biological significance considers:
    • Fold change
    • Whether the change is meaningful beyond natural variation

Fold change context examples:

  • Strongly repressed genes:
    • Small fold changes can still be biologically meaningful
  • Post-transcriptional regulation:
    • Large fold changes may not translate to meaningful biological impact

Pathways vs individual genes

  • Pathway analysis can use less stringent thresholds because:
    • Pathways assess co-expression across genes already significant in gene-level analysis.
  • Students must still check biological plausibility:
    • Avoid importing genes into pathway analysis that aren’t actually significant in the dataset, or results may be misleading.

ANOVA vs t-test

  • t-test: compares two groups
  • ANOVA: compares more than two groups
  • Post hoc comparisons can follow ANOVA to inspect pairwise differences

Students’ Q&A themes included in the lecture

  • Whether recordings will be posted:
    • Yes, but may take time to process
  • How publications relate to dataset pages:
    • Publications using the dataset are flagged/linked (possibly with a lag)
    • Students can search by dataset number in publications/methods
  • Strategies for literature reviews in groups:
    • Instructors plan to share literature review templates in Google Classroom
  • Clarification on “significance”:
    • Distinguishes statistical vs biological significance (especially under multiple testing)
  • Pathway analysis vs single-gene focus:
    • Recommended to do both in parallel:
      • pathway analysis for context/mechanistic interpretation
      • individual gene inspection for biomarkers/targeted insights
  • Clarification on “custom” pathway options:
    • Pathways may come from external/custom datasets/databases, not only built-in pathways

Next steps / logistics

  • A second part of pathway analysis will be in a separate lecture scheduled for Monday (per instructor plan).
  • Notebooks:
    • Will be shared in Slack (not Google Classroom)
    • Instructor intends to release notebooks over the weekend if possible
  • Event reminders:
    • Last organized Kumo space at 4 PM (timezone noted)
    • Guest lecture by “Twimman Clemens” (CEO of Space Tango) at 12:00 PM Pacific time
  • Admin:
    • Slides and recording will be posted when available

Speakers / sources featured (identified)

Speakers

  • Liz (instructor/presenter; primary voice guiding content on OSDR/QC/statistics)
  • Dr. Calto (co-instructor; clarifications on notebook configs and multi-factor setup)
  • James (co-instructor; notebook configuration examples and factor-value matching)
  • Sahi (student asking/engaging in Q&A)
  • Jennifer (student asking about significance/pathway focus)
  • Hashita (student asking about literature review strategies)
  • Anvi/Anvie (student asking about focusing on single genes vs pathway analysis, and other questions)

Other referenced people/sources

  • Miss Clolio / Miss Clauddio (referenced for Classroom access troubleshooting)
  • Dr. Alaska (example of person/center affiliation in OSDR metadata)
  • Twimman Clemens (guest lecture speaker; CEO of Space Tango)
  • Space Tango
  • OSDR / Open Science Data Repository
  • Gene lab sample processing team
  • Tools/terms referenced:
    • RNA-seq pipeline, RNA purification + library prep, ERCC spike-ins
    • MultiQC, Trim Galore (cutadapt), FastQC
    • STAR, GSEA, Over-representation analysis
    • Gene Ontology (GO)
    • Benjamini-Hochberg / FDR, Bonferroni
    • ANOVA, post hoc analysis

Original video