BRM5013 Chap.12 Imaging Data and Scientific Figures
Imaging Data and Scientific Figures
Quantitative imaging begins with the biological question and the correct experimental unit. This chapter connects segmentation and measurement to display and statistical analysis, explains nested cell observations, defines error bars and sample sizes, and shows why speed, displacement, persistence and directionality support different movement claims.
What this chapter covers
- 01
Biological question and measurement choice
- 02
Experimental unit and nested cells
- 03
Axes, units and point meaning
- 04
Error bars, sample size and tests
- 05
Effect size and biological relevance
- 06
Speed, displacement and persistence
Worked example · free
Plan a migration comparison
- +1Treat cultures as the independent biological units.
- +1Calculate the defined cell metric within each culture.
- +1Plot culture-level observations with units and uncertainty.
- +1Conclude about that metric and name unmeasured movement features.
Key terms
- Experimental unit
- The independently treated entity that determines the valid level of inference.
- Error bar
- A plotted summary of variability or uncertainty whose meaning must be defined.
- Effect size
- The magnitude of a difference or association, interpreted alongside uncertainty and significance.
Imaging Data and Scientific Figures FAQ
Why are many cells not automatically many replicates?
Cells measured within one dish share treatment and environment, so they are nested observations. Biological inference depends on independently treated cultures or organisms. Cell-level data remain valuable, but they do not replace independent replication.
Which statistical test does the practical require?
The captured brief asks for an appropriate analysis but does not state one mandatory test. The correct choice depends on group structure, pairing, assumptions and the experimental unit. Explain that reasoning and report any relevant post-hoc procedure.
Why can speed and displacement lead to different conclusions?
Speed measures movement rate, while displacement records net change in position. Cells can move quickly but change direction repeatedly, producing modest displacement. Persistence or directionality may be needed to explain the pattern fully. The variables also depend on the observation window, tracking quality and coordinate definition.
A treatment could reduce mean speed while leaving final displacement similar if trajectories become more direct, or preserve speed while reducing displacement through frequent turns. Plot trajectories and define the metric rather than summarising all behaviours as movement.
Keep cells nested within their independent cultures when making inferences, because many tracks from one preparation do not establish biological replication. A conclusion should name the measured property and time window, then identify the additional metric needed to distinguish alternative trajectory patterns.
Assessment move
Start every analysis with the experimental unit and the property being measured. Draw raw data tables with condition, replicate, object and value in separate columns. Practise writing legends that define points, axes, units, sample size, error bars and tests. Recompute printed summaries and check that prose and figure use the same direction words.
Before opening analysis software, write the biological question in a form that names the comparison and outcome. Decide whether the relevant property is intensity, proportion, area, speed, displacement, persistence or another defined measurement. Similar-looking variables answer different questions. Next, identify the independently treated culture, organism or specimen.
Cells, objects and fields nested within that unit can reveal within-sample variation but should not be silently counted as separate biological replicates. Sketch a tidy data table with one row per observation and separate columns for condition, biological replicate, technical level and measured value. Preserve those identifiers through summarisation.
If a culture contributes many tracked cells, show how cell-level information is combined or modelled while inference respects the culture-level design. Plan exclusions and thresholds before inspecting the desired group difference. Record how segmentation and positivity rules are applied across conditions, and test whether the conclusion depends on a narrow cut-off.
Choose a display that reveals underlying observations, spread and sample balance. Label axes with variables and units; state what each point represents; define whether error bars are SD, SEM, an interval or another summary; and give sample sizes at the correct levels. A graph should not make nested observations look independent. Select statistical analysis from the number of groups, pairing, hierarchy and assumptions.
The captured practical requires an appropriate analysis but does not prescribe one universal test, so justify the choice from the actual design. Read a p-value alongside effect size, uncertainty and biological relevance. A low value does not repair bias or establish a useful effect, while a non-significant comparison does not prove equality.
Practise migration scenarios in which speed, net displacement and directionality disagree, then write a conclusion that names only the measured property. Complete a numerical audit by recalculating summaries, checking units, matching legend to plot and confirming that prose uses the same direction as the data. Finish each answer with the effect, uncertainty, biological meaning and evidence boundary.
If another metric or replicate level is needed to resolve an alternative explanation, state it explicitly rather than broadening the conclusion.
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