The Experts below are selected from a list of 50130 Experts worldwide ranked by ideXlab platform
Hongyi Xin - One of the best experts on this subject based on the ideXlab platform.
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gmm demux sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
Genome Biology, 2020Co-Authors: Hongyi Xin, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Xinjun Wang, Xiaoyi ZhangAbstract:Identifying and removing Multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes Multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 Multiplet-induced fake cell types in a PBMC dataset.
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sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
bioRxiv, 2019Co-Authors: Hongyi Xin, Qi Yan, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Richard H Duerr, Kong ChenAbstract:Abstract Identifying and removing Multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High Multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of Multiplets in a scRNA-seq dataset. We propose a Gaussian-mixture-model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable Multiplets and estimates the percentage of sample-barcoding-undetectable Multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset. We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 Multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.
Sergei M. Kuzenko - One of the best experts on this subject based on the ideXlab platform.
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The massless integer superspin Multiplets revisited
Journal of High Energy Physics, 2018Co-Authors: Jessica Hutomo, Sergei M. KuzenkoAbstract:We propose a new off-shell formulation for the massless ${\cal N}=1$ supersymmetric Multiplet of integer superspin $s$ in four dimensions, where $s =2,3,\dots$ (the $s=1$ case corresponds to the gravitino Multiplet). Its gauge freedom matches that of the superconformal superspin-$s$ Multiplet described in arXiv:1701.00682. The gauge-invariant action involves two compensating Multiplets in addition to the superconformal superspin-$s$ Multiplet. Upon imposing a partial gauge fixing, this action reduces to the one describing the so-called longitudinal formulation for the massless superspin-$s$ Multiplet. Our new model is shown to possess a dual realisation obtained by applying a superfield Legendre transformation. We present a non-conformal higher spin supercurrent Multiplet associated with the new integer superspin theory. This fermionic supercurrent is shown to occur in the Fayet-Sohnius model for a massive ${\cal N}=2$ hyperMultiplet. We also give a new off-shell realisation for the massless gravitino Multiplet.
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New superconformal Multiplets and higher derivative invariants in six dimensions
Nuclear Physics B, 2017Co-Authors: Sergei M. Kuzenko, Joseph Novak, Stefan TheisenAbstract:Abstract Within the framework of six-dimensional N = ( 1 , 0 ) conformal supergravity, we introduce new off-shell Multiplets O ⁎ ( n ) , where n = 3 , 4 , … , and use them to construct higher-rank extensions of the linear Multiplet action. The O ⁎ ( n ) Multiplets may be viewed as being dual to well-known superconformal O ( n ) Multiplets. We provide prepotential formulations for the O ( n ) and O ⁎ ( n ) Multiplets coupled to conformal supergravity. For every O ⁎ ( n ) Multiplet, we construct a higher derivative invariant which is superconformal on arbitrary superconformally flat backgrounds. We also show how our results can be used to construct new higher derivative actions in supergravity.
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$ \mathcal{N} = 2 $ AdS supergravity and supercurrents
Journal of High Energy Physics, 2011Co-Authors: Daniel Butter, Sergei M. KuzenkoAbstract:We consider the minimal off-shell formulation for four-dimensional $ \mathcal{N} = 2 $ supergravity with a cosmological term, in which the second compensator is an improved tensorMultiplet. We use it to derive a linearized supergravity action (and its dual versions) around the anti-de Sitter (AdS) background in terms of three $ \mathcal{N} = 2 $ off-shell Multiplets: an unconstrained scalar superfield, vector and tensor Multiplets. This allows us to deduce the structure of the supercurrent Multiplet associated with those supersymmetric theories which naturally couple to the supergravity formulation chosen, with or without a cosmological term. Finally, our linearized $ \mathcal{N} = 2 $ AdS supergravity action is reduced to $ \mathcal{N} = 1 $ superspace. The result is a sum of two $ \mathcal{N} = 1 $ linearized actions describing (i) old minimal supergravity; and (ii) an off-shell massless gravitino Multiplet. We also derive dual formulations for the massless $ \mathcal{N} = 1 $ gravitino Multiplet in AdS. As a by-product of our consideration, we derive the consistent supergravity extension of the $ \mathcal{N} = 1 $ supercurrent Multiplet advocated recently by Komargodski and Seiberg.
Qiuyu Lian - One of the best experts on this subject based on the ideXlab platform.
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gmm demux sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
Genome Biology, 2020Co-Authors: Hongyi Xin, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Xinjun Wang, Xiaoyi ZhangAbstract:Identifying and removing Multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes Multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 Multiplet-induced fake cell types in a PBMC dataset.
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sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
bioRxiv, 2019Co-Authors: Hongyi Xin, Qi Yan, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Richard H Duerr, Kong ChenAbstract:Abstract Identifying and removing Multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High Multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of Multiplets in a scRNA-seq dataset. We propose a Gaussian-mixture-model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable Multiplets and estimates the percentage of sample-barcoding-undetectable Multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset. We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 Multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.
Yale Jiang - One of the best experts on this subject based on the ideXlab platform.
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gmm demux sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
Genome Biology, 2020Co-Authors: Hongyi Xin, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Xinjun Wang, Xiaoyi ZhangAbstract:Identifying and removing Multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes Multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 Multiplet-induced fake cell types in a PBMC dataset.
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sample demultiplexing Multiplet detection experiment planning and novel cell type verification in single cell sequencing
bioRxiv, 2019Co-Authors: Hongyi Xin, Qi Yan, Yale Jiang, Qiuyu Lian, Jiadi Luo, Carla Erb, Richard H Duerr, Kong ChenAbstract:Abstract Identifying and removing Multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High Multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of Multiplets in a scRNA-seq dataset. We propose a Gaussian-mixture-model-based Multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable Multiplets and estimates the percentage of sample-barcoding-undetectable Multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset. We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 Multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.
Piet Termonia - One of the best experts on this subject based on the ideXlab platform.
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Vector-tensor Multiplets
Fortschritte der Physik, 1999Co-Authors: Piet Claus, B. De Wit, Michael Faux, B. J. K. Kleijn, R. Siebelink, Piet TermoniaAbstract:We review the coupling of N=2 supergravity to vector-tensor Multiplets, based on the method of superconformal Multiplet calculus.
