Data Science

Data science is a field that involves the use of advanced analytical tools and techniques to extract insights and knowledge from data. It combines various disciplines such as statistics, machine learning, and computer science to analyze and interpret data in order to derive actionable conclusions. Data scientists use a variety of methods to collect, process, and analyze large and complex data sets to identify patterns, trends, and relationships. Areas of Flagship expertise include Image Analysis and Genomics.

Image Analysis

A range of new imaging-based methods make it possible to explore the architecture of tissue samples both at the transcriptomics and proteomics level. Multiplexed in situ RNA detection methods map mRNA molecules at sub-cellular resolution, and multiplex immunohistochemical staining make it possible to detect and identify a large number of different cell types in the same tissue sample, enabling the discovery of their functional role inside the tissue architecture.

The first step toward further interpretation of the data is detection and decoding, or classification, of each individual object; in this case, resulting in maps of the locations of either specific mRNA molecules or cells. One of the key challenges in fully exploiting this type of spatially resolved data is the availability of appropriate computational methods. The second step in interpretation is to be able to quantify relationships and patterns in an unbiased and reproducible way, and provide confidence measures for observed patterns as compared to a more randomized organization. This is often referred to as spatial statistics.

Flagship Biosciences utilizes a combination of expert image analyst guidance, pathologist review, and machine learning methodology to ensure the best quality data from your tissue samples. Expert image analysts guide Flagship’s proprietary software through the image analysis pipeline from scanning through final results. Image QC is manually performed with guidance from Flagship’s software to identify artifacts in images. These artifacts are manually excluded from the analysis, and a custom spectral unmixing, specifically tuned to Flagship’s instruments, is performed for multiplex assays. Image analysts identify training regions within the image to train the machine learning algorithms to identify features and classifications specified by the client. These algorithms can be trained to almost any classification exercise desired. Quantification of biomarkers is obtained for the classification regions, and thresholds are set to bin each biomarker into categories, such as positive or negative and high, medium, and low.

At each stage of the analysis Flagship’s expert scientists and pathologists review the images and data to ensure the highest quality data possible.


Flagship Biosciences utilizes state-of-the-art commercial and open-source methods to analyze your genomic data. Whether it is NanoString’s GeoMx® or Illumina’s NGS systems, we have the experience to deliver to you the highest quality data and statistical analysis in the industry. Our computational scientists will use methods for the Bioconductor project such as NanoStringGeoMxSet and GeoDiff to perform better QC and statistical analysis than possible on NanoString’s GeoMx® Digital Spatial Profiler (DSP). These methods properly account for background expression and normalize without introducing bias into the data as in the Q# normalization in DSP-DA. The GeoDiff package also can fit mixed and standard negative binomial regression models to identify differentially expressed genes with greater power then possible with standard normal theory regression models. Starting from raw reads coming off of our Illumina next-generation sequencers, Flagship utilizes the power of StarAligner to perform alignment and quantification of RNA reads, even for splice variant data. Flagship uses the DESeq2 package for most models with the exception of experimental designs that require a mixed model when testing for differential expression. When a mixed model is required, we use the GLMMer package to fit a negative binomial mixed effect model. For both GeoMx and RNAseq, Flagship Computational Scientists have the training and experience to properly ascertain the correct model that corresponds with your experimental design to ensure you have the right results from our analysis. Our team has the experience to perform the most thorough QC of your data and to remedy most experimental artifacts with minimal impact on the results. Flagship has partnered with Genomenon® to provide our customers with access to Genomenon’s rare disease and oncology genetics library. The Mastermind Genomic Search Engine is used by hundreds of genetic labs worldwide to accelerate diagnosis, increase diagnostic yield, and assure repeatability in reporting genetic testing results. Mastermind Genomic Landscapes inform pharmaceutical and bio-pharma companies on precision medicine development, deliver genomic biomarkers for clinical trial target selection, and support CDx regulatory submissions with empirical evidence.