scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Lin, Yingxin; Ghazanfar, Shila; Wang, Kevin Y.X.; Gagnon-Bartsch, Johann A.; Lo, Kitty; Su, Xianbin; Han, Ze‐Guang; Ormerod, John T.; Speed, Terence P.; Yang, Pengyi

doi:10.1073/pnas.1820006116

Cited by 154 publications

(173 citation statements)

References 61 publications

Supporting

Mentioning

170

Contrasting

Order By: Relevance

“…We used scMerge [21] to remove the differences between two batches in the PBMC data collection [9]. PBMC dataset generated from two samples using five different protocols and then construct the learning curve.…”

Section: Learning Curve Construction With Data Integrationmentioning

confidence: 99%

“…We used scMerge [21] to remove batch effects between two samples in the PBMC data collection generated from five different protocols [9] and next constructed the learning curves. We found that in most of the cases (SMART-seq, CEL-seq, and inDrops), the learning curve based on batch-corrected data achieved a higher accuracy rate that the uncorrected data (Supplementary Fig.…”

Section: Learning Curve Construction With Data Integrationmentioning

confidence: 99%

See 1 more Smart Citation

scClassify: hierarchical classification of cells

Lin

Cao

Kim

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

17Cell type identification is a key computational challenge in single-cell RNA-sequencing 18 (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, 19 we present scClassify, a hierarchical classification framework based on ensemble learning. 20 scClassify can identify cells from published scRNA-seq datasets more accurately and more 21 finely than in the original publications. We also estimate the cell number needed for accurate 22 classification anywhere in a cell type hierarchy. 23 24Single cell RNA-sequencing (scRNA-seq) datasets have become larger and more diverse, driving the 25 need for classification methods that can provide more refined discrimination between cell types. 26Many major cell types can be divided into subtypes in a hierarchical fashion, forming what we call a 27 'cell type hierarchy' 1,2 . Approaches to scRNA-seq studies that account for cell type hierarchies in 28 experimental design and cell type identification will permit more nuanced interpretations of the 29 resulting data. 3 30 31The most common approach to identifying cell types within scRNA-seq data is unsupervised 32 clustering 4-6 followed by manual annotation using known marker genes. However, the number of 33

show abstract

Section: Learning Curve Construction With Data Integrationmentioning

confidence: 99%

Section: Learning Curve Construction With Data Integrationmentioning

confidence: 99%

scClassify: hierarchical classification of cells

Lin

Cao

Kim

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…CFD is also able to identify cases in which none of the input fits the pattern of consistently high values. Previous methods such as scMerge [14] rank features by some conservation metric and pick the top n% as the conserved features, for some user-specified n. When no input features are truly consistently high, an approach like this will simply result in a list of false positives. In contrast, we find that CFD returns no statistically significant features when all input features are either high mean and high variance, or low mean and low variance, across 14 different parameter settings (Figure 2b)).…”

Section: Simulation Datamentioning

confidence: 99%

“…Identification of features in single-cell data that are stable across cells has recently become an important problem as single-cell data becomes more available and prevalent in genomic and epigenomic analyses. There have been a few methods developed specifically for the discovery of so-called stably expressed genes (scSEGs) in single-cell RNA sequencing (scRNAseq) [10,14]. Recently, a method called scMerge [14] used a Gamma-Gaussian mixture model to compute certain characteristics related to stability that are then used to rank genes by an "SEG index," which is the average rank across these stability properties.…”

Section: Introductionmentioning

confidence: 99%

“…There have been a few methods developed specifically for the discovery of so-called stably expressed genes (scSEGs) in single-cell RNA sequencing (scRNAseq) [10,14]. Recently, a method called scMerge [14] used a Gamma-Gaussian mixture model to compute certain characteristics related to stability that are then used to rank genes by an "SEG index," which is the average rank across these stability properties. The assumptions made by this model, including the Gamma and Gaussian priors, are specific to the analysis of scRNAseq and would likely not generalize to other domains.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A statistical nonparametric method for identifying consistently important features across samples

Sauerwald

Kingsford

2019

Preprint

View full text Add to dashboard Cite

In many applications, features with consistently high measurements across many samples are particularly meaningful and useful for quality control or biological interpretation. Identification of these features among many others can be challenging especially when the samples cannot be expected to have the same distribution or range of values. We present a general method called conserved feature discovery (CFD) for identifying features with consistently strong signals across multiple conditions or samples. Given real-valued data, CFD requires no parameters, makes no assumptions on the underlying sample distributions, and is robust to differences across these distributions. We show that with high probability CFD identifies all true positives and no false positives under certain assumptions on the median and variance distributions of the feature measurements. Using simulated data, we show that CFD is tolerant to a small percentage of poor quality samples and robust to false positives. Applying CFD to RNA sequencing data from the Human Body Map project and GTEx, we identify lists of housekeeping genes as highly expressed genes across tissue types and compare to previous results in this domain. CFD is consistent between the two data sets, and identifies lists of genes enriched for basic cellular processes as expected. The framework can be easily adapted for many data types and desired feature properties.

show abstract

Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre

et al. 2021

View full text Add to dashboard Cite

As the size and complexity of high‐dimensional (HD) cytometry data continue to expand, comprehensive, scalable, and methodical computational analysis approaches are essential. Yet, contemporary clustering and dimensionality reduction tools alone are insufficient to analyze or reproduce analyses across large numbers of samples, batches, or experiments. Moreover, approaches that allow for the integration of data across batches or experiments are not well incorporated into computational toolkits to allow for streamlined workflows. Here we present Spectre, an R package that enables comprehensive end‐to‐end integration and analysis of HD cytometry data from different batches or experiments. Spectre streamlines the analytical stages of raw data pre‐processing, batch alignment, data integration, clustering, dimensionality reduction, visualization, and population labelling, as well as quantitative and statistical analysis. Critically, the fundamental data structures used within Spectre, along with the implementation of machine learning classifiers, allow for the scalable analysis of very large HD datasets, generated by flow cytometry, mass cytometry, or spectral cytometry. Using open and flexible data structures, Spectre can also be used to analyze data generated by single‐cell RNA sequencing or HD imaging technologies, such as Imaging Mass Cytometry. The simple, clear, and modular design of analysis workflows allow these tools to be used by bioinformaticians and laboratory scientists alike. Spectre is available as an R package or Docker container. R code is available on Github (https://github.com/immunedynamics/spectre).

show abstract

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Cited by 154 publications

References 61 publications

scClassify: hierarchical classification of cells

scClassify: hierarchical classification of cells

A statistical nonparametric method for identifying consistently important features across samples

Integration, exploration, and analysis of high‐dimensional single‐cell cytometry data using Spectre

Contact Info

Product

Resources

About