Define: create and name the experiment
Every qPCR experiment in Clarida begins the same way: create a new experiment and give it a name — for example, "Tutorial Quick Guide". From the first click, you are working inside the structured Define → Design → Execute → Analyze → Report workflow that keeps the whole experiment in one place.
Design: enter sample and assay information
With the experiment open, you provide the design information, starting with sample preparation. If you already keep this information in a spreadsheet, you do not have to retype it: copy the sample table from Excel, paste it straight into Clarida, and import it. Assay information follows the same path — copy from Excel, paste it in, preview the data to check it parsed correctly, and confirm.
Next, Clarida proposes a default protocol. If it is not the one you want, switch to assisted mode and choose a different chemistry — in this walkthrough we pick a Bio-Rad SsoAdvanced mix. Clarida then generates a plate layout for you automatically. The proposed layout is usually sensible, so you can accept it as is, and a standard qPCR cycling protocol completes the design phase.
Execute: prepare mixes, fill plates, and start the run
Execution turns the design into bench actions. Clarida lists the materials to collect, then walks you through preparing each mix and marking the ones you have already made. When filling a plate, you add the mixes first and then the samples, marking each run as ready when it is done — repeating this across every run in the experiment.
If something goes wrong at the bench, you can click the affected well and add a note — for example, a pipetting mistake in well E2 of the third run. That note travels with the data into analysis, so the reason for any later exclusion is documented at the source. The final execute step is starting the run: select the run, pick the manufacturer, instrument type, and specific device, then start it to record the instrument used and the run start time.
Analyze: import Cq data and link it to runs
Once the instrument has finished, import the run files containing Cq data — three files in this experiment — and parse the selected runs. Clarida now holds both the imported measurements and the run annotation from the design stage. The second step is to link each data file to its matching run, so the measured Cq values are connected to the samples and assays you defined earlier.
Analyze: quality control
With the data linked, quality control begins. Filtering to show only wells that have issues surfaces auto-detected problems such as late Cq values and bad replicates, alongside the lab notes you added during execution — like the pipetting mistake in well E2. An observed issue corroborated by a bench note is an objective, documented basis for a data-handling decision — whether that is flagging the replicate or excluding it. Once justified exclusions are applied, the remaining replicates pass QC more cleanly, because the genuinely problematic points have been removed rather than to inflate the metric.
Amplification efficiency is checked next. The overview highlights any assay with a suboptimal efficiency; opening that assay reveals the offending outlier, which you can exclude directly from the graph. Everything recalculates automatically. The same approach applies to reference genes: if a gene is not stably expressed, exclude it and let the results recompute.
Clarida then summarizes general sample and assay quality metrics. Points inside the grey bands behave as expected; points in the red zone are outliers or fail a quality threshold. In this example the negative controls show no issues, but the normalization-factor QC flags a few samples that deviate from the typical range — the data suggest sample 13 has an exceptionally low cDNA concentration.
Analyze: explore results and store annotated analyses
After quality control, you can explore the results. Starting with the NRQ bar-chart view and picking a gene of interest, a clear pattern emerges: the Treatment property separates samples into high- and low-expression groups. You can save this exploration as a result and attach notes — here, recording the downregulation of the gene, and annotating the specific data point that was excluded earlier.
Clarida supports other analyses too, such as gene–gene correlation, which shows the reference genes are co-regulated, as expected. Every stored analysis is kept as an annotated snapshot, so it remains available for review at any point in the future.
