Why Science? | maSigPro and Time-Course Gene-Expression Profiles

Section 1 of 36

1. Define the independent sample: Time Basis

Start with the experimental unit and the question. A polished differential-expression table is only as strong as the sampling design that produced its columns.

maSigPro treats time as part of a regression design so profiles can be compared across experimental groups rather than collapsed into one contrast. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Next section

Section 2 of 36

2. Separate counts from molecules: Group Trajectory

Treat the count matrix as a chain of measurements: extraction, library construction, sequencing, alignment or quantification, annotation and summarisation all shape the number you see.

The foundational method first fits a global model to identify responsive genes and then applies variable selection to describe significant profile differences. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 3 of 36

3. Write the contrast before analysis: Global Model

Write the biological comparison, reference level and covariates before opening the results. This blocks the weak habit of designing a contrast around the most colourful plot.

The current Bioconductor package states support for time-course microarray and RNA-seq experiments. For this method, record both the observation that supports the step and the pattern that would weaken it.

Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 4 of 36

4. Name the uncertainty: Variable Selection

Separate biological variability, sampling noise, technical processing and model uncertainty. Each enters the evidence differently and none is cancelled by a large feature list.

Polynomial or other basis terms encode candidate shapes; the basis and degree must be chosen before a visually pleasing curve becomes persuasive. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 5 of 36

5. State the central assumption: Sampling Schedule

Name the assumption that lets the method learn across genes or samples. Once it is visible, diagnostics and sensitivity checks become purposeful rather than ritual.

Group indicators and time interactions allow questions about baseline differences, common temporal change and divergent trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 6 of 36

6. Keep the claim bounded: Profile Clustering

Keep the conclusion at the level measured. A computational ranking can guide validation; it does not prove cellular mechanism or clinical importance.

Sampling times determine which bends can be learned; no statistical curve recovers an unobserved rapid event with certainty. For this method, record both the observation that supports the step and the pattern that would weaken it.

Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Contents · Previous section · Next section

Section 7 of 36

7. Read the official evidence in scope: Time Basis

Read the primary paper beside the current official package documentation. The paper explains evaluated evidence; the package page describes the software that exists now.

If the same subject contributes repeated observations, within-subject correlation must be handled rather than counting every row as independent. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Previous section · Next section

Section 8 of 36

8. Build a trustworthy count matrix: Group Trajectory

Use documented non-negative integer counts with unique feature identifiers. Record genome and annotation releases, counting rules and strandedness so silent mismatches are discoverable.

Clustering selected profiles can aid interpretation, but cluster labels are summaries and not new inferential evidence. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 9 of 36

9. Create a complete sample sheet: Global Model

Match every matrix column to one metadata row. Include condition, batch, subject, time, sex, processing order and pairing where scientifically relevant.

A gene may be temporally responsive without showing a large endpoint difference, and an endpoint difference may hide opposing trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 10 of 36

10. Inspect library composition: Variable Selection

Inspect totals, detected features, zeros and dominant genes. A few abundant features can alter the comparative scale even when no code produces an error.

RNA-seq preprocessing, count transformation or model family must match the current documented workflow and be recorded explicitly. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 11 of 36

11. Map sample relationships: Sampling Schedule

Use ordination, correlations and sample-distance views as questions, not verdicts. Investigate surprising clusters against provenance before excluding any observation.

Model fit should be read alongside observed points, residual behaviour and sensitivity to influential times or samples. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 12 of 36

12. Filter by information: Profile Clustering

Apply a declared information filter before testing and preserve the retained universe. Filtering changes estimation and the number of decisions receiving multiplicity correction.

A temporal association supports a schedule-dependent expression claim, not a molecular clock mechanism unless separate experiments establish it. For this method, record both the observation that supports the step and the pattern that would weaken it.

Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Contents · Previous section · Next section

Section 13 of 36

13. Confirm the design can answer the question: Time Basis

Check that the design matrix has independent information for every requested effect. Perfect condition–batch confounding has no unique statistical solution.

maSigPro treats time as part of a regression design so profiles can be compared across experimental groups rather than collapsed into one contrast. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Previous section · Next section

Section 14 of 36

14. Choose the comparison scale: Group Trajectory

Choose scaling and offsets from biology and diagnostics rather than the number of discoveries. Save the factors because they are part of the evidence.

