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JOM ◽  
2022 ◽  
Author(s):  
Jing Shi ◽  
Yuchen Yao ◽  
Jie Bao ◽  
Maria Skyllas-Kazacos ◽  
Barry J. Welch ◽  
...  

2022 ◽  
Vol 12 ◽  
Author(s):  
Zicheng Hu ◽  
Sanchita Bhattacharya ◽  
Atul J. Butte

Modern cytometry technologies present opportunities to profile the immune system at a single-cell resolution with more than 50 protein markers, and have been widely used in both research and clinical settings. The number of publicly available cytometry datasets is growing. However, the analysis of cytometry data remains a bottleneck due to its high dimensionality, large cell numbers, and heterogeneity between datasets. Machine learning techniques are well suited to analyze complex cytometry data and have been used in multiple facets of cytometry data analysis, including dimensionality reduction, cell population identification, and sample classification. Here, we review the existing machine learning applications for analyzing cytometry data and highlight the importance of publicly available cytometry data that enable researchers to develop and validate machine learning methods.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
Kridsadakorn Chaichoompu ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
...  

AbstractEpiScanpy is a toolkit for the analysis of single-cell epigenomic data, namely single-cell DNA methylation and single-cell ATAC-seq data. To address the modality specific challenges from epigenomics data, epiScanpy quantifies the epigenome using multiple feature space constructions and builds a nearest neighbour graph using epigenomic distance between cells. EpiScanpy makes the many existing scRNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities, including methods for common clustering, dimension reduction, cell type identification and trajectory learning techniques, as well as an atlas integration tool for scATAC-seq datasets. The toolkit also features numerous useful downstream functions, such as differential methylation and differential openness calling, mapping epigenomic features of interest to their nearest gene, or constructing gene activity matrices using chromatin openness. We successfully benchmark epiScanpy against other scATAC-seq analysis tools and show its outperformance at discriminating cell types.


2021 ◽  
Vol 27 (2) ◽  
pp. 1351-1362 ◽  
Author(s):  
Fahimeh Mohseni ◽  
Mehdi Khaksari ◽  
Raheleh Rafaiee ◽  
Kasra Rahimi ◽  
Pirasteh Norouzi ◽  
...  

Author(s):  
Dmitry A. Balaev ◽  
Sergei V. Semenov ◽  
Sergei N. Varnakov ◽  
Evgeniy Yu. Radionov ◽  
Yaroslav Al. Tretyakov

The magnetic properties of steel of a structural element of an aluminum reduction cell have been investigated in the temperature range of 300–900 K. The analysis of the temperature dependence of the saturation magnetization MS(T) showed (i) the applicability of the Bloch’s 3=2 law and a reason- able value of the Bloch’s constant for steel and (ii) the quadratic dependence MS(T) (1 - T2) in the temperature range of 380–700 K.


2021 ◽  
Author(s):  
Fulong Yu ◽  
Vijay G. Sankaran ◽  
Guo-Cheng Yuan

AbstractGenome-wide profiling of transcription factor binding and chromatin states is a widely-used approach for mechanistic understanding of gene regulation. Recent technology development has enabled such profiling at single-cell resolution. However, an end-to-end computational pipeline for analyzing such data is still lacking. To fill this gap, we have developed a flexible pipeline for analysis and visualization of single-cell CUT&RUN and CUT&Tag data, which provides functions for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregation, and visualization. Furthermore, it is also seamlessly integrated with the functions in original CUT&RUNTools for population-level analyses. As such, this provides a valuable toolbox for the community.


Author(s):  
Yanfang Zhang ◽  
Qiaoyun Liu ◽  
Dengpeng Chai ◽  
Qingjie Zhao ◽  
Yueyong Wang ◽  
...  

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