Karen Sachs | Stanford University (original) (raw)

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Papers by Karen Sachs

Research paper thumbnail of Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

Research paper thumbnail of Analysis of signaling pathways in human T-cells using Bayesian network modeling of single cell data

… , 2004. CSB 2004. …, Jan 1, 2004

Research paper thumbnail of Characterization of patient specific signaling via augmentation of Bayesian networks with disease and patient state nodes

… in Medicine and …, Jan 1, 2009

Characterization of patient-specific disease features at a molecular level is an important emergi... more Characterization of patient-specific disease features at a molecular level is an important emerging field. Patients may be characterized by differences in the level and activity of relevant biomolecules in diseased cells. When high throughput, high dimensional data is available, it becomes possible to characterize differences not only in the level of the biomolecules, but also in the molecular interactions among them. We propose here a novel approach to characterize patient specific signaling, which augments high throughput single cell data with state nodes corresponding to patient and disease states, and learns a Bayesian network based on this data. Features distinguishing individual patients emerge as downstream nodes in the network. We illustrate this approach with a six phospho-protein, 30,000 cell-per-patient dataset characterizing three comparably diagnosed follicular lymphoma, and show that our approach elucidates signaling differences among them.

Research paper thumbnail of Learning signaling network structures with sparsely distributed data

Journal of …, Jan 1, 2009

Flow cytometric measurement of signaling protein abundances has proved particularly useful for el... more Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.

Research paper thumbnail of Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum

Research paper thumbnail of Bayesian network approach to cell signaling pathway modeling

Research paper thumbnail of Causal protein-signaling networks derived from multiparameter single-cell data

Research paper thumbnail of Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE

Research paper thumbnail of Analysis of signaling pathways in human T-cells using Bayesian network modeling of single cell data

… , 2004. CSB 2004. …, Jan 1, 2004

Research paper thumbnail of Characterization of patient specific signaling via augmentation of Bayesian networks with disease and patient state nodes

… in Medicine and …, Jan 1, 2009

Characterization of patient-specific disease features at a molecular level is an important emergi... more Characterization of patient-specific disease features at a molecular level is an important emerging field. Patients may be characterized by differences in the level and activity of relevant biomolecules in diseased cells. When high throughput, high dimensional data is available, it becomes possible to characterize differences not only in the level of the biomolecules, but also in the molecular interactions among them. We propose here a novel approach to characterize patient specific signaling, which augments high throughput single cell data with state nodes corresponding to patient and disease states, and learns a Bayesian network based on this data. Features distinguishing individual patients emerge as downstream nodes in the network. We illustrate this approach with a six phospho-protein, 30,000 cell-per-patient dataset characterizing three comparably diagnosed follicular lymphoma, and show that our approach elucidates signaling differences among them.

Research paper thumbnail of Learning signaling network structures with sparsely distributed data

Journal of …, Jan 1, 2009

Flow cytometric measurement of signaling protein abundances has proved particularly useful for el... more Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.

Research paper thumbnail of Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum

Research paper thumbnail of Bayesian network approach to cell signaling pathway modeling

Research paper thumbnail of Causal protein-signaling networks derived from multiparameter single-cell data

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