scholarly journals Quantitative accuracy and precision in multiplexed single-cell proteomics

2021 ◽  
Author(s):  
Claudia Ctortecka ◽  
Karel Stejskal ◽  
Gabriela Krššáková ◽  
Sasha Mendjan ◽  
Karl Mechtler
Keyword(s):  
Data Quality ◽  
Single Cell ◽  
Experimental Designs ◽  
Ion Current ◽  
Impact Measurement ◽  
Measurement Variability ◽  
Biological Phenomena ◽  
Accuracy And Precision ◽  
Quantitative Accuracy ◽  
Proteomics Experiment

AbstractSingle-cell proteomics workflows have considerably improved in sensitivity and reproducibility to characterize yet unknown biological phenomena. With the emergence of multiplexed single-cell proteomics, studies increasingly present single-cell measurements in conjunction with an abundant congruent carrier to improve precursor selection and enhance identifications. While these extreme carrier spikes are often >100-times more abundant than the investigated samples, undoubtedly the total ion current increases, but quantitative accuracy possibly is affected. We here focus on narrowly titrated carrier spikes (i.e., <20x) and assess their elimination for comparable sensitivity at superior accuracy. We find that subtle changes in the carrier ratio can severely impact measurement variability and describe alternative multiplexing strategies to evaluate data quality. Lastly, we demonstrate elevated replicate overlap while preserving acquisition throughput at improved quantitative accuracy with DIA-TMT and discuss optimized experimental designs for multiplexed proteomics of trace samples. This comprehensive benchmarking gives an overview of currently available techniques and guides conceptualizing the optimal single-cell proteomics experiment.

scholarly journals Quantitative Accuracy and Precision in Multiplexed Single-Cell Proteomics

2021 ◽  
Author(s):  
Claudia Ctortecka ◽  
Karel Stejskal ◽  
Gabriela Krššáková ◽  
Sasha Mendjan ◽  
Karl Mechtler
Keyword(s):  
Single Cell ◽  
Accuracy And Precision ◽  
Quantitative Accuracy

Using ion purity scores for enhancing quantitative accuracy and precision in complex proteomics samples

2012 ◽  
Vol 404 (4) ◽  
pp. 1127-1139 ◽  
Author(s):  
Scott J. Geromanos ◽  
Chris Hughes ◽  
Steven Ciavarini ◽  
Johannes P. C. Vissers ◽  
James I. Langridge
Keyword(s):  
Accuracy And Precision ◽  
Quantitative Accuracy

scholarly journals HiCImpute: A Bayesian Hierarchical Model for Identifying Structural Zeros and Enhancing Single Cell Hi-C Data.

2021 ◽  
Author(s):  
Qing Xie ◽  
Chengong Han ◽  
Victor Jin ◽  
Shili Lin
Keyword(s):  
Quality Improvement ◽  
Data Quality ◽  
Single Cell ◽  
Single Cells ◽  
High Sensitivity ◽  
Real Data ◽  
Cell Types ◽  
Sequencing Depth ◽  
2D Data ◽  
Structural Zeros

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicate things further is the fact that not all zeros are created equal, as some are due to loci truly not interacting because of the underlying biological mechanism (structural zeros), whereas others are indeed due to insufficient sequencing depth (sampling zeros), especially for loci that interact infrequently. Differentiating between structural zeros and sampling zeros is important since correct inference would improve downstream analyses such as clustering and discovery of subtypes. Nevertheless, distinguishing between these two types of zeros has received little attention in the single cell Hi-C literature, where the issue of sparsity has been addressed mainly as a data quality improvement problem. To fill this gap, in this paper, we propose HiCImpute, a Bayesian hierarchy model that goes beyond data quality improvement by also identifying observed zeros that are in fact structural zeros. HiCImpute takes spatial dependencies of scHi-C 2D data structure into account while also borrowing information from similar single cells and bulk data, when such are available. Through an extensive set of analyses of synthetic and real data, we demonstrate the ability of HiCImpute for identifying structural zeros with high sensitivity, and for accurate imputation of dropout values in sampling zeros. Downstream analyses using data improved from HiCImpute yielded much more accurate clustering of cell types compared to using observed data or data improved by several comparison methods. Most significantly, HiCImpute-improved data has led to the identification of subtypes within each of the excitatory neuronal cells of L4 and L5 in the prefrontal cortex.

