SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

Wang, Bo; Ramazzotti, Daniele; Sano, Luca De; Zhu, Junjie; Pierson, Emma; Batzoglou, Serafim

doi:10.1101/118901

Cited by 33 publications

(32 citation statements)

References 12 publications

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“…To test SCENIC performances we applied it to a scRNA-seq data set with well-known cell types from the adult mouse brain previously described in Zeisel et al 16 . This data set has been used extensively for benchmarking purposes 13,14,20,[27][28][29][30][31] and contains the main cell types in hippocampus and somatosensory cortex, namely neurons (pyramidal excitatory neurons, and interneurons), glia (astrocytes, oligodendrocytes, microglia), and endothelial cells. In the first step ( Figure 1a), we inferred co-expression modules using an improved implementation of GENIE3 32 , the top-performing method for network inference in the DREAM challenge 33 .…”

Section: Simultaneous Discovery Of Gene Regulatory Network and Cellumentioning

confidence: 99%

SCENIC: Single-cell regulatory network inference and clustering

Aibar

González-Blas

Moerman

et al. 2017

Preprint

886

1,482

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Single-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

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Section: Simultaneous Discovery Of Gene Regulatory Network and Cellumentioning

confidence: 99%

SCENIC: Single-cell regulatory network inference and clustering

Aibar

González-Blas

Moerman

et al. 2017

Preprint

886

1,482

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“…There are several existing algorithms for learning discriminative metrics from single cell datasets, which can be directly fed into the Hopper framework. For example, SIMLR [15] uses machine learning to jointly predict the clustering and the distance measure. Other possibilities abound, from established kernels (e.g.…”

Section: Discussionmentioning

confidence: 99%

Hopper: A Mathematically Optimal Algorithm for Sketching Biological Data

DeMeo

Berger

2019

Preprint

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Single-cell RNA-sequencing (scRNA-seq) has grown massively in scale since its inception, presenting substantial analytic and computational challenges. Even simple downstream analyses, such as dimensionality reduction and clustering, require days of runtime and hundreds of gigabytes of memory for today's largest datasets. In addition, current methods often favor common cell types, and miss salient biological features captured by small cell populations. Here we present Hopper, a single-cell toolkit that both speeds up the analysis of single-cell datasets and highlights their transcriptional diversity by intelligent subsampling, or sketching. Hopper realizes the optimal polynomial-time approximation of the Hausdorff distance between the full and downsampled dataset, ensuring that each cell is wellrepresented by some cell in the sample. Unlike prior sketching methods, Hopper adds points iteratively and allows for additional sampling from regions of interest, enabling fast and targeted multi-resolution analyses. In a dataset of over 1.3 million mouse brain cells, we detect a cluster of just 64 macrophages expressing inflammatory tissues (0.004% of the full dataset) from a Hopper sketch containing just 5,000 cells, and several other small but biologically interesting immune cell populations invisible to analysis of the full data. On an even larger dataset consisting of ∼2 million developing mouse organ cells, we show even representation of important cell types in small sketch sizes, in contrast with prior sketching methods. By condensing transcriptional information encoded in large datasets, Hopper grants the individual user with a laptop the same analytic capabilities as large consortium.

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“…Instead of t-SNE, one could also use other dimension reduction techniques, such as PCA, diffusion maps, SIMLR ( Wang et al , 2017) or isomaps, some of which are conveniently available via the function from the cytofkit package ( Chen et al , 2016). To speed up the t-SNE analysis, one could use a multicore version that is available via the Rtsne.multicore package.…”

Section: Cell Population Identification With Flowsom and Consensusclumentioning

confidence: 99%

“…Alternative clustering algorithms such as the popular PhenoGraph algorithm ( Levine et al , 2015) (e.g. via the Rphenograph package), dimensionality reduction techniques, such as diffusion maps ( Haghverdi et al , 2015) via the destiny package ( Angerer et al , 2016)), and SIMLR ( Wang et al , 2017) via the SIMLR package could be inserted to the workflow.…”

Section: Introductionmentioning

confidence: 99%

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

et al. 2017

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High dimensional mass and flow cytometry (HDCyto) experiments have become a method of choice for high throughput interrogation and characterization of cell populations.Here, we present an R-based pipeline for differential analyses of HDCyto data, largely based on Bioconductor packages. We computationally define cell populations using FlowSOM clustering, and facilitate an optional but reproducible strategy for manual merging of algorithm-generated clusters. Our workflow offers different analysis paths, including association of cell type abundance with a phenotype or changes in signaling markers within specific subpopulations, or differential analyses of aggregated signals. Importantly, the differential analyses we show are based on regression frameworks where the HDCyto data is the response; thus, we are able to model arbitrary experimental designs, such as those with batch effects, paired designs and so on. In particular, we apply generalized linear mixed models to analyses of cell population abundance or cell-population-specific analyses of signaling markers, allowing overdispersion in cell count or aggregated signals across samples to be appropriately modeled. To support the formal statistical analyses, we encourage exploratory data analysis at every step, including quality control (e.g. multi-dimensional scaling plots), reporting of clustering results (dimensionality reduction, heatmaps with dendrograms) and differential analyses (e.g. plots of aggregated signals).

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SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning

Cited by 33 publications

References 12 publications

SCENIC: Single-cell regulatory network inference and clustering

SCENIC: Single-cell regulatory network inference and clustering

Hopper: A Mathematically Optimal Algorithm for Sketching Biological Data

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets

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