CytoNorm: A Normalization Algorithm for Cytometry Data

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...

Per-channel basis normalization methods for flow cytometry data.

Between-sample variation in high-throughput flow cytometry data poses a significant challenge for analysis of large-scale data sets, such as those derived from multicenter clinical trials. It is often hard to match biologically relevant cell populations across samples because of technical variation in sample acquisition and instrumentation differences. Thus, normalization of data is a critical ...

cDNA Microarray Data Normalization

A cultural algorithm for data ‎clustering‎

Clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. In this paper, a Cultural Algorithm (CA) is presented to optimize partition with N objects into K clusters. The CA is one of the effective methods for searching into the problem space in order to find a n...

Normalization of mass cytometry data with bead standards.

Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with interv...