Single-cell mRNA quantification and differential analysis with Census

By Xiaojie Qiu, Andrew J. Hill, Jonathan S. Packer, Dejun Lin, Yian Ma, Yian Ma, Cole Trapnell. Nature Methods, 2017

1,670 citations recorded by the source corpus.

Single-cell gene expression studies promise to reveal rare cell types and cryptic states, but the high variability of single-cell RNA-seq measurements frustrates efforts to assay transcriptional differences between cells. We introduce the Census algorithm to convert relative RNA-seq expression levels into relative transcript counts without the need for experimental spike-in controls. Analyzing changes in relative transcript counts led to dramatic improvements in accuracy compared to normalized read counts and enabled new statistical tests for identifying developmentally regulated genes. Census counts can be analyzed with widely used regression techniques to reveal changes in cell-fate-dependent gene expression, splicing patterns and allelic imbalances. We reanalyzed single-cell data from several developmental and disease studies, and demonstrate that Census enabled robust analysis at multiple layers of gene regulation. Census is freely available through our updated single-cell analysis toolkit, Monocle 2.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c16c7956d56b4392eb4f5d2d007294d208b777d0

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Citations in the corpus

Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19 (2020, methodology): “To further investigate the characteristics of severe COVID19, we performed a trajectory analysis with Monocle 2 (23) using two internally well-controlled specimens (one severe and one mild) in which both PBMC samples were collected from a single patient (the subject C7) with COVID-19.”
Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing (2018, background): “Census [15] and SCNorm [16] look for proper scaling factors by explicitly modeling the dependence of gene expression on sequencing depth or spike-in RNA.”
PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2019, neutral): “[5] for a complex differentiation tree.”
Deep Generative Modeling for Single-cell Transcriptomics (2018, background): “Census [13] and SCNorm [14] look for proper scaling factors by explicitly modeling the dependence of gene expression on sequencing depth or spike-in RNA.”
Graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells (2017, methodology): “…the revision of the present paper — presents an idea for measuring “overlap between inﬂuence regions” of clusters obtained from 16 of Reference [6] using the same color coding as in the original publication. b, Rerunning Monocle 2 with the exact same parameters as for panel b, but keeping the…”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “The more recent Monocle 2 [8] uses a different approach, with dimensionality reduction and ordering performed by reverse graph embedding (RGE), allowing it to detect branching events in an unsupervised manner.”
Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017, methodology): “Finally, while the issue of how best to normalize scRNA-seq data has not yet been fully resolved, many efforts are underway to develop additional robust and effective normalization techniques and to systematically assess their performance on individual datasets [44, 45, 46].”
Orchestrating single-cell analysis with Bioconductor (2019, methodology): “the five top performers were the Bioconductor packages slingshot [126], TSCAN [30], monocle [123, 124, 127], and cellTree [128]; the fifth was the SCORPIUS [129] package currently hosted on CRAN.”
Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry (2018, methodology): “81 Developmental trajectories were created using Monocle versions 2 and 3 (32-34), the 82 latter using Uniform Manifold Approximation and Projection for Dimension Reduction 83 (UMAP) (35).”
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019, methodology): “Given its importance, there have been a large number of diverse methods proposed for the normalization of scRNA-seq data [6–11].”
CellMarker 2.0: an updated database of manually curated cell markers in human/mouse and web tools based on scRNA-seq data (2022, methodology): “This functional module is implemented based on the R package Monocle 3 (31).”