GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

Jiang, Lan; Chen, Huidong; Pinello, Luca; Yuan, Guo‐Cheng

doi:10.1186/s13059-016-1010-4

Cited by 262 publications

(254 citation statements)

References 26 publications

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“…Improvements in rare cell detection can be achieved by having less noisy data and by developing better down-sampling algorithms that can distinguish technical noise from rare cells. These approaches may leverage current strategies for rare cell detection, such as raceID (Grün et al, 2015) and GiniClust (Jiang et al, 2016). …”

Section: Discussionmentioning

confidence: 99%

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

et al. 2018

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Summary Modern single-cell technologies allow multiplexed sampling of cellular states within a tissue. However, computational tools that can infer developmental cell-state transitions reproducibly from such single-cell data are lacking. Here, we introduce p-Creode, an unsupervised algorithm that produces multi-branching graphs from single-cell data, compares graphs with differing topologies, and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. We have applied p-Creode to mass cytometry, multiplex immunofluorescence, and single-cell RNA-seq data. As a test case, we validate cell state-transition trajectories predicted by p-Creode for intestinal tuft cells, a rare, chemosensory cell type. We clarify that tuft cells are specified outside of the Atoh1-dependent secretory lineage in the small intestine. However, p-Creode also predicts, and we confirm, that tuft cells arise from an alternative, Atoh1-driven developmental program in the colon. These studies introduce p-Creode as a reliable method for analyzing large datasets that depict branching transition trajectories. p-Creode is publicly available for download here: https://github.com/KenLauLab/pCreode.

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Section: Discussionmentioning

confidence: 99%

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

et al. 2018

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“…Resistance to therapy may lie with these small clonal lineages. Deep-sequencing and single cell sequencing methods carry the potential to characterize these small clones (48–50). For example, duplex sequencing enables the detection of a single mutated sequence among tens of millions of wild-type sequences (49).…”

Section: Distinction Between Intra-tumor Genetic Heterogeneity and Gementioning

confidence: 99%

Genomic Instability in Cancer: Teetering on the Limit of Tolerance

2017

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Cancer genomic instability contributes to the phenomenon of intratumoral genetic heterogeneity, provides the genetic diversity required for natural selection and enables the extensive phenotypic diversity that is frequently observed among patients. Genomic instability has previously been associated with poor prognosis. However, we have evidence that for solid tumors of epithelial origin, extreme levels of genomic instability, where more than 75% of the genome is subject to somatic copy number alterations, are associated with a potentially better prognosis compared to intermediate levels under this threshold. This has been observed in clonal subpopulations of larger size, especially when genomic instability is shared among a limited number of clones. We hypothesize that cancers with extreme levels of genomic instability may be teetering on the brink of a threshold where so much of their genome is adversely altered that cells rarely replicate successfully. Another possibility is that tumors with high levels of genomic instability are more immunogenic than other cancers with a less extensive burden of genetic aberrations. Regardless of the exact mechanism, but hinging on our ability to quantify how a tumor’s burden of genetic aberrations is distributed among coexisting clones – genomic instability has important therapeutic implications. Herein, we explore the possibility that a high genomic instability could be the basis for a tumor’s sensitivity to DNA damaging therapies. We primarily focus on studies of epithelial-derived solid tumors.

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“…Although this approach has several parameters that require tweaking, it has been used successfully for identifying rare Paneth progenitor cells in intestinal organoids (Grün et al, 2015). Other similar approaches have also been described, including GiniClust (Jiang et al, 2016), and predictions generated with these methods can be tested by searching for genes encoding cell-surface markers that distinguish the new cell clusters, prospective isolation by fluorescence-activated cell sorting (FACS) and subsequent functional assessment.…”

Section: The Basics Of Scrna-seq Analysismentioning

confidence: 99%

Understanding development and stem cells using single cell-based analyses of gene expression

2017

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In recent years, genome-wide profiling approaches have begun to uncover the molecular programs that drive developmental processes. In particular, technical advances that enable genome-wide profiling of thousands of individual cells have provided the tantalizing prospect of cataloging cell type diversity and developmental dynamics in a quantitative and comprehensive manner. Here, we review how singlecell RNA sequencing has provided key insights into mammalian developmental and stem cell biology, emphasizing the analytical approaches that are specific to studying gene expression in single cells.

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GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

Cited by 262 publications

References 26 publications

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut

Genomic Instability in Cancer: Teetering on the Limit of Tolerance

Understanding development and stem cells using single cell-based analyses of gene expression

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