Identifying cell types to interpret scRNA-seq data: how, why and more possibilities

2020 ◽  
Vol 19 (4) ◽  
pp. 286-291 ◽  
Author(s):  
Ziwei Wang ◽  
Hui Ding ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Phenomena ◽  
Single Cell Rna Sequencing ◽  
Numerous Data ◽  
Near Future

Abstract Single-cell RNA sequencing (scRNA-seq) has generated numerous data and renewed our understanding of biological phenomena at the cellular scale. Identification of cell types has been one of the most prevalent means for interpreting scRNA-seq data, based upon which connections are made between the transcriptome and phenotype. Herein, we attempt to review the methods and tools that dedicate to the task regarding their feature and usage and look at the possibilities for scRNA-seq development in the near future.

scholarly journals Realistic precision and accuracy of online experiment platforms, web browsers, and devices

2020 ◽  
Author(s):  
Alexander Anwyl-Irvine ◽  
Edwin S. Dalmaijer ◽  
Nick Hodges ◽  
Jo K. Evershed
Keyword(s):  
Data Quality ◽  
Ease Of Use ◽  
Web Browsers ◽  
Display Time ◽  
Manual Response ◽  
Accuracy And Precision ◽  
Impact Experiment ◽  
Behavioural Research ◽  
Impact Data ◽  
Web Building

Abstract Due to increasing ease of use and ability to quickly collect large samples, online behavioural research is currently booming. With this popularity, it is important that researchers are aware of who online participants are, and what devices and software they use to access experiments. While it is somewhat obvious that these factors can impact data quality, the magnitude of the problem remains unclear. To understand how these characteristics impact experiment presentation and data quality, we performed a battery of automated tests on a number of realistic set-ups. We investigated how different web-building platforms (Gorilla v.20190828, jsPsych v6.0.5, Lab.js v19.1.0, and psychoJS/PsychoPy3 v3.1.5), browsers (Chrome, Edge, Firefox, and Safari), and operating systems (macOS and Windows 10) impact display time across 30 different frame durations for each software combination. We then employed a robot actuator in realistic set-ups to measure response recording across the aforementioned platforms, and between different keyboard types (desktop and integrated laptop). Finally, we analysed data from over 200,000 participants on their demographics, technology, and software to provide context to our findings. We found that modern web platforms provide reasonable accuracy and precision for display duration and manual response time, and that no single platform stands out as the best in all features and conditions. In addition, our online participant analysis shows what equipment they are likely to use.

A Methodology to Assure Measurement Data Quality for Key Engineering Materials

2010 ◽  
Vol 108-111 ◽  
pp. 972-978
Author(s):  
Ying Su ◽  
Jing Hua Huang ◽  
Latif Al-Hakim
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Measurement Data ◽  
Limited Attention ◽  
Decision Making Process ◽  
Precision Determination ◽  
Accuracy And Precision ◽  
Process Documentation ◽  
Engineering Materials

Purpose – Only limited attention has been paid to the issue of Measurement Data Quality (MDQ) in a metrology context. To address this critique of the literature a methodology to assure MDQ was proposed. Methodology – The study proposes a methodology which consists of four steps can be used to 1 identify the importance of a measurement (identification), 2 determine accuracy and precision (determination), 3 evaluate the criticality of the measurement to its impact on the final result (evaluation) and 4 record the facts that influenced the decision making process (documentation). Findings –When followed and properly documented, these four steps can help ensure our measurements are valid and worthwhile. Identifying the important measurements that are made, determining the level of accuracy required and then using the proper tools to make the measurements will yield valid, useful results.

scholarly journals ICan: An Optimized Ion-Current-Based Quantification Procedure with Enhanced Quantitative Accuracy and Sensitivity in Biomarker Discovery

2014 ◽  
Vol 13 (12) ◽  
pp. 5888-5897 ◽  
Author(s):  
Chengjian Tu ◽  
Quanhu Sheng ◽  
Jun Li ◽  
Xiaomeng Shen ◽  
Ming Zhang ◽  
...  
Keyword(s):  
Biomarker Discovery ◽  
Ion Current ◽  
Quantitative Accuracy

scholarly journals projectR: An R/Bioconductor package for transfer learning via PCA, NMF, correlation, and clustering

