Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Yang, Yang; Sun, Hongjian; Zhang, Tiefu; Gong, Jialei; Wei, Yunbo; Duan, Yong‐Gang; Shu, Minglei; Yang, Yuchen; Wu, Di; Yu, Di

doi:10.1101/2021.01.12.426467

Cited by 14 publications

(21 citation statements)

References 65 publications

(72 reference statements)

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“…Specifically, UMAP first constructs a k‐nearest neighbour (kNN) graph to approximate the manifold structure of data, then embeds the kNN graph to two‐dimensional space to visualize the data (Figure 4B). UMAP has been shown to have a better capacity at maintaining local information as well as global structure in analyzing datasets for scRNA‐seq 70 …”

Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

“…UMAP has been shown to have a better capacity at maintaining local information as well as global structure in analyzing datasets for scRNA-seq. 70 A recent study systemically compared UMAP with other mainstream methods for dimensionality reduction including PCA and demonstrated that UMAP was superior to PCA for clustering accuracy, neighbour information preserving and feature separating. 70 In this study, a data set consisting of longitudinal transcriptome profiles of 65 SLE patients and 20 HCs was analyzed.…”

Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

“…Such clustering structures were found associated with patient visiting date and the trends of disease improvement or deterioration. 70 This is an example of the capacity of new methods that can effectively analyze omics data and strengthen the detection of the heterogeneity within patients for disease diagnosis and therapy choices.…”

Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

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Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Puri

Jiang

Yang

et al. 2021

Rheumatology & Autoimmunity

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Systemic lupus erythematosus (SLE) is a systemic autoimmune disease characterized by extraordinary heterogeneity, due to the complex pathogenesis and diverse manifestations. Stratification of patients for therapy and prognosis represents a major challenge to manage SLE. Conventional biomarkers for disease diagnosis and activity assessment provide very limited insight into immunological pathogenesis and therapeutic response rates. The advancement of “omics” technologies including genomics, transcriptomics, proteomics, and metabolomics has constituted an unprecedented opportunity to characterize the immunopathological landscape in individual patients with SLE. Indeed, genomic studies reveal a subset of SLE patients carrying one or more functional single nucleotide polymorphisms (SNPs) underlying immune dysregulation while transcriptomic studies have revealed subgroups in SLE patients showing distinct signatures for Type I interferon (TI‐IFN) pathway activation or aberrant differentiation of B cells into plasma cells. This review will summarize results from the latest studies using omics technology to understand SLE heterogeneity. In addition, we propose that the application of artificial intelligence, such as by machine learning‐based nonlinear dimensionality reduction method uniform manifold approximation and projection (UMAP) can further strengthen the analysis of omics big data. The combination of new technology and novel analysis pipeline can lead to breakthroughs in stratifying SLE patients for a better monitoring of disease activity and more precise design of treatment regime, not only for conventional immunosuppression but also novel immunotherapies targeting B‐cell activating factor (BAFF), TI‐IFN, and interleukin 2 (IL‐2).

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Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

Section: Improvement Of the Mining Of Omics Data By New Analytic Methods Including Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Puri

Jiang

Yang

et al. 2021

Rheumatology & Autoimmunity

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“…To explore the topic words visually, we use the Uniform Manifold Approximation and Projection (UMAP), an effective [59] and efficient [3] dimensionality reduction method, to reduce the dimensionality of the GloVe embeddings. This method works by finding lowdimensional projections of the data that preserves their topological structures in highdimensional space as much as possible [39].…”

Section: Identifying Over-represented Termsmentioning

confidence: 99%

Characterizing partisan political narrative frameworks about COVID-19 on Twitter

Jing

Ahn

2021

EPJ Data Sci.

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The COVID-19 pandemic is a global crisis that has been testing every society and exposing the critical role of local politics in crisis response. In the United States, there has been a strong partisan divide between the Democratic and Republican party’s narratives about the pandemic which resulted in polarization of individual behaviors and divergent policy adoption across regions. As shown in this case, as well as in most major social issues, strongly polarized narrative frameworks facilitate such narratives. To understand polarization and other social chasms, it is critical to dissect these diverging narratives. Here, taking the Democratic and Republican political social media posts about the pandemic as a case study, we demonstrate that a combination of computational methods can provide useful insights into the different contexts, framing, and characters and relationships that construct their narrative frameworks which individual posts source from. Leveraging a dataset of tweets from the politicians in the U.S., including the ex-president, members of Congress, and state governors, we found that the Democrats’ narrative tends to be more concerned with the pandemic as well as financial and social support, while the Republicans discuss more about other political entities such as China. We then perform an automatic framing analysis to characterize the ways in which they frame their narratives, where we found that the Democrats emphasize the government’s role in responding to the pandemic, and the Republicans emphasize the roles of individuals and support for small businesses. Finally, we present a semantic role analysis that uncovers the important characters and relationships in their narratives as well as how they facilitate a membership categorization process. Our findings concretely expose the gaps in the “elusive consensus” between the two parties. Our methodologies may be applied to computationally study narratives in various domains.

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“…Highly efficient implementations of t-SNE 30 and UMAP 31 have been made available recently (late 2018), and the latter has seen widespread used in many application domains. [32][33][34][35][36][37] Within the context of these tools, it is interesting to consider their efficacy for: 1) identifying collective variables for small molecule reactions in the condensed phase (where solvent participates in the reaction coordinates), and subsequently 2) their ability to elucidate the importance of solvent degrees of freedom within the reduced energy landscape framework.…”

Section: Introductionmentioning

confidence: 99%

Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identi�cation of Small Molecule Solution-Phase Reactions

Kumar

May

et al. 2021

Preprint

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Identifying collective variables for chemical reactions is essential to reduce the 3$N$ dimensional energy landscape into lower dimensional basins and barriers of interest. However in condensed phase processes, the non-meaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis, although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., UMAP, that have become more popular recently. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in solution, and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. The first reaction consists of the dynamic exchange of a cation about a polyhydroxy anion that is facilitated by waters of solvation, while the second reaction consists of a nucleophilic attack of water upon ethylene to initiate cis/trans isomerization. When applied to raw data, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables in solution.

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Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Cited by 14 publications

References 65 publications

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Understand SLE heterogeneity in the era of omics, big data, and artificial intelligence

Characterizing partisan political narrative frameworks about COVID-19 on Twitter

Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identi�cation of Small Molecule Solution-Phase Reactions

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