2019
DOI: 10.1101/746339
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A multiresolution framework to characterize single-cell state landscapes

Abstract: Dissecting the cellular heterogeneity embedded in single-cell transcriptomic data is challenging. Although a large number of methods and approaches exist, robustly identifying underlying cell states and their associations is still a major challenge; given the nonexclusive and dynamic influence of multiple unknown sources of variability, the existence of state continuum at the time-scale of observation, and the inevitable snapshot nature of experiments. As a way to address some of these challenges, here we intr… Show more

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Cited by 16 publications
(36 citation statements)
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“…1c ). De novo markers for these cell types are consistent with our previous studies of the human prefrontal cortex 28 ( Supplementary Table 3 ). Neuronal subtypes, particularly excitatory neurons, showed higher numbers of expressed genes and identified UMIs ( Extended Data Fig.…”
Section: Introductionsupporting
confidence: 89%
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“…1c ). De novo markers for these cell types are consistent with our previous studies of the human prefrontal cortex 28 ( Supplementary Table 3 ). Neuronal subtypes, particularly excitatory neurons, showed higher numbers of expressed genes and identified UMIs ( Extended Data Fig.…”
Section: Introductionsupporting
confidence: 89%
“…(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Total count of up-and downregulated genes in each cell-type/state and the overlap of cell-type/state-specific SZ DEGs with DEGs observed in previous studies of bulk cortical tissue 28 . Cell-type-specific differential expression of selected top-ranked genes across all 20 cell-types/states as well as previous studies of bulk cortical tissue.…”
Section: Supplementary Tablesmentioning
confidence: 89%
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“…We next sought to explore heterogeneously expressed programs from the 160 tumours. To identify these latent gene expression programs, we performed multi-resolution archetypal analysis on each tumour sample using the ACTIONet algorithm (Mohammadi et al, 2020), learning cell activity scores for distinct programs in each tumour ( Figure 1B). This identified multiple programs in each sample, including expected sources of variation, such as cell cycle activity.…”
Section: A6 A2mentioning
confidence: 99%
“…Samples with <100 annotated cancer cells were removed from the analysis. Multi-resolution archetypal analysis was performed independently on the cancer cells from all 160 tumours using ACTIONet v2.0.15 (Mohammadi et al, 2020) to decompose cells' gene expression profiles into a small set of latent expression programs that are heterogeneously expressed throughout the population. Reduced kernel matrices were first computed with the reduce.ace() function implemented in the R package ACTIONet with the parameter reduced_dim=25.…”
Section: Identifying Latent Emp Expression Programs With Archetypal Amentioning
confidence: 99%