Article

Single-cell RNA-Seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-Seq by genetic and contextual information

2024-04-06

Abstract excerpt

Single-cell RNA sequencing (scRNA-Seq) data from complex human tissues have prevalent blood cell contamination during the sample preparation process. They may also comprise cells of different genetic makeups. These issues demand rigorous preprocessing and filtering prior to the downstream functional analysis, to avoid biased conclusions due to cell types not of interest. Towards this, we propose a new computationa...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: b1a90d9a-09cc-5424-a03c-1f96e4612170
DOI: 10.1101/2024.04.04.588144

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Single-cell RNA-Seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-Seq by genetic and contextual informationDOI 10.1101/2024.04.04.588144Read

Cell type signatures in cell-free DNA fragmentation profiles reveal disease biologySemantic proximity Read

Discovering cell types underlying rare disease phenotypes using scRNA-seq data from non-diseased tissuesSemantic proximity Read

Cell Types of Origin in the Cell Free Transcriptome in Human Health and DiseaseSemantic proximity Read

Single-cell analysis of human diversity in circulating immune cellsSemantic proximity Read

<i>ctQC</i> improves biological inferences from single cell and spatial transcriptomics dataSemantic proximity Read

Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and diseaseSemantic proximity Read

What Knowledge Can Be Extracted from Single-Cell Data? An Empirical Analysis of Donor-Robust Cell-Type AnnotationSemantic proximity Read

The Human Cell Atlas bone marrow single-cell interactive web portalSemantic proximity Read

HiDDEN: A machine learning label refinement method for detection of disease-relevant populations in case-control single-cell transcriptomicsSemantic proximity Read

Optimized Detection and Inference of Names in scRNA-seq dataSemantic proximity Read

Cell-ID: gene signature extraction and cell identity recognition at individual cell levelSemantic proximity Read

Data Matrix Normalization and Merging Strategies Minimize Batch-specific Systemic Variation in scRNA-Seq DataSemantic proximity Read