MIPHENO: data normalization for high throughput metabolite analysis

High-throughput flow cytometry data normalization for clinical trials.

Flow cytometry datasets from clinical trials generate very large datasets and are usually highly standardized, focusing on endpoints that are well defined apriori. Staining variability of individual makers is not uncommon and complicates manual gating, requiring the analyst to adapt gates for each sample, which is unwieldy for large datasets. It can lead to unreliable measurements, especially i...

A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the appropriate normalization method to be used or the impact of a chosen method on the downstream analysis...

cDNA Microarray Data Normalization

Comprehensive Analysis of High-Throughput Screening Data

High-Throughput Screening (HTS) data in its entirety is valuable raw material for the drug-discovery process. It provides the most complete information about the biological activity of a company's compounds. However, its quantity, complexity and heterogeneity require novel, sophisticated approaches in data analysis. At GeneData, we are developing methods for large-scale, synoptical mining of sc...

High throughput data analysis in behavioral genetics

Anat Sakov, Ilan Golani, Dina Lipkind, Yoav Benjamini Tel Aviv University Abstract In recent years, a growing need has arisen in different fields, for the development of computational systems for automated analysis of large amounts of data (high-throughput). Dealing with non-standard noise structure and outliers, that could have been detected and corrected in manual analysis, must now be built ...