scholarly journals Conditional resampling improves calibration and sensitivity in single-cell CRISPR screen analysis

2020 ◽  
Author(s):  
Eugene Katsevich ◽  
Timothy Barry ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Functional Data ◽  
Large Scale ◽  
Model Misspecification ◽  
Screen Analysis ◽  
Confounding Effect ◽  
Crispr Screen ◽  
Expression Model ◽  
Technical Factors ◽  
Expression Measurement

Single-cell CRISPR screens are an emerging biotechnology promising unprecedented insights into gene regulation. However, the analysis of these screens presents significant statistical challenges. For example, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two recent large-scale single-cell CRISPR screens how these challenges cause calibration issues among existing analysis methods. To address these challenges, we propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling. This methodology, designed to avoid calibration issues due to technical confounders and expression model misspecification, infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCETPRE demonstrates excellent calibration and sensitivity on the CRISPR screen data and yields 200 new regulatory relationships, many of which are supported by existing functional data.

scholarly journals SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Timothy Barry ◽  
Xuran Wang ◽  
John A. Morris ◽  
Kathryn Roeder ◽  
Eugene Katsevich
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Regulatory Elements ◽  
Screen Analysis ◽  
Genome Wide ◽  
Crispr Screen ◽  
Wide Scale ◽  
Technical Factors ◽  
Expression Measurement ◽  
Good Calibration

AbstractSingle-cell CRISPR screens are a promising biotechnology for mapping regulatory elements to target genes at genome-wide scale. However, technical factors like sequencing depth impact not only expression measurement but also perturbation detection, creating a confounding effect. We demonstrate on two single-cell CRISPR screens how these challenges cause calibration issues. We propose SCEPTRE: analysis of single-cell perturbation screens via conditional resampling, which infers associations between perturbations and expression by resampling the former according to a working model for perturbation detection probability in each cell. SCEPTRE demonstrates very good calibration and sensitivity on CRISPR screen data, yielding hundreds of new regulatory relationships supported by orthogonal biological evidence.

scholarly journals Single-cell normalization and association testing unifying CRISPR screen and gene co-expression analyses with Normalisr

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lingfei Wang
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Large Scale ◽  
Linear Models ◽  
P Value ◽  
Statistical Association ◽  
Experimental Conditions ◽  
Library Size ◽  
Association Testing ◽  
Crispr Screen

AbstractSingle-cell RNA sequencing (scRNA-seq) provides unprecedented technical and statistical potential to study gene regulation but is subject to technical variations and sparsity. Furthermore, statistical association testing remains difficult for scRNA-seq. Here we present Normalisr, a normalization and statistical association testing framework that unifies single-cell differential expression, co-expression, and CRISPR screen analyses with linear models. By systematically detecting and removing nonlinear confounders arising from library size at mean and variance levels, Normalisr achieves high sensitivity, specificity, speed, and generalizability across multiple scRNA-seq protocols and experimental conditions with unbiased p-value estimation. The superior scalability allows us to reconstruct robust gene regulatory networks from trans-effects of guide RNAs in large-scale single cell CRISPRi screens. On conventional scRNA-seq, Normalisr recovers gene-level co-expression networks that recapitulated known gene functions.

scholarly journals FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanping Long ◽  
Zhijian Liu ◽  
Jinbu Jia ◽  
Weipeng Mo ◽  
Liang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Walls ◽  
Large Scale ◽  
Full Length ◽  
Cell Level ◽  
Root Cells ◽  
Rna Profiling ◽  
Different Types ◽  
Long Read ◽  
Single Nucleus

AbstractThe broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

scholarly journals Iterations as the result of social and technical factors: empirical evidence from a large-scale design project

2018 ◽  
Vol 30 (2) ◽  
pp. 251-270 ◽  
Author(s):  
Sebastiano A. Piccolo ◽  
Anja M. Maier ◽  
Sune Lehmann ◽  
Chris A. McMahon
Keyword(s):  
Empirical Evidence ◽  
Large Scale ◽  
Design Project ◽  
Technical Factors ◽  
Scale Design

scholarly journals Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
High Accuracy ◽  
Molecular Heterogeneity ◽  
Precision Oncology ◽  
Scale Analysis ◽  
Rna Seq ◽  
Large Scale Analysis

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.

scholarly journals Leveraging high-powered RNA-Seq datasets to improve inference of regulatory activity in single-cell RNA-Seq data

2019 ◽  
Author(s):  
Ning Wang ◽  
Andrew E. Teschendorff
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Large Scale ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Dropout Rate ◽  
Rna Seq ◽  
Regulatory Activity

AbstractInferring the activity of transcription factors in single cells is a key task to improve our understanding of development and complex genetic diseases. This task is, however, challenging due to the relatively large dropout rate and noisy nature of single-cell RNA-Seq data. Here we present a novel statistical inference framework called SCIRA (Single Cell Inference of Regulatory Activity), which leverages the power of large-scale bulk RNA-Seq datasets to infer high-quality tissue-specific regulatory networks, from which regulatory activity estimates in single cells can be subsequently obtained. We show that SCIRA can correctly infer regulatory activity of transcription factors affected by high technical dropouts. In particular, SCIRA can improve sensitivity by as much as 70% compared to differential expression analysis and current state-of-the-art methods. Importantly, SCIRA can reveal novel regulators of cell-fate in tissue-development, even for cell-types that only make up 5% of the tissue, and can identify key novel tumor suppressor genes in cancer at single cell resolution. In summary, SCIRA will be an invaluable tool for single-cell studies aiming to accurately map activity patterns of key transcription factors during development, and how these are altered in disease.

scholarly journals Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

2019 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Ping Chen ◽  
Daniel Ramsköld ◽  
Gert-Jan Hendriks ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Cell Types ◽  
Mouse Strains ◽  
Rna Molecules ◽  
Counting Strategy ◽  
Long Read ◽  
Sequencing Strategy ◽  
Transcriptome Coverage ◽  
Scale Characterization

AbstractLarge-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5’ unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

scholarly journals EpiScanpy: integrated single-cell epigenomic analysis

2019 ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
Maria Colomé-Tatché
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Feature Space ◽  
Rna Seq ◽  
Computational Framework ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Many ◽  
Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

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