MicroFlex

Microplate assays produce semi-structured data in a variety of formats. Due to recent advances in high throughput biology, data from a single experiment can exceed billions or even trillions of data points. Combining, normalizing and analyzing this data requires a significant investment in time, money and resources.

Introducing MicroFlex, the world's first microplate library designed for bioscience data processing and software development. MicroFlex saves you time and money by simplifying code and eliminating errors in data handling.

Features:

• Two flavors:
  • Java
  • Javascript


• Immutable and mutable Java data types:
  • Integer
  • Double
  • BigInteger
  • BigDecimal


• Efficient output and input without loss of precision:
  • Byte
  • Short
  • Integer
  • Long
  • Float
  • Double
  • BigInteger
  • BigDecimal


• Plate stack, plate, well set and well data handling:
  
  
  • Standard mathematical operations:
    • Addition
    • Subtraction
    • Multiplication
    • Division
    • Modulus
    • Increment
    • Decrement
  
  
  • Standard logical operators:
    • Logical AND
    • Logical OR
    • Logical XOR
    • Left Shift
    • Right Shift Arithmetic
    • Right Shift Logical
    • Compliment
  
  
  • Descriptive statistics:
    • Mean
    • Skewness
    • Minimum
    • Maximum
    • Sample Variance
    • Percentile
    • Sum
    • Standard Deviation
    • Standard Error
    • N
    • Quadratic Mean
    • Population Variance
    • Geometric Mean
    • Sum of Squares
    • Kurtosis
    • More...
  
  
• Input and output in JSON, XML and a variety of other markup languages and formats.


• Flexible constructors and methods to accommodate data in a wide variety of formats.


• Full implementation of the Java math library for BigInteger and BigDecimal objects.


• Random data set generation for plate stacks, plates, well sets and wells

Computational Modeling

Biologists and chemists were slow to react to the digital revolution. Hypothesis generation and deep sequencing are now a reality and applied statistics and biological models can be harnessed to independently validate experimental results in a non-biased manner. Computer science and bioinformatics are no longer optional areas of expertise but rather essential skills for biologists and biochemists at all levels.

Quantifying synapse numbers is notoriously difficult due to noise generated through high background levels and non-specific binding. Positive controls are marginal at best, making it difficult to calculate the statistical effect size. To overcome these limitations I've generated two computational models to verify and predict the colocalization of pre and post synaptic neuronal markers.

These models generate random data sets against which actual experimental results can be compared. Because excitatory cortical neurons produce thousands of synapses in a single image, processing synapse data also requires the implementation of distributed cluster computing and parallel processing via multi-threading.

High Content Imaging

Producing tissues derived from pluripotent stem cells at scale is difficult even under ideal conditions. High content imaging overcomes these limitations by allowing researchers to extract a multitude of experimental parameters from a small subset of cells.

For the past five years my career has focused on the production of neuronal lineages from stem cells and their subsequent use as input to pharmaceutical screens for drug discovery. This work involved the development of novel imaging algorithms and quantification methods as well as instrument specific scripting to increase efficiency and reduce costs. Some highlights of my career as a high content imaging expert include:

• Completion of the world's first and only human motor neuron screen to identify regulators of the Survival of Motor Neuron gene, the cause of the debilitating neuromuscular disease Spinal Muscular Atrophy.
• Successful identification of small molecules to facilitate the direct conversion of skin cells to motor neurons. Development of a direct conversion protocol with an efficiency 150-300 times that of any existing published method.
• Creation of the world's first algorithm that accurately identifies and quantitates synapses as well as pre and post synaptic puncta. This assay normalizes synapse numbers so results can be compared across fields, wells, plates and experiments.
• ImageJ tools for extracting local minima, local maxima, amplitude, period and frequency of action potentials from an image stack as well as plugins to identify puncta in fields with high background noise.
• Experience with numerous high content imaging platforms and software suites. In-Cell, Opera, Operetta, ArrayScan and many others...

High Content Analysis

High content imaging produces terabytes of data and billions of data points. To process these large data sets biologists and chemists rely heavily upon data scientists, creating a bottle neck in the data processing pipeline.

The CPAnalyzer tool kit widens this bottle neck by providing a user friendly interface for researchers to parse and display results. Commercial high content imaging software is often complex, costly and lacks the features desired by wet lab researchers. Tailored specifically for high content analysis and ease of use, CPAnalyzer is completely free and contains the following features:

• Automatic chart rendering and customizable charts.


• Analysis using fields, wells, well sets and plates.


• Input using file formats and meta data from all major imaging platforms:
  • Thermo-Fisher - ArrayScan & CellInsight
  • Perkin-Elmer - Opera & Operetta
  • Molecular Devices - Image XPress
  • GE - In-Cell


• Output compatible with major wet lab data processing software:
  • GraphPad - Prism
  • Mathworks - MATLAB
  • Microsoft - Excel
  • Tibco - Spotfire
  • Tableau


• Descriptive statistics:
  • Mean
  • Skewness
  • Minimum
  • Maximum
  • Sample Variance
  • Percentile
  • Sum
  • Standard Deviation
  • Standard Error
  • N
  • Quadratic Mean
  • Population Variance
  • Geometric Mean
  • Sum of Squares
  • Kurtosis
  • More...

Please check back soon for links to GitHub and Maven repositories containing the beta release.