Mic qPCR Data Analysis: Export Options, File Formats, and How to Actually Work With Your Data

The Bio Molecular Systems Mic qPCR cycler exports data as .mic project files and CSV/Excel tables, but the CSV output has some quirks that can trip you up when you move to downstream analysis — non-standard column headers, Cq values reported with varying decimal precision, and a layout that doesn't play nicely with most third-party analysis tools without reformatting. If you're trying to get your Mic data into R, Python, Prism, or any automated analysis pipeline, you'll need to know what's coming out of the software and how to reshape it.

The short version: export your run as an Excel file from the micPCR software (Analysis > Export), grab the "Quantification" or "Results" sheet, and expect to do some cleanup. The Mic reports Cq (not Ct — same thing, different naming convention per MIQE guidelines), well position, sample name, and fluorescence data. But unlike the clean columnar exports you get from a QuantStudio or CFX96, the Mic's export bundles metadata and results in ways that require a bit of parsing.

What the Mic Software Actually Exports

The micPCR software (v2.x and later) gives you several export options under the Analysis menu:

• Project file (.mic): The native format. Contains raw fluorescence, run protocol, sample setup, and analysis parameters. Only opens in the micPCR software. Not useful for external analysis, but always save this as your archival copy.
• Excel export (.xlsx): This is your workhorse. It generates a multi-sheet workbook with tabs for Run Information, Sample Setup, Quantification Data, Melt Data (if applicable), and Standard Curve results. The Quantification Data sheet has columns for Well, Sample Name, Target, Cq, Quantity (if standards were defined), and sometimes ΔCq or ΔΔCq if you ran the built-in relative quantification.
• CSV export: Available but less structured. You get a flat file, but it sometimes includes merged header rows from the Excel template that break standard CSV parsing. Watch for rows above the actual data table — many users hit errors importing into R because read.csv() chokes on the metadata preamble.
• PDF report: Good for lab notebooks, useless for analysis.
• Image exports (PNG/SVG): For amplification plots and melt curves. Decent quality for figures, though most people re-plot in Prism or ggplot2 anyway.

One thing the Mic does well: it exports raw fluorescence cycle-by-cycle, which means you can recalculate Cq values yourself using your own baseline and threshold settings. This is valuable if you don't trust the software's automatic Cq determination (and honestly, for any automated threshold, a healthy skepticism is warranted).

Common Compatibility Issues

Here's where Mic users run into friction:

Non-standard column headers. The Mic labels its quantification cycle as "Cq" rather than "Ct" — which is technically more correct per the MIQE guidelines (Bustin et al., 2009), but if your analysis script or template expects a column called "Ct" or "CT," it'll fail silently or throw an error. A one-line rename fixes this, but it catches people off guard the first time.

Merged cells and multi-row headers in Excel exports. The Mic software likes to put the run name, date, and protocol info in the first few rows of the Excel file before the actual data table starts. If you're reading this into pandas with pd.read_excel(), you need to set skiprows appropriately — usually skiprows=3 or skiprows=4, but it varies by software version and whether you exported from the Quantification view or the Raw Data view. Check the file manually first.

Well position formatting. The Mic is a 48-well rotor-based instrument, so well positions are labeled 1–48 rather than the A1–H12 grid format used by 96-well instruments like the CFX96 or QuantStudio. Any tool that expects plate-grid coordinates won't map Mic wells correctly without conversion. This sounds trivial, but it matters if you're using analysis software that auto-assigns biological and technical replicates based on plate position.

Fluorescence data scaling. The Mic's raw fluorescence values tend to be on a different absolute scale than what you'd see from a QuantStudio or LightCycler 480. This doesn't affect Cq determination — that's relative to baseline and threshold within the run — but if you're comparing amplification curves across instruments or doing any cross-platform normalization, be aware that the raw RFU values aren't directly comparable.

Missing NTC annotation. Some Mic software versions don't explicitly flag no-template controls in the export file with a dedicated column. NTCs are just samples with a particular name (whatever you typed during setup). If you named them "NTC" consistently, great — you can filter on that string. If someone in the lab typed "neg control" or "water" or left it blank, your automated QC checks won't catch them.

Tips for Cleaner Exports and Faster Analysis

A few things that save time once you learn them:

Standardize your sample naming before the run. The Mic software lets you set up sample names in the run template. Use a consistent convention — something like GeneX_Treatment_BioRep1_TechRep1 — and your downstream parsing becomes trivial. Fixing inconsistent names after export is the number-one time sink in qPCR data analysis, on any instrument.

Export from the Analysis view, not the Run view. The Analysis view in micPCR software applies your threshold and baseline settings before export. If you export from the Run view, you may get raw data without computed Cq values, and then you're recalculating everything.

Use the Excel export over CSV. Counterintuitive if you're a code-first person, but the Excel file actually has better-structured sheets. You can read individual sheets with pd.read_excel(file, sheet_name='Quantification Data') in Python or readxl::read_excel() in R, skipping the metadata rows, and get a clean data frame. The CSV mashes everything together.

Re-export if you change analysis settings. The Mic software lets you adjust the fluorescence threshold and baseline range after the run. If you do this — say, moving the threshold from the auto-set value of 0.02 to a manual 0.05 to better fit your amplification curves — you need to re-export. The previously exported file still has the old Cq values. Obvious, but I've seen people spend hours troubleshooting "wrong" fold-change values because they analyzed a stale export.

Efficiency calculations from the Mic. If you ran a standard curve, the Mic software will report primer efficiency on the Standard Curve tab. The acceptable range is 90–110% (corresponding to a slope between −3.6 and −3.1 on the log-dilution vs. Cq plot). If your efficiency falls outside this and you're using the Livak ΔΔCq method (Livak & Schmittgen, 2001), your fold-change calculations assume 100% efficiency and will be inaccurate. Switch to the Pfaffl method (Pfaffl, 2001), which corrects for unequal efficiencies between your target and reference genes.

Working With Mic Data in Third-Party Tools

For GraphPad Prism: Copy the Cq values from the Excel export into a grouped data table. Prism doesn't have a qPCR-specific import, so you'll calculate ΔCq and ΔΔCq in the table or bring in pre-calculated fold changes. Run statistics on ΔCq values (not fold changes) — a paired or unpaired t-test for two groups, one-way ANOVA for multiple groups.

For R users: The readxl package handles the Mic Excel files well. Skip the header rows, rename "Cq" to "Ct" if your pipeline expects it, and convert the well numbers (1–48) to a factor. The pcr package on CRAN (Ahmed & Kim, 2018) can then handle ΔΔCq calculations, and the HTqPCR Bioconductor package works if you reshape to its expected input format.

For Python users: openpyxl or pandas.read_excel() with appropriate skiprows and sheet_name parameters. Most of the cleanup is stripping whitespace from column names (the Mic exports sometimes pad with spaces) and ensuring Cq values are numeric (undetermined wells may be exported as empty strings or "N/A" rather than NaN).

For any of these workflows, the main bottleneck isn't the analysis math — it's getting the data into the right shape. Every instrument exports differently, and the Mic's format is neither the best nor the worst. It's just different enough from the QuantStudio .eds or CFX .pcrd exports that you can't reuse import scripts across instruments without modification.

If you'd rather skip the reformatting entirely, VoilaPCR accepts Mic exports directly — upload the Excel file, map your samples and targets, and it handles the ΔΔCq calculations, outlier detection, and statistical tests without you needing to clean a single column header.