Here's an example of what's due today

Expression data lab

Tue, Oct 27, 2026 · Week 10 · Genetics of Disease (Medical Interventions)

Today's goal: Use a gene expression table to calculate fold change and flag upregulated and downregulated genes.

Learn first

What a finished product looks like

This is a model of the work you should turn in today. Use it to check your own: match the structure and the level of detail, do not copy it. Your data and wording should be your own.

Fold-change table
Completes: A worked parallel example on a different dataset, untreated versus drug-treated cells: a fold-change table for four genes with upregulated or downregulated labels and one sentence on the meaning of an upregulated gene. Use it to model the method, then run it on today's own numbers.

This is a parallel example on different data, untreated versus drug-treated cells, so you can see the method and then run it on today's own healthy-versus-diseased numbers.

I divided each gene's treated value by its untreated value to get fold change, then labeled each gene.

Rule I used: fold change above 1 is upregulated (more active after treatment); below 1 is downregulated (less active).

What an upregulated gene might mean: A gene turned up after treatment could be a stress-response gene the drug switched on, or a gene the drug was meant to boost, so the number flags a gene worth investigating, not an automatic cause.

GeneUntreatedTreatedFold changeLabel
Gene A401203.0upregulated
Gene B250500.2downregulated
Gene C301505.0upregulated
Gene D200800.4downregulated
Fold-change table: Genes A and C are upregulated (3.0 and 5.0); Genes B and D are downregulated (0.2 and 0.4).
Why this matters

This model shows the level of evidence and organization needed to complete: A worked parallel example on a different dataset, untreated versus drug-treated cells: a fold-change table for four genes with upregulated or downregulated labels and one sentence on the meaning of an upregulated gene. Use it to model the method, then run it on today's own numbers.

Build yours step by step
  1. Name the variables and include units.
  2. Enter observations without changing the raw values.
  3. Check labels, calculations, and patterns before interpreting the data.
Change it for a new task

Keep the structure. Replace the question, facts, measurements, and evidence. Then recheck units, vocabulary, and whether the conclusion goes beyond the evidence.

Also due today: Save your fold-change table to Schoology.

Turn in: Fold-change table

Go to Schoology to turn this in.

Submit one PDF. Put your first and last name in the document header. Name the file: FirstName LastName - Assignment Title - YYYY-MM-DD.pdf.

Open Schoology PDF upload help

If you cannot get in, see Mr. Mendoza. Do not skip the work.

Claim ceiling: Today's evidence supports a classroom claim about expression data lab. It cannot prove causation, diagnose a real patient, or justify action outside this room.

Check yourself

WebXam problem for today's skill

One exam-style question that uses exactly what you practiced today. Try it before you reveal the answer, then read why each choice is right or wrong.

WebXam-style domain: Molecular and Genetic TechnologySelf-check skill: Calculating and interpreting fold change from expression data
A gene shows an expression value of 100 in healthy tissue and 25 in diseased tissue. What is its fold change, and how is it labeled?

Tap an answer to see the full explanation. Nothing is recorded or graded.

Why this practice matters

It builds this reusable test skill: Calculating and interpreting fold change from expression data.

Use it on a new WebXam question
  1. Name the concept or data pattern being tested.
  2. Cross out choices that violate that rule or the evidence.
  3. Justify the best remaining choice before checking the answer.