2019 ◽  
Author(s):  
Gaurav Sharma ◽  
Carlo Colantuoni ◽  
Loyal A Goff ◽  
Elana J Fertig ◽  
Genevieve Stein-O’Brien
Keyword(s):  
Single Cell ◽  
Transfer Learning ◽  
Single Cell Analysis ◽  
Ground Truth ◽  
Biological Data ◽  
Bioconductor Package ◽  
Reduction Techniques ◽  
Biological Phenomena ◽  
Feature Discovery ◽  
Dimension Reduction Techniques

AbstractMotivationDimension reduction techniques are widely used to interpret high-dimensional biological data. Features learned from these methods are used to discover both technical artifacts and novel biological phenomena. Such feature discovery is critically import to large single-cell datasets, where lack of a ground truth limits validation and interpretation. Transfer learning (TL) can be used to relate the features learned from one source dataset to a new target dataset to perform biologically-driven validation by evaluating their use in or association with additional sample annotations in that independent target dataset.ResultsWe developed an R/Bioconductor package, projectR, to perform TL for analyses of genomics data via TL of clustering, correlation, and factorization methods. We then demonstrate the utility TL for integrated data analysis with an example for spatial single-cell analysis.AvailabilityprojectR is available on Bioconductor and at https://github.com/genesofeve/[email protected]; [email protected]

scholarly journals Learning dynamics by computational integration of single cell genomic and lineage information

2021 ◽  
Author(s):  
Shou-Wen Wang ◽  
Allon Klein
Keyword(s):  
Single Cell ◽  
Disease Onset ◽  
Lineage Tracing ◽  
Experimental Designs ◽  
Cell Dynamics ◽  
Genome Wide ◽  
Cell Assays ◽  
Bona Fide ◽  
Cell Genome ◽  
Dynamic Relationships

Abstract A goal of single cell genome-wide profiling is to reconstruct dynamic transitions during cell differentiation, disease onset, and drug response. Single cell assays have recently been integrated with lineage tracing, a set of methods that identify cells of common ancestry to establish bona fide dynamic relationships between cell states. These integrated methods have revealed unappreciated cell dynamics, but their analysis faces recurrent challenges arising from noisy, dispersed lineage data. Here, we develop coherent, sparse optimization (CoSpar) as a robust computational approach to infer cell dynamics from single-cell genomics integrated with lineage tracing. CoSpar is robust to severe down-sampling and dispersion of lineage data, which enables simpler, lower-cost experimental designs and requires less calibration. In datasets representing hematopoiesis, reprogramming, and directed differentiation, CoSpar identifies fate biases not previously detected, predicting transcription factors and receptors implicated in fate choice. Documentation and detailed examples for common experimental designs are available at https://cospar.readthedocs.io/.

scholarly journals Vireo: Bayesian demultiplexing of pooled single-cell RNA-seq data without genotype reference

2019 ◽  
Author(s):  
Yuanhua Huang ◽  
Davis J McCarthy ◽  
Oliver Stegle
Keyword(s):  
Single Cell ◽  
Real Data ◽  
Experimental Designs ◽  
Joint Analysis ◽  
Rna Seq ◽  
Computationally Efficient ◽  
Primary Object ◽  
Genotype Information ◽  
Object Of Study ◽  
Multiple Samples

AbstractThe joint analysis of multiple samples using single-cell RNA-seq is a promising experimental design, offering both increased throughput while allowing to account for batch variation. To achieve multi-sample designs, genetic variants that segregate between the samples in the pool have been proposed as natural barcodes for cell demultiplexing. Existing demultiplexing strategies rely on access to complete genotype data from the pooled samples, which greatly limits the applicability of such methods, in particular when genetic variation is not the primary object of study. To address this, we here present Vireo, a computationally efficient Bayesian model to demultiplex single-cell data from pooled experimental designs. Uniquely, our model can be applied in settings when only partial or no genotype information is available. Using simulations based on synthetic mixtures and results on real data, we demonstrate the robustness of our model and illustrate the utility of multi-sample experimental designs for common expression analyses.

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