scholarly journals Brain Antibody Sequence Evaluation (BASE): an easy-to-use software for complete data analysis in single cell immunoglobulin cloning

2019 ◽  
Author(s):  
S. Momsen Reincke ◽  
Harald Prüss ◽  
Jakob Kreye
Keyword(s):  
Monoclonal Antibody ◽  
Data Analysis ◽  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Complete Data ◽  
Plasmid Sequence ◽  
Sequence Identity ◽  
Brain Antibody ◽  
Complete Data Analysis

AbstractBackgroundRepertoire analysis of patient-derived recombinant monoclonal antibodies is an important tool to study the role of B cells in autoimmune diseases of the human brain and beyond. Current protocols for generation of patient-derived recombinant monoclonal antibody libraries are time-consuming and contain repetitive steps, some of which can be assisted with the help of software automation.ResultsWe developed BASE, an easy-to-use software for complete data analysis in single cell immunoglobulin cloning. BASE consists of two modules: aBASE for immunological annotations and cloning primer lookup, and cBASE for plasmid sequence identity confirmation before expression. Comparing automated BASE analysis with manual analysis we confirmed the validity of BASE output: identity between manual and automated aBASE analysis was 100% for all outputs, except for immunoglobulin isotype determination. In this case, aBASE yielded correct results in 96% of cases, whereas 4% of cases required manual confirmation. cBASE automatically concluded expression recommendations in 89.8% of cases, 91.8% of which were identical to manually derived results and none of them were false-positive.ConclusionsBASE offers an easy-to-use software solution suitable for complete Ig sequence data analysis and tracking during recombinant mcAB cloning from single cells. Plasmid sequence identity confirmation by cBASE offers functionality not provided by existing software solutions in the field and will help to reduce time-consuming steps of the monoclonal antibody generation workflow.

scholarly journals Brain antibody sequence evaluation (BASE): an easy-to-use software for complete data analysis in single cell immunoglobulin cloning

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
S. Momsen Reincke ◽  
Harald Prüss ◽  
Jakob Kreye
Keyword(s):  
Monoclonal Antibody ◽  
Data Analysis ◽  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Complete Data ◽  
Plasmid Sequence ◽  
Sequence Identity ◽  
Brain Antibody ◽  
Complete Data Analysis

Abstract Background Repertoire analysis of patient-derived recombinant monoclonal antibodies is an important tool to study the role of B cells in autoimmune diseases of the human brain and beyond. Current protocols for generation of patient-derived recombinant monoclonal antibody libraries are time-consuming and contain repetitive steps, some of which can be assisted with the help of software automation. Results We developed BASE, an easy-to-use software for complete data analysis in single cell immunoglobulin cloning. BASE consists of two modules: aBASE for immunological annotations and cloning primer lookup, and cBASE for plasmid sequence identity confirmation before expression. Comparing automated BASE analysis with manual analysis we confirmed the validity of BASE output: identity between manual and automated aBASE analysis was 100% for all outputs, except for immunoglobulin isotype determination. In this case, aBASE yielded correct results in 96% of cases, whereas 4% of cases required manual confirmation. cBASE automatically concluded expression recommendations in 89.8% of cases, 91.8% of which were identical to manually derived results and none of them were false-positive. Conclusions BASE offers an easy-to-use software solution suitable for complete Ig sequence data analysis and tracking during recombinant mAb cloning from single cells. Plasmid sequence identity confirmation by cBASE offers functionality not provided by existing software solutions in the field and will help to reduce time-consuming steps of the monoclonal antibody generation workflow. BASE can be installed locally or accessed online at Code Ocean.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals scTPA: A web tool for single-cell transcriptome analysis of pathway activation signatures

2020 ◽  
Author(s):  
Yan Zhang ◽  
Yaru Zhang ◽  
Jun Hu ◽  
Ji Zhang ◽  
Fangjie Guo ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Biological Pathways ◽  
Rna Seq ◽  
Web Tool ◽  
Functional Interpretation ◽  
Cell Functions ◽  
Pathway Activation

