SCnorm: robust normalization of single-cell RNA-seq data

Bacher, Rhonda; Chu, Li‐Fang; Leng, Ning; Gasch, Audrey P.; Thomson, James A.; Stewart, Ron; Newton, Michael A.; Kendziorski, Christina

doi:10.1038/nmeth.4263

Cited by 275 publications

(234 citation statements)

References 26 publications

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“…Clustering using this approach was highly consistent with an alternative approach (Figure S1E) (Bacher et al, 2017). Furthermore, additional t-SNE analyses with multiple perplexity parameters (15, 20, 25, 30 and 35) and six instances for each perplexity parameter confirmed the robustness of the clustering patterns (data not shown).…”

Section: Star Methodssupporting

confidence: 77%

“…The relative expression levels, across the remaining subset of cells and genes, were used for downstream analysis. Although normalization approaches can potentially introduce bias into initial clustering, relative expression levels, as defined above and as defined with an alternative normalization method (Bacher et al, 2017) were highly similar. The use of alternative normalization had a limited influence on downstream results such as the distribution of p-EMT scores (data not shown).…”

Section: Star Methodsmentioning

confidence: 99%

“… (E) t-SNE plot of non-malignant cells (as shown in Figure 2A) colored by their assignment to 14 clusters by SC3 (Bacher et al, 2017) with default parameters, demonstrating high consistency between SC3 clusters and tSNE coordinates.…”

Section: Supplementary Materialsmentioning

confidence: 99%

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Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Puram

Tirosh

Parikh

et al. 2017

Cell

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SUMMARY The diverse malignant, stromal, and immune cells in tumors affect growth, metastasis and response to therapy. We profiled transcriptomes of ~6,000 single cells from 18 head and neck squamous cell carcinoma (HNSCC) patients, including five matched pairs of primary tumors and lymph node metastases. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined HNSCC subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

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Section: Star Methodssupporting

confidence: 77%

Section: Star Methodsmentioning

confidence: 99%

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Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Puram

Tirosh

Parikh

et al. 2017

Cell

1,865

147

2,350

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“…BigSCale presents a complete standalone package combining the aforementioned analysis steps. Of note, bigSCale can be used in combination with external normalization tools (Lun et al 2016;Bacher et al 2017), as we have shown for scran normalization applied to our simulated data sets (Supplemental Fig. S6).…”

Section: Discussionmentioning

confidence: 92%

bigSCale: an analytical framework for big-scale single-cell data

et al. 2018

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Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.[Supplemental material is available for this article.]Single-cell RNA sequencing (scRNA-seq) is at the forefront of techniques to chart molecular properties of individual cells. Recent microfluidic-based methods are scalable to tens of thousands of cells, enabling an unbiased sampling and in-depth characterization without prior knowledge Macosko et al. 2015;Zheng et al. 2017). Consequently, studies are less confined by the number of cells and aim to produce comprehensive cellular atlases of entire tissues, organs, and organisms (Regev et al. 2017). Increasing cell numbers, however, generate extremely large data sets, which extend processing time and challenge computing resources. Current scRNA-seq analysis tools are not designed to analyze data sets larger than thousands of cells and often lack sensitivity and specificity to identify marker genes for cell populations or experimental conditions.To address the challenges of large scRNA-seq data sets, we developed bigSCale, an analytical framework for the sensitive detection of population markers and differentially expressed genes, being scalable to analyze mil...

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“…Although it has the advantage of being able to profile the complete transcriptome, the abundance of lowly expressed transcripts like transcription factors remains a challenge and requires pooling of data when the magnitude of change is also small. Recent reports have aimed to improve the analysis algorithms and to extract more information out of the data [73–76]. Since the cells are lysed to retrieve the mRNA, scRNA-seq ideally provides a snapshot of the cell state, inferred by the transcript profile.…”

Section: Integration Of Protein Detection and Transcriptional Profilimentioning

confidence: 99%

Single-cell technologies for profiling T cells to enable monitoring of immunotherapies

Varadarajan

2018

Current Opinion in Chemical Engineering

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Immunotherapy relies on the reinvigoration of immune system to combat diseases and has transformed the landscape of cancer treatments. Clinical trials using immune checkpoint inhibitors (ICI), and adoptive transfer of genetically modified T cells have demonstrated durable remissions in subsets of cancer patients. A comprehensive understanding of the polyfunctionality of T lymphocytes in ICI or adoptive cell transfer (ACT), at single-cell resolution, will quantify T-cell properties that are essential for therapeutic benefit. We briefly highlight several emerging integrated single-cell technologies focusing on the profiling of multiple properties/functionalities of T cells. We envision that these tools have the potential to provide valuable experimental and clinical insights on T-cell biology, and eventually pave the road for the discovery of surrogate T-cell biomarkers for immunotherapy.

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SCnorm: robust normalization of single-cell RNA-seq data

Cited by 275 publications

References 26 publications

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

bigSCale: an analytical framework for big-scale single-cell data

Single-cell technologies for profiling T cells to enable monitoring of immunotherapies

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