Expression data lab
This page has four parts, in the order you use them. Part 1 is what you need to know. Part 2 says what to do, in plain English. Part 3 shows the work done once on a case that is not yours. Part 4 is your own work and where to turn it in.
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.
Optional: listen or watch a unit review▸
Part 1 of 4 · Background and definitions▸
💡 Big idea: 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 that gene is less active in the disease sample.
- • = 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).
A reads fluorescence where labeled cDNA made from the sample's mRNA hybridizes to matching probes, so one chip can report the expression of thousands of genes at once.
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 that gene is less active in the disease sample.
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 labeled cDNA made from the sample's mRNA hybridizes to matching probes, so one chip can report the expression of thousands of genes at once.
New idea: 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 that gene is less active in the disease sample.
Visual or model: No figure is attached to this lesson. The readings, the worked example and E1 to E3 carry the evidence.
- 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 to E3 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 .
- • : A chip holding thousands of tiny spots that lets scientists measure the activity of many genes at once by detecting which spots light up.
- • : A grid where each square is coloured according to its value, so a table too large to read as numbers can be read as a pattern. On a each row is a gene, each column is a sample, and the colour shows how much of that gene the sample is making.
- • : How many times larger or smaller a measurement is than its reference. A of 3 means three times as much as the control. A fold change of 0.5 means half as much. A fold change of 1 means no change at all.
- • : 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.
Use it now: Choose one decision option. Cite E1 and E3, then explain how the rule connects the evidence to your choice. This is thinking practice for the one turn-in named under Turn in, not a second submission.
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 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.
Today's decision · 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.
- • Flag each gene up or down by its , and treat that as activity, not proof of cause.
- • Name the most gene as the cause of the disease, since it is working hardest in sick cells.
- • Hold any claim about what the disease is doing until repeated samples show the same .
Response: State one choice, cite at least two evidence IDs, explain the rule that connects them, and add one limitation. Use it inside the one turn-in named under Turn in; it is not a separate submission.
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 (Activity 3.1.4 in myPLTW if it opens, otherwise the practice dataset in Part 2 of this page) and find the diseased and healthy sample columns.
- • T2: For four genes, calculate by dividing diseased expression by healthy expression.
- • T3: Label each gene or based on whether 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 from expression data.
Measurements: Use only the measurements, units, graph, or counts supplied in today's task. No additional patient measurement is implied.
Uncertainty: This is a composite classroom scenario. Missing history, measurements, or confirmation tests remain unknown and limit the conclusion.
= diseased / healthy. First gene: 900 / 600 = 1.5, above 1, so . Second gene: 180 / 600 = 0.3, below 1, so .
A practice gene reads 900 in diseased and 600 in healthy tissue; another reads 180 diseased and 600 healthy. What is each , and which way is each gene regulated?
Diseased always goes on top. A above 1 means the disease turned the gene up; a value between 0 and 1 means down; 1 means no change. If you get a number above 1 for a gene that is clearly quieter in the disease, you divided the wrong way.
Compute the for each of your four genes with diseased on top, label each up or down, and check that the labels match which column is larger.
Students often think 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.
- 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.
Part 2 of 4 · Directions in plain English▸
2.1 Open your materials
Use the materials named in the first step below. Open lesson resources.
Practice expression dataset (made-up numbers for practice, not from any patient, class or study)
Microarray signal for four genes, one healthy sample and one diseased sample (fluorescence, arbitrary units)
| Gene | Healthy tissue signal | Diseased tissue signal |
|---|---|---|
| Gene K | 300 | 1,350 |
| Gene L | 900 | 270 |
| Gene M | 400 | 520 |
| Gene N | 640 | 160 |
- Fold change = diseased signal divided by healthy signal. Use Activity 3.1.4 in myPLTW if it opens; otherwise this is your dataset.
2.2 Start the work
Open the expression dataset (Activity 3.1.4 in myPLTW if it opens, otherwise the practice dataset in Part 2 of this page) and find the diseased and healthy sample columns.
Show all 5 required steps
- Open the expression dataset (Activity 3.1.4 in myPLTW if it opens, otherwise the practice dataset in Part 2 of this page) 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? Reopen the expression dataset (myPLTW, or the practice dataset on this page), 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.
- 0-8Hook and fold-change formula introduction; confirm dataset access
- 8-25Open dataset; locate diseased and healthy columns; calculate for genes 1-2
- 25-45Calculate 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
- 72-80Submit the fold-change table as one PDF in Schoology; preview the heat-map lesson that follows
- • Hook: Show a 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 the expression data and interpret what the numbers mean.
- • Exit goal: Fold-change table with / labels and one interpretation sentence saved before the bell.
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: Method comparison table should be done; fold-change table due today, and 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 in Part 4 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
Part 3 of 4 · Worked example▸
This is a parallel case, not your own. Read it for the order of the work, then do your own in Part 4.
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.
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 .
- • Working copy maps to RNA.
- • Production output maps to or a regulated cell function.
Part 4 of 4 · Your work▸
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.
Working rules for today
- No wet lab materials today; all work is computational.
- The practice dataset is made-up numbers, not anyone's health data; do not enter or share personal identifying information.
- Save your work frequently; do not rely on browser auto-save for the dataset.
4.1 Turn in your work
DueCheck the Schoology assignment for this day- 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
Submit as one PDF in Schoology.
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.
Today's lesson is in Schoology
Each school day has one lesson page in Schoology that holds everything for the day: the goal, the story so far, every table and data value, the numbered steps, and what to turn in. Open it first and work from it. The other rows for this day, if any, are the score line for a paper quiz or test and the John Carroll debate item; they do not add work. Check Schoology for each deadline.
- Today's Lesson · Tue 10/27 · Wk10 · Run the gel and read genotypes, then SNPs from one base to a trait(today's lesson page: the goal, the data, the steps, and what to turn in are all on it)
Link will not open? Open Schoology, choose your course and section, and find the title shown above.
- 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. Schoology attaches your name to your upload; on the page itself, write your period and code. Name the file: FirstName LastName - Assignment Title - YYYY-MM-DD.pdf. If you cannot get in, see Mr. Mendoza. Do not skip the work.
How this lesson connects
Keep using what you learned last class: A microarray reads fluorescence where labeled cDNA made from the sample's mRNA 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 that gene is less active in the disease sample.
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?▸
Optional practice for this unit's lab skills. Nothing in it is due today.
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 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: , , CDH3, MSX1, .
Goes with: The cleft gene set: more than one way to interrupt a face
From home, open the expression dataset (myPLTW if it opens, otherwise the practice dataset on this page) and complete the same analysis: calculate for four genes and label each up or .
NHGRI: gene expression fact sheet (background; the dataset is in myPLTW or on this page)Submit as one PDF in Schoology.
Class still runs. Complete today's work as this page describes (it is self-guided). Need the concept taught without a teacher? Use this authoritative explainer:
Genetic Science Learning Center: Genes and gene expressionThe 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 trackSources: slides, readings, and resources▸
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.
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.
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
Use this if you were absent, got stuck, or need another pass before you submit the lesson artifact.
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.
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