Cost note: This workflow is not free. It requires a paid Microsoft 365 plan with an active Copilot license, and some steps depend on plan-specific features.

A quick primer before the walkthrough:

In plain English: You already have Excel. Copilot lets you talk to your spreadsheet — say "plot OD600 vs time for each run" or "compare pH between conditions A and B" and it writes the chart, formula, or summary for you. This intermediate walkthrough uses illustrative fermentation runs as the example, but the approach applies to similar timecourse or replicate datasets (such as assay plates, HPLC runs, cell counts, or stability studies) subject to your specific analytical context.

Key terms:

• Timecourse — measurements taken at multiple timepoints during an experiment.
• OD600 — optical density at 600 nm, a common proxy for cell growth.
• Descriptive stats — summary metrics such as mean, standard deviation (SD), and coefficient of variation (CV).
• Inferential stats — statistical tests (e.g., t-test, ANOVA) used to evaluate differences between experimental groups across independent replicates.
• Predictive modeling — using existing data to estimate a trend or forecast values (here, fitting linear regression or exponential growth approximations).
• p-value — the probability of seeing a difference as large as the one observed if no true difference exists; smaller values suggest stronger evidence against that null assumption.
• R² — a number from 0 to 1 describing how closely data follow a fitted line or curve; values closer to 1 mean the model captures more of the variation.
• Replicate — an independent repeat of the same experimental condition, used to estimate variability.
• Log scale — a y-axis where equal distances represent multiplicative changes; exponential growth looks like a straight line on a log scale.
• Long format — a table layout with one observation per row (e.g. one timepoint for one run), as opposed to wide format where each run has its own column.

What you will need: Microsoft 365 with a Copilot license, and one Excel file with your dataset. Python in Excel (bundled with select M365 plans) is optional for advanced modeling steps.

Comfort level: Intermediate. Designed for users comfortable navigating Excel tables and reviewing generated formulas. Copilot drafts formulas and charts, but human review against your domain standards is required before accepting outputs.

Analyzing lab and fermentation data with Copilot in Excel

Copilot in Excel turns a spreadsheet into an interactive interface you can query using natural language prompts. For analytical workflows involving fermentation timecourses or replicate plates, this can support faster visualization, initial sanity checks, and standard summary workflows directly within Excel.

This walkthrough uses a synthetic fermentation dataset containing multiple independent replicate runs per condition as an illustrative example. The prompts can be adapted for cell-culture runs, stability studies, assay plates, or other structured tables with time or condition variables, provided they are adjusted to your specific experimental design and SOPs.

Prerequisites

• Microsoft 365 with an active Copilot license. Check your organization's current agreement with Microsoft regarding Copilot feature availability and administrative settings, as vendor offerings and terms change over time.
• Your data formatted in a single Excel sheet converted to a Table (Ctrl+T on Windows, Cmd+T on Mac). Copilot works best on standardized tables with explicit headers and one observation per row.
• Optional: Python in Excel enabled (Formulas → Insert Python) for optional curve-fitting steps.

Getting started with Copilot

Open your Excel file, select any cell inside your table, then click the Copilot icon on the Home tab (or open the Copilot side pane). If Copilot is grayed out, confirm the file is saved to OneDrive or SharePoint and that your table has clear headers.

Why long format matters. A wide-format table might look like this:

A long-format table looks like this:

Copilot, pivot tables, and statistical functions generally expect long format because each row is one observation. If your data are wide, ask Copilot to unpivot them before proceeding.

The example dataset

The table below shows an illustrative export from a bioreactor study with three independent replicate runs per condition (Runs R1–R3 in Condition A; Runs R4–R6 in Condition B). Note that all parameter thresholds, sample sizes, and ranges in this tutorial are example values for demonstration purposes only:

Structure your dataset with one row per timepoint per run ("long format"). Statistical testing and visualization tools process long-format tabular data with adequate independent replication more reliably than single-run or wide pivot formats.

Step 1 — Sanity check the data

Before initiating statistical analysis, use Copilot to inspect table structure and summarize contents:

Summarize this table: number of rows, unique Runs, unique Conditions, 
time range in hours, and any missing values by column.

A good response will look something like: 24 rows, 6 unique Runs, 2 Conditions (A and B), Time_h range 0–24, no missing values. If the counts do not match your expectation — for example, only 5 runs appear or Time_h stops at 12 — stop and inspect the table for hidden filters, blank rows, or inconsistent labels ("Condition A" vs "A").

Review Copilot's response in the side pane for potential discrepancies, such as missing timepoints, whitespace formatting errors in categorical variables, or incomplete sensor channels.

Next, run an automated data-quality check tailored to your assay parameters:

List any rows where Time_h is negative, OD600 is below 0, pH is outside 4–9, 
or DO_pct is outside 0–110. Put them on a new sheet called "Flagged".

