Expression data lab
Safety gate · before any work
- 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.
Do now
Use a gene expression table to calculate fold change and flag upregulated and downregulated genes.
- Hand in
- Fold-change table for four genes with upregulated/downregulated labels and one sentence on the biological meaning of an upregulated gene.
- Where
- Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
You get two school days for every day you were absent, so this deadline moves with you.
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?
Use a table to calculate fold change and flag and genes.
- • You'll be able to calculate fold change from expression data.
- • You'll be able to flag genes as or .
- If a gene reads 3,200 in diseased and 400 in healthy tissue, is that gene more active or less active in the disease?
- Healthy and diseased from the same person carry the exact same DNA. So what could actually be different between the two samples?
- 1Open the expression dataset in the shell and find the diseased and healthy sample columns.
- 2For four genes, calculate fold change by dividing diseased expression by healthy expression.
- 3Label each gene or based on whether fold change is above or below one.
- 4Write one sentence on what an gene might mean for the disease.
- 5Save your fold-change table as your lab evidence.
What did this day actually feel like?
Expression data lab
LAB Straight into the lab after two days off, working real expression data. Which genes are turned up, which are turned down, compared against a control.
Expression is the layer between having a gene and it doing anything, which I had never really separated before.
Turned in: data table → Data Tables folder
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
The same day, drawn.
Straight into the lab after two days off, working real expression data. Which genes are turned up, which are turned down, compared against a control.
Fiction. There is no such student. The lessons, labs and dates are the real planned course; the student, the classmates and the conversations are invented.
🛠 Get unstuck · pick your level
Lab day: Tier 1 is the whole class at the bench. No extension today.
🔑 Today's words · 5
Tap a word in the lesson for a plain meaning and one example. Recycled into next week's Do-Now.
Do the work · 80-minute blockfirst 5 min = hook▸
💡 Big idea: Fold change divides diseased expression by healthy expression, so a result above 1 means the disease turned a gene up and a result below 1 means it turned the gene down.
- 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).
Differential expression, fold change, correlation, disease risk vs. diagnosis. · Expression data lab
Day 1 of this lesson. Open this exact section in myPLTW (find it in Clever, Microsoft sign-in), then do the work below.
Do this: Open Activity 3.1.5 Unlocking the Secrets in Our Genes in myPLTW and use the dataset to calculate fold change for four genes.
Mark the expression data activity complete after your fold-change table is saved.
No school Monday/Tuesday; this data lab is the opening hands-on benchmark for this unit.
Fold-change table for four genes with / labels saved in the course shell.
The official PLTW activity stays inside myPLTW. If myPLTW will not open, use F1 and E1-E3 on this page to complete today's local evidence decision, then make up the official activity when access returns. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
Check things off as you work, then submit. This tells Mr. Mendoza how you're doing so he can help the class. It does not replace turning in your producible through the submission route shown below.
Use the code Mr. Mendoza gave you, not your name. Saved on this device.
Differential expression, fold change, correlation, disease risk vs. diagnosis. · Expression data lab
Open Activity 3.1.5 Unlocking the Secrets in Our Genes in myPLTW and use the dataset to calculate fold change for four genes.
No school Monday/Tuesday; this data lab is the opening hands-on benchmark for this unit.
This is how Mr. Mendoza sees the class keeping pace with PLTW. Be honest, it only helps if it is accurate.
🎯 Use a table to calculate fold change and flag and genes.
- 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 or based on whether fold change is above or below one.
- Write one sentence on what an gene might mean for the disease.
- Save your fold-change table as your lab evidence.
Data table: Fold-change table for four genes with / labels and one sentence on the biological meaning of an upregulated gene.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Use the checklist just below and upload by 11:29 PM for full credit. Absent with an excused absence? You get two school days for every day you were absent, so this deadline moves with you.
| Task | Who |
|---|---|
| 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 or based on whether fold change is above or below one. | _______ |
| Write one sentence on what an gene might mean for the disease. | _______ |
| Save your fold-change table as your lab evidence. | _______ |
Working solo? Put your own name in "Who" for every row.
- You'll be able to calculate fold change from expression data.
- You'll be able to flag genes as or .
- 1Do thisUse a gene expression table to calculate fold change and flag upregulated and downregulated genes.
