Expression data lab
Open your materials, follow the steps, then turn in your work.
Use a gene expression table to calculate fold change and flag upregulated and downregulated genes.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Open the expression dataset in the shell and find the diseased and healthy sample columns.
Show all 5 required steps
- Open the expression dataset in the shell and find the diseased and healthy sample columns.
- For four genes, calculate fold change by dividing diseased expression by healthy expression.
- Label each gene upregulated or downregulated based on whether fold change is above or below one.
- Write one sentence on what an upregulated gene might mean for the disease.
- Save your fold-change table as your lab evidence.
Lost your place? Lost your place? Reopen the expression dataset in the shell, find the diseased and healthy columns again, and restart at step 2: divide diseased by healthy for your four genes to rebuild the fold-change table.
Check your work before submitting
- You'll be able to calculate fold change from expression data.
- You'll be able to flag genes as upregulated or downregulated.
Before lab work: read the safety rules
- No wet lab materials today; all work is computational.
- Dataset is anonymized class-aggregate; do not enter or share personal identifying information.
- Save your work frequently; do not rely on browser auto-save for the dataset.
3. Turn in your work
DueCheck Schoology- Hand in
- Fold-change table for four genes with upregulated/downregulated labels and one sentence on the biological meaning of an upregulated gene.
How to submit and name your file
Use the submission route shown on today's today's page.
In Schoology, open your course and the assignment for this lesson. Attach your file, select Submit, and check that it appears in the submission.
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How this lesson connects
Keep using what you learned last class: A microarray reads fluorescence where sample DNA hybridizes to matching probes, so one chip can report the expression of thousands of genes at once. Today: Fold change divides diseased expression by healthy expression, so a result above 1 means that gene is more active in the disease sample and a result below 1 means it that gene is less active in the disease sample.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 Big idea: Fold change divides diseased expression by healthy expression, so a result above 1 means that gene is more active in the disease sample and a result below 1 means it that gene is less active in the disease sample.
- 0-8Hook heat map and fold-change formula introduction; confirm dataset access
- 8-25Open dataset; locate diseased and healthy columns; calculate fold change for genes 1-2
- 25-45Calculate fold change for genes 3-4; label all four as up or
- 45-60Write one sentence on what an gene might mean for the disease
- 60-72Partner check: verify each other's calculations for arithmetic errors
- 72-80Save fold-change table to course shell; preview Thursday heat-map work
- • Hook: Show a heat map of vs. normal and ask: which genes are the disease turning up, and which is it turning off?
- • Why it matters: analysis is how researchers identify biomarkers and drug targets.
- • Today's work: You apply the fold-change formula to real expression data and interpret what the numbers mean.
- • Exit goal: Fold-change table with / labels and one interpretation sentence saved before the bell.
- • Fold change = diseased expression / healthy expression; greater than 1 is , less than 1 is .
- • data is generated by measuring fluorescence intensity at each probe spot; the numbers in the dataset represent those intensities.
- • An gene in diseased may be driving the disease () or responding to protect cells (repair gene).
PLTW connection and today's work
Open Activity 3.1.4 DNA Microarray in myPLTW and use the gene expression dataset to calculate fold change for four genes.
Today's stopping point: No school Monday/Tuesday; this data lab is the opening hands-on benchmark for this unit.
PLTW activity titles identify the course connection. If your account will not open, use the posted materials for today and tell Mr. Mendoza. Do not mark an online activity complete unless you completed it.
Use the turn-in directions at the top of this page. Do not create a second submission unless your teacher asks for one.
Show another explanation or a smaller first step
Need help? Choose a starting point
Finish the assigned lab safely before starting extra practice.
Lesson resources: reading, slides, and vocabulary▸
The deck carries the prior idea forward, lets you inspect an analogy, maps the rule to biology, and ends with the same evidence decision and exit ticket used on this page.
Generated from this lesson's canonical data with a red-team citation check.
A reads fluorescence where sample DNA hybridizes to matching probes, so one chip can report the expression of thousands of genes at once.
Fold change divides diseased expression by healthy expression, so a result above 1 means that gene is more active in the disease sample and a result below 1 means it that gene is less active in the disease sample.
A library keeps a master plan protected while working copies guide production at different stations.
- Why protect the master copy?
- What information moves?
- Where can an error change the final product?
Stored information can be copied, read, and converted into a functional product.
Genes are regulated biological sequences, not conscious instructions, and one gene rarely determines a whole trait alone.
- • Master plan maps to DNA.
- • Working copy maps to RNA.
- • Production output maps to or a regulated cell function.