Note: Data validation boundaries (e.g., pH 4–9 or DO 0–110%) are assay-specific examples. Always define screening rules in accordance with your laboratory's standard operating procedures and expert scientific judgment.

Step 2 — Plot the growth curves

Generate basic visual representations of your timecourse data across all replicate runs:

Insert a line chart of OD600 versus Time_h, with one line per Run, colored by Condition. 
Add a legend and label the axes.

This gives a first view of how all six runs grow over time and whether the two conditions separate.

To refine axis scales or series formatting:

Redo the chart with Time_h on the x-axis, OD600 on the y-axis (log scale), 
one line per Run, and use dashed lines for Condition B runs.

A log scale is useful because exponential growth appears as an approximately straight line, making it easier to pick out the exponential phase by eye.

To visualize multi-parameter trends on dual axes:

Create a chart with Time_h on the x-axis, pH on the left y-axis, and DO_pct 
on the right y-axis, for Run R1 only.

Dual-axis charts help compare two variables that have different units (pH and dissolved oxygen percentage) on the same time axis.

To break out individual runs:

For each Run, insert a small line chart of OD600 vs Time_h and arrange them 
in a grid on a new sheet called "Run panels".

Small multiples make it easier to spot one run that behaves differently from the others, which can be hidden in an overlaid chart.

Step 3 — Descriptive statistics

Prompt Copilot to build summary statistics tables across the independent replicates:

On a new sheet called "Summary", add a table with one row per Condition and columns for:
- mean and SD of final OD600 (Time_h = 24 across Runs in that Condition)
- mean and SD of pH at Time_h = 12
- CV (%) of final OD600 across Runs in that Condition
Round to 2 decimals.

What these terms mean in this context:

• Mean — the average value across the three replicate runs at the chosen timepoint.
• SD (standard deviation) — how spread out the three run values are around the mean.
• CV (coefficient of variation) — SD divided by mean, expressed as a percentage; a quick way to compare variability relative to the size of the measurement.

From the example table, the final OD600 values at Time_h = 24 are:

• Condition A: 6.05, 6.12, 5.98 → mean ≈ 6.05, SD ≈ 0.07, CV ≈ 1.2%
• Condition B: 5.80, 5.72, 5.88 → mean ≈ 5.80, SD ≈ 0.08, CV ≈ 1.4%

In this illustrative dataset, a CV above roughly 10–15% would suggest the replicates are more variable than expected — the figure is an example only, and the limits that apply to your work are the ones set by your assay validation criteria and SOP and warrant inspection before proceeding to statistical tests.

Always inspect generated Excel formulas. Structured references (e.g., [@[OD600]]) are preferable to hard-coded range references (B2:B25) as they maintain integrity when rows are added or filtered.

Step 4 — Comparative statistical tests

Request comparative analyses directly in the sheet. Valid statistical inference requires independent replicate runs per condition (such as $n = 3$ runs in Condition A vs. $n = 3$ runs in Condition B) to estimate within-group variance:

Do a two-sample t-test comparing final OD600 at Time_h = 24 between Condition A (Runs R1, R2, R3) 
and Condition B (Runs R4, R5, R6), assuming unequal variances. Report the t statistic, degrees 
of freedom, and p-value on the Summary sheet.

In plain English, this test asks: "If the two conditions truly had the same final OD600, how surprising would it be to see a difference as large as the one in our data?" A small p-value means the observed difference is unlikely under that assumption, but it does not prove why the difference exists or that it is biologically meaningful.

"Assuming unequal variances" (Welch's t-test) is the safer default when you are not sure the two conditions have identical variability.

For study designs evaluating three or more conditions with adequate independent replicates:

Perform a one-way ANOVA on final OD600 by Condition across all replicate runs. 
Report the F statistic and p-value. If the overall p < 0.05, run an appropriate 
post-hoc pairwise comparison procedure (such as Tukey's HSD) to identify which 
specific condition pairs differ significantly.

A plausible Copilot output for the t-test might look like:

t ≈ 5.2, df ≈ 3.9, p ≈ 0.007
Condition A mean final OD600 ≈ 6.05
Condition B mean final OD600 ≈ 5.80

Use this only as a starting point; always verify the underlying numbers yourself.

Scientific verification requirement: Generated statistical outputs must be independently validated before citation or formal reporting. Ask Copilot to output its underlying formulas:

Show the exact cells and formulas used for the t-test and ANOVA, and list which rows 
were treated as final OD600 for each Condition.

Verify that selected rows match your study design and that distributional assumptions (e.g., independence of replicates, normality, variance homogeneity) satisfy your organization's analytical guidelines and expert scientific judgment.

Step 5 — Simple predictive modeling (Illustrative examples)

The mathematical relationships and time windows below are examples for demonstration. Adapting curve-fitting parameters requires domain expertise specific to your biological system.