- 2Use this resource
- 3Submit thisData table: Fold-change table for four genes with upregulated/downregulated labels and one sentence on the biological meaning of an upregulated gene.
- 4Submit it here
- 1Open the drop folder.
- 2Sign in with your district Microsoft account, not a personal one.
- 3Upload the file, named Lastname_Firstname__Assignment Title.
- 4Your own upload panel says Uploaded with a green check: that is your receipt.
Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not. Genetics of Disease (Medical Interventions) › Differential expression, fold change, correlation, disease risk vs. diagnosis. › Data tableOpen the drop folder
Learn it · deck, reading, 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 the disease turned a gene up and a result below 1 means it turned the gene down.
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 the disease turned a gene up and a result below 1 means it turned the gene down.
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.
- • : The process by which the information in a gene is used to build a working product, usually a , through and .
- • mRNA: Messenger RNA, the single-stranded copy of a gene that carries instructions from the DNA in the to the ribosome to build a .
- • : Describing a gene or that the cell is making in larger amounts than usual, often in response to a signal or change in conditions.
- • : Describes a gene or whose activity or amount has been turned down, so the cell makes less of its product.
- • correlation: Two things tending to occur together, which does not by itself prove that one causes the other.
- • risk: The chance that a harmful event, such as getting a disease, will happen within a given group or time period.
- • diagnosis: The process of identifying a disease or condition by examining symptoms, history, and test results to explain what is wrong.
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.
Fold change = diseased expression / healthy expression; greater than 1 is , less than 1 is .
Limit: E1 supplies context or an observation; it does not by itself establish the explanation.
Fold change divides diseased expression by healthy expression, so a result above 1 means the disease turned a gene up and a result below 1 means it turned the gene down.
Limit: E2 is a teaching statement or comparison and must be checked against the task evidence.
You'll be able to calculate fold change from expression data.
Limit: E3 supports only the result or product criterion named here; it cannot justify a broader clinical or causal claim.
PLTW-GEND-2026-10-21 · 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 the lesson page.
- • Proceed because the readiness evidence is complete.
- • Pause and correct the named setup or gap.
- • Repeat the measurement because quality controls are not acceptable.
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: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Expression data lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
Reason for review: Your team must decide what the evidence from Expression data lab supports before submitting the labeled and result claim named on the lesson 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: Fold change = diseased expression / healthy expression; greater than 1 is , less than 1 is .
- • E2: Fold change divides diseased expression by healthy expression, so a result above 1 means the disease turned a gene up and a result below 1 means it turned the gene down.
- • E3: You'll be able to 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 the course shell.
- 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.
Tap the speaker to hear a term. Add two of these to your notebook glossary with a definition and an example in your own words.
Pick just 2 or 3 words from today and make them yours: write what each one means in your own words, name the context clue or evidence that helped, then give one example from what you actually did in Expression data lab. Try your own words first; the glossary is there if you get stuck. This is voluntary and counts as extra credit, so keep it short.
Saved on this device. Show Mr. Mendoza or add these to your notebook glossary to claim the extra credit.
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).
How to get there: open Clever and sign in with your Microsoft (district) account. Both myPLTW and Schoology are in Clever. Do the activity in myPLTW. Turn the work in on this site or hand it to Mr. Mendoza, because that is the step that counts as submitted. Schoology only shows your report-card grade later.
Check yourself · commit, then reveal▸
Claim ceiling for this check: The supplied lesson evidence can support an observation, pattern, classroom mechanism, or next-step decision about Expression data lab. It cannot by itself prove causation, establish a real clinical diagnosis, or justify action outside this classroom task.
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.
Fast retrieval with instant answers, not the commit-then-reveal check above. Try each from memory first: write what you remember about the earlier units, then check yourself here.
Go further and get help▸
I can name the procedure's purpose and the evidence I will record. I can identify each named hazard and the control that reduces it: No wet lab materials today; all work is computational. 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)Then submit your Data table. Turn this in at the drop folder with your district Microsoft sign-in, or hand it to Mr. Mendoza in class. Both count as submitted. Doing the activity in myPLTW does not.
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 expression- 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.
- SubmittedTurned in the right way, on the class site or handed to Mr. Mendoza in class, and confirmed. Not in Schoology: that is where the report-card grade appears later.
- 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.