Driving question: Given four genes with diseased and healthy expression numbers, which ones did the disease turn up and which did it turn down, and what does that suggest about what the disease is doing?
What you already know: A reads fluorescence where sample DNA hybridizes to matching probes, so one chip can report the expression of thousands of genes at once.
New idea: Fold change divides diseased expression by healthy expression, so a result above 1 means that gene is more active in the disease sample and a result below 1 means it that gene is less active in the disease sample.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Expression data lab. Use it with E1-E3; it is a model or context image, not experimental or patient data. What to notice: Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision.
- Observe or measure the relevant feature in expression data lab.
- Organize the observation with a stable evidence ID.
- Apply this rule: Stored information can be copied, read, and converted into a functional product.
- Choose the option the evidence supports and state the limit of the conclusion.
Real biomedical example: Given four genes with diseased and healthy expression numbers, which ones did the disease turn up and which did it turn down, and what does that suggest about what the disease is doing?
What the evidence supports: E1-E3 and F1 support the daily take-home when the response meets the stated success criteria.
What it cannot prove: The package does not support claims beyond this lesson's or any real patient diagnosis.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice.
Go further, optional: The source links below are optional enrichment. Every fact required for today's local evidence decision appears in this lesson package.
A gene-expression comparison describes relative signal patterns across the supplied conditions, and interpretation depends on normalization, replication, controls, measurement range, and the difference between association and causation.
Limit: A classroom expression table can support a bounded pattern claim but cannot diagnose disease, prove a regulatory mechanism, or establish clinical significance.
Stored information can be copied, read, and converted into a functional product.
Limit: Genes are regulated biological sequences, not conscious instructions, and one gene rarely determines a whole trait alone.
You can calculate fold change from expression data.
Limit: E3 defines the classroom product or success criterion. It is not independent scientific evidence and cannot justify a clinical or causal claim.
PLTW-GEND-2026-10-27 · Simulated classroom evidence scenario
Your role: medical interventions team member
Decision: Your team must decide what the evidence from expression data lab supports before submitting the labeled and result claim named on today's page.
- • Write the claim as elevated risk, since the color pattern shows expression differences and not clinical confirmation.
- • State that the patient has the disease, because the diseased cluster stands out so clearly on the grid.
- • Pause the claim until a clinical confirmation test is run, since expression can be elevated for many reasons.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the labeled and result claim.
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.
Reason for review: Your team must decide what the evidence from expression data lab supports before submitting the labeled and result claim named on today's page.
Context: A gene's activity level, not just its , tells you what a cell is doing right now, so measuring how much each gene is turned up or down can reveal a disease at work.
- • T1: Open the expression dataset in the shell and find the diseased and healthy sample columns.
- • T2: For four genes, calculate fold change by dividing diseased expression by healthy expression.
- • T3: Label each gene or based on whether fold change is above or below one.
- • T4: Write one sentence on what an gene might mean for the disease.
- • T5: Save your fold-change table as your lab evidence.
- • E1: A gene-expression comparison describes relative signal patterns across the supplied conditions, and interpretation depends on normalization, replication, controls, measurement range, and the difference between association and causation.
- • E2: Stored information can be copied, read, and converted into a functional product.
- • E3: You can calculate fold change from expression data.
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Figure finding: Teaching diagram for Expression data lab. Trace the labeled testing, treatment, or biological process and identify where evidence limits the decision. This is a teaching model, not patient or experimental data.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
Mean = sum of values / number of values. Median = middle ordered value. Range = maximum - minimum.
For 2, 4, 4, and 10: mean = 20 / 4 = 5, median = 4, and range = 10 - 2 = 8.
Mean, median, and range keep the measurement unit. Order the values before finding the median.
Calculate the requested summary for today's supplied values, then write what it reveals and what it hides.
Students often think Students assume that if a gene is more active in a disease, that gene must be causing the disease.. The trap: An gene can be the driver (an pushing the disease) or the cell fighting back (a repair gene working overtime). The number alone tells you a gene is busier, not whether it is the villain or the rescuer, so never read high expression as guilt on its own.
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.
| Gene | Untreated | Treated | Fold change | Label |
|---|---|---|---|---|
| Gene A | 40 | 120 | 3.0 | upregulated |
| Gene B | 250 | 50 | 0.2 | downregulated |
| Gene C | 30 | 150 | 5.0 | upregulated |
| Gene D | 200 | 80 | 0.4 | downregulated |
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.
- Name the variables and include units.
- Enter observations without changing the raw values.
- Check labels, calculations, and patterns before interpreting the data.
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.
- CER:
- Claim, Evidence, Reasoning: make a claim, back it with evidence, explain your reasoning.