ABSTRACTThe most fundamental challenge in current single-cell RNA-seq data analysis is functional interpretation and annotation of cell clusters. The biological pathways in distinct cell types have different activation patterns, which facilitates understanding cell functions in single-cell transcriptomics. However, no effective web tool has been implemented for single-cell transcriptomic data analysis based on prior biological pathway knowledge. Here, we introduce scTPA (http://sctpa.bio-data.cn/sctpa), which is a web-based platform providing pathway-based analysis of single-cell RNA-seq data in human and mouse. scTPA incorporates four widely-used gene set enrichment methods to estimate the pathway activation scores of single cells based on a collection of available biological pathways with different functional and taxonomic classifications. The clustering analysis and cell-type-specific activation pathway identification were provided for the functional interpretation of cell types from pathway-oriented perspective. An intuitive interface allows users to conveniently visualize and download single-cell pathway signatures. Together, scTPA is a comprehensive tool to identify pathway activation signatures for dissecting single cell heterogeneity.

scholarly journals Deep soft K-means clustering with self-training for single-cell RNA sequence data

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Liang Chen ◽  
Weinan Wang ◽  
Yuyao Zhai ◽  
Minghua Deng
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Sequence Data ◽  
Dimensional Space ◽  
Expression Profiles ◽  
Single Cells ◽  
Clustering Algorithms ◽  
Training Procedure ◽  
Latent Space

Abstract Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called ‘scziDesk’, alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.

scholarly journals SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

2019 ◽  
Author(s):  
Yiliang Zhang ◽  
Kexuan Liang ◽  
Molei Liu ◽  
Yue Li ◽  
Hao Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Expression Patterns ◽  
Dropout Rate ◽  
Biological Variation ◽  
Specific Gene ◽  
Batch Effects ◽  
Cell Recovery

AbstractSingle-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells. Our software is freely available online at https://github.com/YiliangTracyZhang/SCRIBE.

scholarly journals Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

2021 ◽  
Author(s):  
Wenkai Han ◽  
Yuqi Cheng ◽  
Jiayang Chen ◽  
Huawen Zhong ◽  
Zhihang Hu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Biological Diversity ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Data Representation ◽  
Superior Performance ◽  
Peripheral Blood Mononuclear ◽  
Technical Noise ◽  
Downstream Analysis

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool to reveal the complex biological diversity and heterogeneity among cell populations. However, the technical noise and bias of the technology still have negative impacts on the downstream analysis. Here, we present a self-supervised Contrastive LEArning framework for scRNA-seq (CLEAR) profile representation and the downstream analysis. CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events. In the task, the deep learning model learns to pull together the representations of similar cells while pushing apart distinct cells, without manual labeling. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43,695 single cells from peripheral blood mononuclear cells. Further experiments to process a million-scale single-cell dataset demonstrate the scalability of CLEAR. This scalable method generates effective scRNA-seq data representation while eliminating technical noise, and it will serve as a general computational framework for single-cell data analysis.

scholarly journals Comparative proteome signatures of trace samples by multiplexed Data-Independent Acquisition

2021 ◽  
Author(s):  
Claudia Ctortecka ◽  
Gabriela Krššáková ◽  
Karel Stejskal ◽  
Josef M. Penninger ◽  
Sasha Mendjan ◽  
...  
Keyword(s):  
Data Analysis ◽  
Protein Expression ◽  
Single Cell ◽  
Missing Values ◽  
Expression Profiles ◽  
Single Cells ◽  
Proteomics Data ◽  
Biological Origin ◽  
Low Input ◽  
Data Independent Acquisition

AbstractSingle cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single cell resolution. Despite continuous technological improvements in sensitivity, mass spectrometry-based single cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultra-low input samples. While conventional, data-dependent shotgun proteomics (DDA) of ultra-low input samples critically suffers from the accumulation of missing values with increasing sample-cohort size, data-independent acquisition (DIA) strategies do usually not take full advantage of isotope-encoded sample multiplexing. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin, and to identify cell types and single protein knockouts. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. These data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultra-low input samples, such as single cells.

scholarly journals Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application

2015 ◽  
Vol 38 (2) ◽  
pp. 485-502 ◽  
Author(s):  
Betül Kan-Kilinç ◽  
Ozlem Alpu
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
R Package ◽  
Complete Data ◽  
Regression Coefficients ◽  
Estimation Methods ◽  
Robust Estimators ◽  
Least Trimmed Squares ◽  
Biased Estimators ◽  
Complete Data Analysis

<p>In the case of multicollinearity and outliers in regression analysis, the researchers are encouraged to deal with two problems simultaneously. Biased methods based on robust estimators are useful for estimating the regression coefficients for such cases. In this study we examine some robust biased estimators on the datasets with outliers in x direction and outliers in both x and y direction from literature by means of the R package ltsbase. Instead of a complete data analysis, robust biased estimators are evaluated using capabilities and features of this package.</p>

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