Data tables

A table in Dioptra is not a blank sheet. It has a kind, and the kind says what the rows and columns mean. That meaning is what lets Dioptra offer the right analyses and draw the right graph without asking you to explain your experiment twice.

Why not one flat sheet for everything? Because the same six numbers mean different things in different experiments. Six values in two columns might be two independent groups of three (an unpaired comparison) or three subjects measured twice (a paired one). The numbers cannot tell you which; the layout can. Prism made this choice, and it is the part of Prism people value most once they have learned it (the guided analysis, K1 in the complaint map). It is also the part they find hardest to learn (P9). Dioptra keeps the idea and tries to make the choice easier: each kind is explained in one sentence with a sketch when you create or paste a table, the paste dialog suggests a kind from the data, and when in doubt the Multiple variables kind is a safe default that can be reshaped into any other.

Shared vocabulary

• A data set is a named group of columns: one group in a Column table, one Y series in an XY table, one variable in a Multiple variables table. Data sets are lettered A, B, C, as in formulas.
• A data set has a subcolumn format. With replicate values each subcolumn holds one replicate (values(3) is three replicates side by side). With a summary format you type statistics computed elsewhere: mean, SD and N; mean, SEM and N; mean and a 95% confidence interval with N; mean and SD; mean and SEM; median and quartiles; lower and upper limits; or a single value. Summary formats exist because published papers and instrument software often give you only the summary, and with N present most analyses can still be computed exactly.
• Row titles label rows: subjects, samples, levels of a factor.
• An empty cell is missing, never zero. Zero is a number.

The eight kinds

Column

Groups side by side, one column each.

Use it for one factor: treatments, genotypes, time points measured in separate subjects. Rows mean nothing unless the design is paired or repeated, in which case each row is one subject and you choose a paired analysis.

Analyses: descriptive statistics, t tests (one-sample, unpaired Welch or Student, paired, ratio paired), Mann–Whitney, Wilcoxon, one-way ANOVA (with Welch and Brown–Forsythe variants), repeated-measures one-way ANOVA, Kruskal–Wallis, Friedman, normality tests, outliers, correlation, ROC curves, Bland–Altman. Default graph: column (bar) with every point shown. Prism: Column table.

XY

An X column and one or more Y data sets.

Use it whenever each value has an X coordinate that is a number: dose–response, time courses, standard curves, calibration. Each Y data set can have replicate subcolumns or a summary format (mean, SD, N).

Analyses: simple linear regression (with confidence and prediction bands and residual diagnostics), nonlinear regression (the curve-fitting library: 4PL and 5PL, exponential decay and association, Michaelis–Menten, binding, Gaussian, polynomials, custom equations), correlation, descriptive statistics, normality, outliers. Default graph: scatter. Prism: XY table.

Grouped

Rows are levels of one factor, data sets are levels of another, subcolumns are replicates.

Use it for two factors at once: genotype by sex, treatment by time. The question it answers is whether each factor matters and whether they interact.

Analyses: two-way ANOVA (type III sums of squares by default, with type I and II available; repeated measures on rows or columns), descriptive statistics, normality, outliers. Default graph: grouped column. Prism: Grouped table.

Contingency

Counts of subjects in each combination of two categories.

Use it when each subject falls into one row category and one column category and you counted them. The cells hold counts, not percentages and not measurements.

Analyses: chi-square (with Yates’ correction for 2 × 2 as an option), Fisher’s exact test (2 × 2 and larger, by the network algorithm), with relative risk and odds ratio and their confidence intervals. Dioptra does not rewrite what you paste: a value such as 1.5 is kept, and the analysis tells you that it expected whole counts (Prism silently turns “1.23” into “123”, P7). Default graph: column. Prism: Contingency table.

Survival

Time in X; for each group, 1 for an event and 0 for a censored observation.

Use it for time-to-event data: one row per subject, the time in X, and in that subject’s group column a 1 if the event happened at that time or a 0 if follow-up ended without it.

Analyses: Kaplan–Meier curves with Greenwood confidence intervals, median survival, log-rank and Gehan–Breslow–Wilcoxon comparisons, hazard ratios. Default graph: Kaplan–Meier with an at-risk table. Prism: Survival table.

Parts of whole

One data set of categories that add up to a whole.

Use it for fractions of a total: cell types in a population, reads per category.

Analyses: chi-square goodness of fit against equal proportions. Default graph: column (pie and donut are available, reluctantly: lengths are easier to compare than angles). Prism: Parts of whole table.

Multiple variables

One column per variable, one row per case, like a data frame.

Use it for anything shaped like a spreadsheet of observations, and whenever you are not sure. Each variable has a declared type: number, category (text labels such as “control”), text (identifiers and notes, never analysed) or date. When you paste, the type is inferred: all numbers become a number; ISO dates (2026-10-11) a date; text with at most twelve distinct values a category; anything else text. Text stays text: a gene called MARCH1 or SEPT2 is never turned into a date.

Analyses: descriptive statistics, t tests, Mann–Whitney and Wilcoxon between numeric variables, correlation (Pearson, Spearman, Kendall), principal component analysis, ROC, Bland–Altman, normality, outliers. To compare groups named in a category column, pivot long to wide: one data set per group (see Entering and shaping data). Default graph: column. Prism: Multiple variables table.

Nested

Groups as data sets, subgroups (such as animals) as subcolumns, observations stacked within each.

Use it when you measured several things (cells, fields, wells) within each experimental unit (animal, culture, patient). This is the table kind that protects you from the most common error in cell biology: treating 300 cells from three mice as 300 independent observations. The experimental unit here is the mouse, so n = 2 per group, not 6.

Analyses: descriptive statistics per subcolumn, normality, outliers, and t tests and one-way ANOVA on the subgroup means, which the results sheet says it did. Mixed-effects models are planned (see the roadmap). Default graph: SuperPlot: every observation small and pale, each unit’s mean large, coloured by unit. Prism: Nested table.

Choosing quickly

Ask three questions.

1. Do the values have a numeric X? Yes: XY (or Survival if X is time to an event).
2. Are they counts of subjects in categories? Yes: Contingency (two classifications) or Parts of whole (one).
3. How many grouping factors? One: Column. Two: Grouped. One, with several measurements inside each unit: Nested.

If none of these fits, or the data arrive in long format from another program, use Multiple variables and reshape later. Changing your mind costs nothing: transforms produce new tables and leave the original as it was.