Tag: CITE-seq

SCHNAPPs – Single Cell sHiNy APPlication(s)

Abstract : Single-cell RNA-sequencing (scRNAseq) experiments are becoming a standard tool for bench-scientists to explore the cellular diversity present in all tissues. Data produced by scRNAseq is technically complex and requires analytical workflows that are an active field of bioinformatics research, whereas a wealth of biological background knowledge is needed…

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Generation of Centered Log-RatioCentered log-ratio (CLR) Normalized Antibody-Derived TagAntibody-derived tag (ADT) Counts from Large Single-Cell Sequencing Datasets

Recent developments in single-cell analysis has provided the ability to assay >50 surface-level proteins by combining oligo-conjugated antibodies with sequencing technology. These methods, such as CITE-seq and REAP-seq, have added another modality  …more Recent developments in single-cell analysis has provided the ability to assay >50 surface-level proteins by combining oligo-conjugated…

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Precision Oncology – Data Scientist – Principal Scientist, Immuno-Oncology Bioinformatics at Sanofi

  Position Overview: We seek an experienced computational biologist / data scientist to identify and advance new immuno-oncology therapies for solid tumors and hematological malignancies. In this hands-on role, you will work as an integral part of project teams to identify targets, advance programs, and develop and test actionable biomarker strategies for…

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Cell Type Prediction – covering 45 immune cell types

Tool:Cell Type Prediction – covering 45 immune cell types 0 Dear Colleagues, We just released a new tool for predicting cell types from single-cell RNA-seq data (and scRNA-seq + CITE-Seq). Currently, it covers 45 sub-types of immune cells. More subtypes are continuously being added. This tool is available in both…

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WNN (Seurat v4) vs. totalVI (scvi-tools) for CITE-seq data

WNN (Seurat v4) vs. totalVI (scvi-tools) for CITE-seq data 0 I want to build a UMAP from CITE-seq data with a joint embedding of the scRNA-seq and protein ab data. What’s the ‘best’ method in terms of representing the most accurate embedding? In the totalVI paper, they say totalVI >…

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