Signals One™ Native Analytics for In Vitro Analysis

AT A GLANCE

Signals One Native Analytics in vitro analysis capability: From raw data to processed results faster than ever

The Signals One Native Analytics in vitro analysis capability accelerates data processing from raw input to results, streamlining common laboratory tasks with intuitive visual analytics. Integrated within the Revvity Signals One™ solution, it supports streamlined workflows, rapid decision-making, and assay adaptability without coding. Features include embedded data parsing, curve fitting with logistic 4-parameter fit, heat maps, dose-response visualization, and publishing to the Signals One Data-Driven Analytics capability.

With Native Analytics, you can keep simple analytics simple.

Common tasks should be simple. With Signals One Native Analytics, they are.

Visual analytics just became easier—and much more powerful. With the Native Analytics in vitro analysis capability, assay scientists can visualize data intuitively, following the same logical steps used in the lab. Plus, it’s integrated seamlessly with the entire Revvity Signals One™ solution.

Signals One customers now have access to Native Analytics for data visualization and quality control of experimental results. It includes the logistic 4-parameter (4P) fit, allowing data plots and curve fitting at dramatically faster speeds. Graphs, histograms, and scatter plots will be created directly with Native Analytics.

Integration with the Signals One solution throughout the workflow
Empowers in vitro curve fitting and visualization and group-comparison without custom coding
Rapid curve fit calculation for faster decision-making
Instantaneous point knockout and curve fit refresh
Publication to the Signals One Data-Driven Analytics capabilities

With the Native Analytics in vitro analysis capability, you can handle routine analytics quickly, with a few easy steps. For advanced workflows, leverage natively integrated Spotfire® for analysis that unites assay development, low- to ultra-high-throughput production assays, high-content screening, and in vivo studies in a single solution.

The Happy Path workflow

The Native Analytics in vitro analysis capability follows an intuitive sequence of steps matching how most screening experts work. A breadcrumb workflow makes it easy to start using in vitro analysis from day one.

Step 1. Materials

All experimental materials included in the analysis are loaded into the Native Analytics in vitro Analysis capability leveraging the Materials Library of Signals One.

Signals One Native Analytics Data Sheet Figure 1 IVAO Materials — Figure 1. Add materials included in the analysis.

Step 2. Configure plates

Configure the 96-well plates (or any other custom or standard plate size), including well format and concentrations, either from an existing plate map or by creating a new well format layer. Next, create a concentration layer.

Signals One Native Analytics data sheet Figure 2 IVAO Plate — Figure 2. Configuration of plate maps and content.

Step 3. Load instrument data

Add instruments, such as plate readers or high-content screening instruments from Revvity and other instrument providers. Each instrument generates an output file with readouts in a columnar or block format derived from the plate design.

Signals One Native Analytics Data Sheet Figure 3 IVAO Instrument Data — Figure 3. Load instrument data into the analysis.

Data parser

The Native Analytics in vitro analysis capability has access to an embedded data parser, making it straightforward to define how instrument data is analyzed. The parser reads output files from an instrument, defines the blocks of data corresponding to the results from the plate, and captures annotations.

Step 4. Assemble data

This flexible analytics-ready data assembler pulls data from multiple sources, including instruments, plates, materials, and liquid handlers, in preparation for analysis.

Signals One Native Analytics Data Sheet Figure 5 IVAO Data Assembly — Figure 5. Merge all data to run analytics.

Step 5. Run and explore visual analytics

Configure data normalization
Raw data from plates provided from the instrument is visualized as heat maps (upper Figure 6) or histograms. Once normalized, the data from different plates can be compared and analyzed together. In the example shown, each plate contains seven different compounds with serial dilutions, which allows creation of dose-response curves.

Signals One Native Analytics Data Sheet Figure 6 IVAO Data Normalization and Curve fit — Figure 6. Plate heat maps allow creation of dose-response curves.

Configure curve fitting

The dose-response curves can be displayed and expanded for QA/QC. Outlier data points can be excluded. Logistic 4-parameter (4P) fit includes lower and upper asymptotes, slope, and the inflection point to define a sigmoid curve (Figure 6). The analysis endpoints can be published into the Data-Driven Analytics capability.