5a. Trendlines and growth rate μ

On the OD600 vs Time_h chart, add an exponential trendline for each Run 
in the exponential-growth window (Time_h between 0 and 12). Display the 
equation and R² on the chart.

Fitting exponential growth models ($\text{OD} = \text{OD}_0 \cdot e^{\mu \cdot t}$) allows estimation of specific growth rates ($\mu$). Prompt Copilot to compute these metrics in your summary sheet:

For each Run, compute μ by fitting ln(OD600) vs Time_h with a linear regression 
over Time_h 0 to 12. Report μ (slope) and doubling time = ln(2)/μ on the Summary sheet.

For Run R1, the OD600 values from 0 to 12 h are 0.10, 1.24, and 5.10. A straight-line fit of ln(OD600) versus Time_h gives a slope μ. If Copilot returns μ ≈ 0.35 h⁻¹, the doubling time would be approximately ln(2) / 0.35 ≈ 1.98 hours. That means the population is doubling roughly every 2 hours during that window.

Confirm whether Copilot uses functions like LINEST or SLOPE/INTERCEPT on LN(OD600), and verify exponential phase boundaries against your actual growth profiles. An R² below ~0.95 in this window suggests the growth is not well approximated by a simple exponential and you should reconsider the time window.

5b. Linear regression on secondary metrics

Fit a linear regression of Glucose_g_L (y) versus Time_h (x) for each Run in 
Condition A. Report slope, intercept, and R² on the Summary sheet.

In this context, the calculated slope serves as an estimate of consumption rate over the chosen window.

5c. Optional: Curve fitting via Python in Excel

If Python in Excel is enabled in your environment:

Using Python in Excel, fit a logistic growth model to OD600 vs Time_h for Run R1 
and add the fitted curve to the chart. Report the fitted parameters (K, r, N0).

Copilot will generate a Python cell using libraries like scipy.optimize.curve_fit. Review all code for mathematical logic and parameter bounds before relying on results.

Step 6 — Export a shareable summary

Create a one-page summary sheet titled "Run report" with:
- The growth-curve chart
- The Summary stats table
- A short bulleted "Interpretation" section written from the numbers above
Then export this sheet as a PDF.

Ensure all generated narrative text in the "Interpretation" block is thoroughly reviewed for accuracy and domain alignment by qualified scientific personnel.

Verification protocols

AI assistant interfaces may occasionally interpret user intent incorrectly or generate flawed formulas. Common failure modes include:

• Selecting incorrect columns with similar labels (e.g., OD600 vs OD600_corrected).
• Omitting missing values or blank rows without explicit user notification.
• Defaulting to visualization formats that obscure data distributions or outliers.

If a Copilot result looks wrong, try one of these recovery prompts:

• "Show the exact cells and formulas you used."
• "Recalculate using only rows where Condition = 'A' and Time_h = 24."
• "Compare your answer to a manual AVERAGE of the same range and explain any difference."
• "Use structured table references instead of fixed ranges like B2:B25."

Prior to reporting or archiving results:

1. Click into all generated formula cells to verify mathematical logic and references.
2. Confirm exact row selections and sample counts used by Copilot for each calculation.
3. Perform spot-check calculations independently using standard statistical tools or manual verification.
4. Maintain an immutable, read-only backup of raw primary data. Note that Copilot outputs may support, but do not satisfy, validated-records requirements (such as 21 CFR Part 11 or GxP compliance framework standards).

Key considerations and precautions

• Workbook modifications: Copilot applies changes directly to active workbooks. Always perform analyses on a working copy of primary data files.
• Data privacy and governance: Check your organization's current agreement with the vendor and consult internal data governance policies before processing proprietary data or patient-related information, as vendor terms change over time. Ensuring compliance with PHI handling guidelines and corporate data policies remains the reader's responsibility.
• Interpretation boundaries: Linear regressions and fitted metrics reflect statistical associations in the provided sample size; inferring biological causality requires rigorous experimental design and appropriate replication.

When to transition to specialized platforms

While Copilot in Excel aids rapid exploration, complex scientific workflows should transition to dedicated programming environments (such as R or Python) or validated analytical platforms when requiring:

• Mixed-effects modeling (e.g., accounting for run-to-run random effects).
• Non-parametric bootstrapping or Bayesian parameter estimation.
• Fully automated, reproducible pipelines across large multi-plate datasets.
• Advanced model selection criteria (AIC/BIC), cross-validation, or complex non-linear kinetics.

Related resources

• Adapt these visualization steps for assay plate workflows by substituting well coordinates for timepoints and raw fluorescence or absorbance signals for optical density.
• Request Copilot to generate quality control charts (e.g., mean ± 3 SD limits across historical runs) aligned with your lab's quality management protocols.
• Review Organizing and checking assay data with Copilot in Excel for details on upstream data preparation.