Dataset Information

Automated identification of stratifying signatures in cellular subpopulations.

ABSTRACT: Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry increase the parameterization of single-cell measurements, the scalability and subjectivity inherent in manual analyses slows both analysis and progress. We therefore developed Citrus (cluster identification, characterization, and regression), a data-driven approach for the identification of stratifying subpopulations in multidimensional cytometry datasets. The methodology of Citrus is demonstrated through the identification of known and unexpected pathway responses in a dataset of stimulated peripheral blood mononuclear cells measured by mass cytometry. Additionally, the performance of Citrus is compared with that of existing methods through the analysis of several publicly available datasets. As the complexity of flow cytometry datasets continues to increase, methods such as Citrus will be needed to aid investigators in the performance of unbiased--and potentially more thorough--correlation-based mining and inspection of cell subsets nested within high-dimensional datasets.

SUBMITTER: Bruggner RV

PROVIDER: S-EPMC4084463 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated identification of stratifying signatures in cellular subpopulations.

Bruggner Robert V RV Bodenmiller Bernd B Dill David L DL Tibshirani Robert J RJ Nolan Garry P GP

Proceedings of the National Academy of Sciences of the United States of America 20140616 26

Elucidation and examination of cellular subpopulations that display condition-specific behavior can play a critical contributory role in understanding disease mechanism, as well as provide a focal point for development of diagnostic criteria linking such a mechanism to clinical prognosis. Despite recent advancements in single-cell measurement technologies, the identification of relevant cell subsets through manual efforts remains standard practice. As new technologies such as mass cytometry incr ...[more]

PMID: 24979804

Similar Datasets

Project description:Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e., biomovies. They show the behavior of cells over time under controlled conditions. One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information. The extraction of the cell line by human observers is time-consuming and error-prone. Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high-resolution images with high noise. Our task is to characterize subpopulations in biomovies. In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even subpopulations, we propose to represent the cells by two new abstractions: the particle and the patch. We use a three-step framework: preprocessing, particle tracking, and construction of the patch lineage. First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames. Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space. Particle analysis includes the following: particle tracking, trajectory linking, filtering, and color information, respectively. Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time. Typical tracking problems may occur (e.g., appearance or disappearance of cells, spurious artifacts). They are effectively processed using trajectory linking and filtering. Third, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e., proximity and fluorescence intensity) and feature common ancestry. This step is based on patch finding, patching trajectory propagation, patch splitting, and patch merging. The main idea is to group together the trajectories of particles in order to gain spatial coherence. The final result of CYCASP is the complete graph of the patch lineage. Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies. We present results showing a computation time of less than 5?min for biomovies and simulated films. The method, presented here, allowed for the separation of colonies into subpopulations and allowed us to interpret the growth of colonies in a timely manner.

Dataset Information

Automated identification of stratifying signatures in cellular subpopulations.

Publications

Automated identification of stratifying signatures in cellular subpopulations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets