MULTI2: Multi-Omics 2
Systems Medicine - or - What I learned about Arnold Schwarzenegger while studying breast cancer survival.
Technical University of Munich, Germany
On major obstacle in current medicine and drug development is inherent in the way we define and approach diseases. Here, we will discuss the diagnostic and prognostic value of (multi-)omics panels in general. We will have a closer look at breast cancer subtyping and treatment outcome, as case example, using gene expression panels - and we will discuss the current "best practice" in the light of critical statistical considerations. Afterwards, we will introduce computational approaches for network-based medicine. We will discuss novel developments in graph-based machine learning using examples ranging from Huntington's disease mechanisms via lung cancer drug target discovery back to where we started, i.e. breast cancer subtyping and treatment optimization - but now from a systems medicine point of view. We conclude that systems medicine and modern artificial intelligence open new avenues to shape future medicine.
Related paper: De novo pathway-based biomarker identification.
From Next Generation Sequencing to Next Generation Biomarkers: How Adaptive Focused Acoustics® is Transforming High-throughput Biology and Multi-omics Analyses
Covaris, United Kingdom
“Standardization of sample preparation” is our core mission with a focus on clinical and pharmaceutical samples. As pre-analytical processes are increasingly recognized as the limiting factors for sensitivity and specificity of biomarker detection, this is especially relevant for highly advanced analytical methods such as Next Generation Sequencing or Mass Spectrometry. The AFA® (Adaptive Focused Acoustics®) process is isothermal and non-contact, providing precise process control, which is beneficial to a number of scientific disciplines in both advanced biological and chemical applications. Its high level of experimental condition control enables processes to be developed or improved upon very quickly, easily, and reproducibly. Covaris Focused-ultrasonicators may be programmed for intensity, duration, and duty factor, supporting a wide variety of applications, from gentle mixing to extreme compound reformatting and dissolution.
This talk will present some of the major applications driven by AFA (e.g. DNA and chromatin shearing, cfDNA isolation, nucleic acid and protein extraction from FFPE). Many of these were launched recently, including a series of kits in the truChIP/truXTRAC product line. We will also discuss insights into current developments in automation and robotization, introducing the first focused-ultrasonicator integrated on a liquid handler deck with precise energy, control, and a proprietary scanning process. This instrument provides increased workflow efficiency, full automation, and high-throughput sample prep workflows.
Finding Signatures, Fingerprints and Prognostic Biomarkers in Large Biomedical Datasets: Computational and visual Approaches.
UKE Hamburg, Germany
In the last ten years, the amount of experimental data acquired by high-throughput technologies such as microarrays and RNA sequencing (RNAseq) has increased exponentially and resulted in partly Gigabyte-sized expression matrices. It is not uncommon that the researcher is faced with tables of 20000 rows (transcripts, genes) and 2000 columns (samples), necessitating mathematical, computational and visual approaches that are specifically tailored to these high-dimensional datasets. Frequently, the wet lab scientist “outsources” these analyses to an associated bioinformatics department, getting in return an often black box-type sophisticated analysis on which to rely. Here, it is important that a common ground on existing analysis approaches of this kind of data must be established.
In my talk, I will give a concise and comprehensive overview on existing methods to analyze large-scale gene expression data. Without going into deep mathematical details – these can be obtained from the literature – I will provide an outline on the important aspects and idiosyncrasies of current methodology based largely on the 2D- and 3D-visual depiction of data. Starting from very basic topics such as data cleaning/normalization/scaling, I will emphasize on efforts to uncover the intrinsic signature of the data (without imposing any presumptions), based on unsupervised clustering methods such as hierarchical clustering and dimension reduction methods such as PCA (linear) or the recent t-SNE approach (non-linear). I will demonstrate that in published datasets, the intrinsic structure of the data can be significantly different to the one assumed or defined by the experimental setup (such as batch effects). Next, I will give a summary on how to filter signatures that discriminate between different cellular states and how to use computationally expensive methods (bootstrapping, cross-validation) to avoid extracting signatures that perform great on the training set but bad on independent data (overfitting). Along these lines, a short introduction on recent machine learning approaches such a random forests, neural networks and gradient boosting will be delivered, and their advantage in finding predictive biomarkers and reduced discriminator sets through feature selection. For all the discussed approaches, I will also highlight the different pitfalls, for instance when to correct for multiple testing, why to never perform a statistical test before clustering, and (quite crucially) the identification of differential expression that is mimicked by the shifting of cellular proportions.