The foundational method first fits a global model to identify responsive genes and then applies variable selection to describe significant profile differences. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 15 of 36

15. Practise with invented evidence: Global Model

The table below is a fictional classroom example, visibly labelled so it cannot be mistaken for a study result.

The current Bioconductor package states support for time-course microarray and RNA-seq experiments. Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 16 of 36

16. Run the method reproducibly: Variable Selection

Record the package release, function calls, arguments, factor levels and seeds. A reproducible command history is a scientific explanation another analyst can challenge.

Polynomial or other basis terms encode candidate shapes; the basis and degree must be chosen before a visually pleasing curve becomes persuasive. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 17 of 36

17. Interpret the central output: Sampling Schedule

Read the method-specific statistic together with its sign and units. Confirm which level is the reference before writing increase or decrease.

Group indicators and time interactions allow questions about baseline differences, common temporal change and divergent trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 18 of 36

18. Control the testing family: Profile Clustering

Control false discoveries across all genes and planned contrasts. A threshold selected after browsing the ranking is not a prespecified decision rule.

Sampling times determine which bends can be learned; no statistical curve recovers an unobserved rapid event with certainty. For this method, record both the observation that supports the step and the pattern that would weaken it.

Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Contents · Previous section · Next section

Section 19 of 36

19. Read magnitude beside uncertainty: Time Basis

Show fold change or another effect beside adjusted evidence and uncertainty. Statistical detectability and biological importance are not synonyms.

If the same subject contributes repeated observations, within-subject correlation must be handled rather than counting every row as independent. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Previous section · Next section

Section 20 of 36

20. Plot every biological sample: Group Trajectory

Plot every independent sample behind each headline feature. Means and model lines can hide outliers, inconsistent direction and sparse support.

Clustering selected profiles can aid interpretation, but cluster labels are summaries and not new inferential evidence. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 21 of 36

21. Inspect the decisive diagnostic: Global Model

Choose a diagnostic that attacks the method’s defining assumption. A dashboard is useful only when each panel can change a decision.

A gene may be temporally responsive without showing a large endpoint difference, and an endpoint difference may hide opposing trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 22 of 36

22. Challenge the central assumption: Variable Selection

Ask what pattern would make the core assumption false, then look for it. Scientific confidence grows from surviving serious alternatives, not repeating one fit.

RNA-seq preprocessing, count transformation or model family must match the current documented workflow and be recorded explicitly. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 23 of 36

23. Test filtering sensitivity: Sampling Schedule

Repeat a small set of defensible count filters and record changes in sign, rank and adjusted evidence.

Model fit should be read alongside observed points, residual behaviour and sensitivity to influential times or samples. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 24 of 36

24. Test normalisation sensitivity: Profile Clustering

Compare justified normalisation choices with the same samples, feature universe and contrast. Explain consequential reversals rather than hiding them.

A temporal association supports a schedule-dependent expression claim, not a molecular clock mechanism unless separate experiments establish it. For this method, record both the observation that supports the step and the pattern that would weaken it.

Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Contents · Previous section · Next section

Section 25 of 36

25. Test sample influence: Time Basis

Where replication permits, leave out one biological sample at a time. A discovery that disappears with one sample needs narrower language.

maSigPro treats time as part of a regression design so profiles can be compared across experimental groups rather than collapsed into one contrast. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Previous section · Next section

Section 26 of 36

26. Test method settings: Group Trajectory

Vary only settings supported by the documentation and question. Do not tune until a preferred gene crosses a threshold.

The foundational method first fits a global model to identify responsive genes and then applies variable selection to describe significant profile differences. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 27 of 36

27. Compare an adjacent method: Global Model

Compare an adjacent method after aligning inputs and contrasts. Differences are clues about assumptions, not an automatic contest with one universal winner.

The current Bioconductor package states support for time-course microarray and RNA-seq experiments. For this method, record both the observation that supports the step and the pattern that would weaken it.

Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 28 of 36

28. Build an evidence card: Variable Selection

Summarise provenance, design, preprocessing, model, estimate, uncertainty, diagnostics, sensitivities and validation in one compact evidence card.

Polynomial or other basis terms encode candidate shapes; the basis and degree must be chosen before a visually pleasing curve becomes persuasive. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 29 of 36

29. Connect to fair testing: Sampling Schedule

This is the same logic as a fair school experiment: control what can be controlled, record what cannot, repeat independent units and separate observation from explanation.