- SOP:
- Standard Operating Procedure, the exact steps to follow (especially in a lab).
- Tracker:
- Your PLTW progress log where you record completed evidence.
- myPLTW:
- The PLTW course site where you do the online activities. Find it in Clever with your Microsoft sign-in, right next to Schoology.
Classroom documents for this lesson are posted in Schoology. Open Clever, then Schoology, and find each one by the name shown on its card.
Use this as the classroom resource for and analysis.
Placement rationale
Matched and analysis by path:Medical-Interventions/Unit-2_How-to-Screen-Your-Genes/00_Unit-Overview; keywords:gene expression, microarray. Score 138. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched and analysis by path:Medical-Interventions/Unit-2_How-to-Screen-Your-Genes/00_Unit-Overview; keywords:microarray. Score 126. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Placement rationale
Matched and analysis by path:Medical-Interventions/Unit-2_How-to-Screen-Your-Genes/2.1_Genetic-Testing-and-Screening. Score 126. Visibility: student-schoology (student-facing resource; link through Schoology rather than local path).
Sign in to Clever with your district Microsoft account to open Schoology or myPLTW. Follow today's posted steps. If myPLTW will not open, use the posted alternative and tell Mr. Mendoza. Turn in your completed work through the Schoology assignment.
Practice: try a question, then check your answer▸
Claim ceiling for this check: 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.
A gene reads 500 in diseased tissue and 2,000 in healthy tissue. What is the fold change, and is the gene upregulated or downregulated in the disease?
Write an answer and pick a confidence to unlock the key.
Missed class or ready for more?▸
Run this before you touch the bench. It is built from the real lab procedure, so the decisions you make here are the ones you will make with the equipment in your hands.
I can name the procedure's purpose and the evidence I will record. I can state today's specific hazards and the control for each. If this deck does not name them, I ask Mr. Mendoza before I touch anything. My data table is ready before materials are handled.
Finish the checklist before you handle any material.
- • No wet lab materials today; all work is computational.
- • Dataset is anonymized class-aggregate; do not enter or share personal identifying information.
- • Save your work frequently; do not rely on browser auto-save for the dataset.
- 1Before materials are handled, identify the purpose, variables or comparison, controls, measurement units, and stop-work condition.
- 2Open the expression dataset in the shell and find the diseased and healthy sample columns.
- 3For four genes, calculate fold change by dividing diseased expression by healthy expression.
- 4Label each gene upregulated or downregulated based on whether fold change is above or below one.
- 5Write one sentence on what an upregulated gene might mean for the disease.
- 6Save your fold-change table as your lab evidence.
- 7Record each result in the prepared table before interpreting it. Mark missing, repeated, or invalid results truthfully.
- 8Complete the named cleanup and waste route, remove PPE safely, wash hands when required, and confirm the station is ready for the next group.
| Trial or sample ID | Independent condition | Measured result with units | Observation before interpretation | Quality-control note |
|---|---|---|---|---|
Before the procedure, predict the result and cite the rule behind the prediction.
After the procedure, compare the result with the prediction and name one limitation or source of uncertainty.
What today's skills lead to. These are real health-science careers this course builds toward. Tap one to see, on the US Department of Labor's O*NET site, what the job actually involves, what it pays, and how fast it is growing.
Follow IRF6 from gene to , and see why the lip and need it.
Goes with: From gene to protein: IRF6 has a job
Five genes, five ways to interrupt a face: IRF6, , CDH3, MSX1, .
Goes with: The cleft gene set: more than one way to interrupt a face
From home, open the provided expression spreadsheet and complete the same analysis: calculate fold change for four genes, label each up or , and shade a small heat map by value.
Gene expression dataset (PLTW course shell)Use the submission route shown on today's today's page.
Class still runs. Complete the online activity above (it's self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
Genetic Science Learning Center: Genes and gene expressionYou've passed Unit 2, so the optional extra-credit track is open. Complete reserved-unit work from home, including virtual labs, for extra credit. Each item shows its correct submission route.
Open the extra-credit track- CompleteEvery required part of the artifact is present, nothing left blank.
- AccurateThe science and the data are correct and match the evidence.
- Scientific reasoningYou explain your claim with evidence and reasoning (CER), not just an answer.
- Professional communicationClear, organized, labeled, and written the way a clinician or scientist would.
- SubmittedGo 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. If you cannot get in, see Mr. Mendoza. Do not skip the work.
- Error analysis and method · counts doubleName a specific limit of the method and how it moved your result, and compare what you predicted to what happened. "Human error" does not count; say what about the procedure or instrument caused it.
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