Iterative single-cell multi-omic integration using online learning.
Iterative single-cell multi-omic integration using online learning.
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DOI：
10.1038/s41587-021-00867-x
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发表时间：
2021-08
影响因子：
46.9
通讯作者：
Welch JD
中科院分区：
文献类型：
--
作者：
Gao C;Liu J;Kriebel AR;Preissl S;Luo C;Castanon R;Sandoval J;Rivkin A;Nery JR;Behrens MM;Ecker JR;Ren B;Welch JD
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative nonnegative matrix factorization (iNMF), an algorithm for integrating large, diverse, and continually arriving single-cell datasets. Our approach scales to arbitrarily large numbers of cells using fixed memory, iteratively incorporates new datasets as they are generated, and allows many users to simultaneously analyze a single copy of a large dataset by streaming it over the internet. Iterative data addition can also be used to map new data to a reference dataset. Comparisons with previous methods indicate that the improvements in efficiency do not sacrifice dataset alignment and cluster preservation performance. We demonstrate the effectiveness of online iNMF by integrating more than a million cells on a standard laptop, integrating large single-cell RNA-seq and spatial transcriptomic datasets, and iteratively constructing a single-cell multi-omic atlas of the mouse motor cortex. A new algorithm enables scalable and iterative integration of single-cell datasets.
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影响因子：
64.8
作者：
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者：
Shendure, Jay
下丘脑前区域的分子，空间和功能性单细胞分析。
DOI：
10.1126/science.aau5324
发表时间：
2018-11-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Moffitt JR;Bambah-Mukku D;Eichhorn SW;Vaughn E;Shekhar K;Perez JD;Rubinstein ND;Hao J;Regev A;Dulac C;Zhuang X
通讯作者：
Zhuang X
影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
64.5
作者：
Saunders A;Macosko EZ;Wysoker A;Goldman M;Krienen FM;de Rivera H;Bien E;Baum M;Bortolin L;Wang S;Goeva A;Nemesh J;Kamitaki N;Brumbaugh S;Kulp D;McCarroll SA
通讯作者：
McCarroll SA
影响因子：
3.7
作者：
RAND, WM
通讯作者：
RAND, WM