Group indicators and time interactions allow questions about baseline differences, common temporal change and divergent trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 30 of 36

30. Try a classroom investigation: Profile Clustering

A safe classroom model can make the invisible assumption tangible without pretending to reproduce a laboratory sequencing experiment.

Measure a harmless response such as seedling height or cooling water at several planned times, plot every replicate, compare endpoint-only and full-trajectory conclusions, and discuss what the sampling gaps hide. The activity is a learning model, not an RNA-seq experiment, diagnostic test or health claim.

Sampling times determine which bends can be learned; no statistical curve recovers an unobserved rapid event with certainty. Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Contents · Previous section · Next section

Section 31 of 36

31. Connect to mathematics: Time Basis

Ratios, logarithms, distributions, smoothing, shrinkage and multiple-testing correction turn a biological question into quantities that can be checked.

If the same subject contributes repeated observations, within-subject correlation must be handled rather than counting every row as independent. For this method, record both the observation that supports the step and the pattern that would weaken it.

Archive counts, sample metadata, annotations, software versions, commands, filters and random seeds so another analyst can reconstruct the route from reads to claim.

Contents · Previous section · Next section

Section 32 of 36

32. Connect to computing literacy: Group Trajectory

Scripts make identifiers, filters, factor levels and versions inspectable. Computing literacy means understanding how code changes the claim, not merely making code run.

Clustering selected profiles can aid interpretation, but cluster labels are summaries and not new inferential evidence. For this method, record both the observation that supports the step and the pattern that would weaken it.

Count independently collected organisms, cultures, people or specimens as biological replicates. Extra reads, lanes, re-runs and repeated software fits do not create new experimental units.

Contents · Previous section · Next section

Section 33 of 36

33. Connect to education pathways: Global Model

RNA-seq connects biology, statistics and computing. It can illuminate study routes in life science, biotechnology and data analysis without promising admission or a career outcome.

A gene may be temporally responsive without showing a large endpoint difference, and an endpoint difference may hide opposing trajectories. For this method, record both the observation that supports the step and the pattern that would weaken it.

Pair adjusted evidence with direction, magnitude, uncertainty, sample-level plots and a sensitivity analysis able to contradict the headline rather than merely decorate it.

Contents · Previous section · Next section

Section 34 of 36

34. Ask the family question: Variable Selection

A useful family question is simple: how many independent biological samples support this result, and which assumption most affects the ranking?

RNA-seq preprocessing, count transformation or model family must match the current documented workflow and be recorded explicitly. For this method, record both the observation that supports the step and the pattern that would weaken it.

Declare the tested family, planned contrasts and exclusions before highlighting genes. False-discovery control belongs to the complete decision set, not just the displayed rows.

Contents · Previous section · Next section

Section 35 of 36

35. Use precise scientific language: Sampling Schedule

Write that the analysis supports differential expression or an association under the stated design and method. Avoid cause, cure, diagnosis or mechanism unless separate evidence justifies them.

Model fit should be read alongside observed points, residual behaviour and sensitivity to influential times or samples. For this method, record both the observation that supports the step and the pattern that would weaken it.

Build an evidence card naming the question, measured material, statistical unit, model, contrast, result, alternatives, validation status and narrowest defensible claim.

Contents · Previous section · Next section

Section 36 of 36

36. Finish with a reproducible bundle: Profile Clustering

Deliver counts or stable accessions, metadata, annotations, code, settings, complete results, plots, sensitivity analyses, package versions and the narrowest defensible interpretation.

A temporal association supports a schedule-dependent expression claim, not a molecular clock mechanism unless separate experiments establish it. For this method, record both the observation that supports the step and the pattern that would weaken it.

Use differential expression or association when that is what was estimated. Reserve cause, mechanism, diagnosis and benefit for designs and validation that can support those stronger words.

Source date and scope note

Primary studies and current official software documentation were checked for this article on 11 October 2026. Package interfaces, dependencies and recommendations can change; consult the linked current release page and guide before reproduction.

Did You Know?

Two analyses can use the same RNA-seq samples yet ask different questions: total gene abundance, relative exon usage, a named splicing event, a time-course profile or a variance-component association. Scientific literacy begins by naming the question before comparing the answers.

Final reader checklist

Confirm the independent biological sample count, design, measured material, annotation, preprocessing, statistical unit, exact contrast, effect scale, uncertainty, controls, sensitivity checks, validation plan and the boundary between association and causation.

Contents · Previous section · Continue to the Science Learning Hub