Heat-map claim
Open your materials, follow the steps, then turn in your work.
Read a microarray heat map and write a claim that separates disease risk from disease diagnosis.
1. Open your materials
Use the materials named in the first step below. Open lesson resources.
2. Start the work
Shade your fold-change values from Wednesday into a small heat map, high values one color and low another.
Show all 5 required steps
- Shade your fold-change values from Wednesday into a small heat map, high values one color and low another.
- Identify the cluster of genes that differs most between diseased and healthy samples.
- Write a CER claim about what the pattern suggests, with two values as evidence.
- Add one sentence explaining why this pattern shows risk, not a confirmed diagnosis.
- Submit your heat map and claim as your daily evidence.
Lost your place? Lost your place? Pull up your Wednesday fold-change values and restart at step 1: shade high values one color and low values another, then find the cluster that differs most before you write your claim.
Check your work before submitting
- You'll be able to read clusters on a heat map.
- You'll be able to distinguish risk from diagnosis in your claim.
3. Turn in your work
DueCheck Schoology- Hand in
- Shaded heat map of four genes and a CER claim (claim, two fold-change values as evidence, reasoning) that distinguishes risk from diagnosis.
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.
PDF upload helpYou get two school days for every day you were absent, so this deadline moves with you.
Find this lesson's Schoology assignments
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How this lesson connects
Keep using what you learned last class: 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. Today: A heat map encodes fold-change magnitude as color, so it can reveal an elevated-risk pattern, but because expression alone is not clinical confirmation, that pattern signals risk rather than a diagnosis.
Optional: listen or watch a unit review▸
Need help? Warm-up, timing, and directions▸
💡 Big idea: A heat map encodes fold-change magnitude as color, so it can reveal an elevated-risk pattern, but because expression alone is not clinical confirmation, that pattern signals risk rather than a diagnosis.
- 0-8Hook heat maps; review risk vs. diagnosis distinction
- 8-25Shade fold-change values from Wednesday into heat map grid (high = one color, low = another)
- 25-45Identify the gene cluster with the greatest diseased-vs.-healthy difference
- 45-60Write CER claim using two specific fold-change values as evidence
- 60-72Add risk-vs.-diagnosis sentence; peer check for accuracy
- 72-80Submit heat map and claim to the class site; preview Friday report
- • Hook: Show two heat maps: one from a healthy subject, one from a diagnosed patient, and ask students to point to the biggest difference.
- • Why it matters: Clinicians use expression clusters to decide which patients need follow-up biopsies or monitoring.
- • Today's work: You shade your own data into a heat map and write the claim a clinician would write, carefully distinguishing risk from diagnosis.
- • Exit goal: Heat map and CER claim submitted before the bell.
- • A heat map encodes fold-change magnitude as color intensity; clustering similar patterns reveals co-regulated gene groups.
- • A risk indicator shows an elevated probability of disease; a diagnosis requires clinical confirmation beyond expression data alone.
- • CER claims from data should be falsifiable: if the expression pattern reversed, what would that mean for your claim?
PLTW connection and today's work
Open Activity 3.1.4 DNA Microarray in myPLTW and shade your fold-change values into a heat map to identify the gene cluster with the greatest difference.
Today's stopping point: Fold-change table should be done (Wednesday); heat map and CER claim due today.
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
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.
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 heat map encodes fold-change magnitude as color, so it can reveal an elevated-risk pattern, but because expression alone is not clinical confirmation, that pattern signals risk rather than a diagnosis.
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: Looking at your own color-coded grid of fold-change values, which cluster of genes differs most between diseased and healthy, and does that pattern let you say a patient has the disease or only that they are at elevated risk?
What you already know: 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.
New idea: A heat map encodes fold-change magnitude as color, so it can reveal an elevated-risk pattern, but because expression alone is not clinical confirmation, that pattern signals risk rather than a diagnosis.
Visual or model: F1. F1. A lesson illustration or teaching diagram for Heat-map claim. 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 heat-map claim.
