Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis

By Jean Fan, N. Salathia, R. Liu, Gwendolyn E. Kaeser, Y. Yung, Joseph L. Herman, F. Kaper, Jian-Bing Fan, Kun Zhang, J. Chun and 1 more. Nature Methods, 2015

364 citations recorded by the source corpus.

The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains challenging. We developed pathway and gene set overdispersion analysis (PAGODA) to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/856b19a6069d3ad71793b61f6e69be1421bf1507

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “The rest of the PAGODA steps were run using default parameters, except: The evaluation of overdispersion of 'de novo' gene sets was performed with a trimming value of 7.1 extreme cells, and 50 as the number of clusters to be determined; threshold in the determination of top aspects a p-value: 0.01; ”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Specifically, hierarchical clustering and/or PCA is often performed on DE [3, 15, 65], highly expressed [66, 67], or highly variable genes [61, 62] (or gene sets [68]) to identify cell sub-populations.”
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022, methodology): “To promote its wide application, either as a stand-alone tool or together with other popular single-cell data analysis software (e.g., Seurat 42 , MAST 43 , PAGODA 44 ), we have deployed ScType both as an interactive web-platform (http://sctype.app), and as an open-source R implementation (https://g”
bigSCale: an analytical framework for big-scale single-cell data (2017, methodology): “Consistentwithprevi-ous analyses (Zeisel et al. 2015; Fan et al. 2016), bigSCale was able to segregate all major brain cell types, namely, somato-sensory pyramidal neurons, different types of CA1/2 pyramidal neurons, inter-neurons, astrocytes, oligodendrocytes, and vascular cells (Fig.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, neutral): “This property makes scVI a good baseline to be used by interactive visualization tools [52, 53, 54].”
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade (2018, neutral): “As input to SCDE we used the normalized and imputed expression matrix E’ (see Imputation and normalization).”
chromVAR: Inferring transcription factor-associated accessibility from single-cell epigenomic data (2017, background): “Methods developed for single cell RNA-seq have shown that measuring the dispersion of gene sets, such as Gene Ontology or co-expression modules, rather than individual genes can be a powerful approach for analyzing sparse data 7 .”
Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma (2017, positive): “A similar analysis using PAGODA (Fan et al., 2016) gave consistent results (Fig.”
Toward Minimal Residual Disease-Directed Therapy in Melanoma. (2018, positive): “The single-cell RNA-seq (scRNA-seq) data, which were processed using a pipeline that was conceptually similar to PAGODA (Fan et al., 2016), Figure 1.”
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions (2019, methodology): “Fitting more flexible intra-MC models, capable of accounting for not only sampling noise but also convergent processes such as cell cycle or stress [47, 48], or embedding the metacells in hierarchical or multi-resolution structures [49, 50] should allow for more efficient extraction of the signals…”