- 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: Looking at your own color-coded grid of fold-change values, which cluster of genes differs most between diseased and healthy, and does that pattern let you say a patient has the disease or only that they are at elevated risk?
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 read clusters on a heat map.
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-2026-10-28 · Simulated classroom evidence scenario
Your role: medical interventions team member
Decision: Your team must decide what the evidence from heat-map claim supports before submitting the claim-evidence-reasoning response named on today's page.
- • Add a few general paragraphs about genes and disease so the report looks thorough enough to submit.
- • Hold the risk claim until evidence outside your own expression numbers supports it, since expression differences are not a diagnosis.
- • Cut every sentence in the report that does not point to a fold-change value from your own data.
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Submit it as the claim-evidence-reasoning response.
Claim ceiling: Today's evidence supports a classroom claim about heat-map claim. 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 heat-map claim supports before submitting the claim-evidence-reasoning response named on today's page.
Context: Data can shift the odds without settling the verdict, so a strong expression pattern is a reason to look harder, not a finished diagnosis.
- • T1: Shade your fold-change values from Wednesday into a small heat map, high values one color and low another.
- • T2: Identify the cluster of genes that differs most between diseased and healthy samples.
- • T3: Write a CER claim about what the pattern suggests, with two values as evidence.
- • T4: Add one sentence explaining why this pattern shows risk, not a confirmed diagnosis.
- • T5: Submit your heat map and claim as your daily 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 read clusters on a heat map.
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 Heat-map claim. 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 believe that a striking heat-map pattern is itself proof that the patient has the disease.. The trap: A heat map shows expression differences, and expression can be elevated for many reasons, so the pattern raises the probability of disease but does not confirm it. Calling risk a diagnosis is a trap because it skips the clinical confirmation a real patient still needs.
Note: This is a parallel model on a different case (a newborn metabolic screening panel), not the heat-map task you are doing today. Use it to see the CER format and depth, then build your own claim from your own grid.\n\nI shaded four screening values from a newborn blood-spot panel into a small strip, using a darker color for values above the reference cutoff and a lighter color for values below it.\n\nClaim: The pattern in this sample suggests an elevated risk of phenylketonuria, especially in the raised amino-acid values, but it does not confirm the condition.\n\nEvidence: The phenylalanine level is 6 mg/dL and the phenylalanine-to-tyrosine ratio is 3.5, both above the standard screening cutoffs, which matches the pattern flagged for follow-up in the reference.\n\nWhy this is risk, not diagnosis: A screening panel is designed to catch samples that need a closer look, so an above-cutoff result raises the probability of the condition rather than proving it. Some raised values come from feeding timing, prematurity, or lab handling, so a confirmatory test such as a plasma amino-acid analysis or a genetic test is needed before anyone can say the newborn has the disorder. For that reason my claim names risk and points to follow-up, not a confirmed diagnosis.\n\nFalsifiability check: If a confirmatory plasma amino-acid test came back within the normal range, the risk signal would be explained as a false positive and my claim would be wrong, which shows the claim is testable.
| Gene | Fold change | Heat-map shade |
|---|---|---|
| Gene 3 | 4.0 | darkest |
| Gene 1 | 3.2 | dark |
| Gene 4 | 0.5 | light |
| Gene 2 | 0.25 | lightest |
This model shows the level of evidence and organization needed to complete: Parallel worked CER: a shaded results strip for a newborn screening panel and a claim with two numeric values as evidence that separates screening risk from diagnosis. Models the format only. Your own heat-map claim is different.
- Write one defensible claim.
- Choose specific evidence that supports the claim.
- Explain the scientific rule that connects the evidence to the claim.
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: Submit your heat map and claim 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 heat-map claim. It cannot prove causation, diagnose a real patient, or justify action outside this room.
Your heat map shows a bright cluster of upregulated genes in the diseased sample. A classmate writes: 'This proves the patient has the disease.' Fix the claim so it is scientifically honest.
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.
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
Today is individual work you can do from home: complete the same target above, then submit your CER.
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. If you cannot get in, see Mr. Mendoza. Do not skip the work.
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.
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