Droplet barcoding for single cell transcriptomics applied to embryonic stem cells
Recently, RNA sequencing has achieved single cell resolution, but what is limiting is an effective way to routinely isolate and process large numbers of individual cells for in-depth sequencing, and to do so quantitatively. We have developed a droplet-microfluidic approach for parallel barcoding thousands of individual cells for subsequent RNA profiling by next-generation sequencing. This high-throughput method shows a surprisingly low noise profile and is readily adaptable to other sequencing-b...
Provenance — who produced it, who reused it
Linked to 35 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.
- Pooling across cells to normalize single-cell RNA sequencing dat... 2016 · 1,291 cites
- scGNN is a novel graph neural network framework for single-cell... 2021 · 457 cites
- Deep learning for inferring gene relationships from single-cell... 2019 · 263 cites
- dropEst: pipeline for accurate estimation of molecular counts in... 2018 · 212 cites
- scIGANs: single-cell RNA-seq imputation using generative adversa... 2020 · 198 cites
- Linnorm: improved statistical analysis for single cell RNA-seq e... 2017 · 134 cites
- Imputing single-cell RNA-seq data by combining graph convolution... 2021 · 110 cites
- Exploiting single-cell expression to characterize co-expression... 2016 · 94 cites
- GiniClust2: a cluster-aware, weighted ensemble clustering method... 2018 · 89 cites
- Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA... 2019 · 86 cites
24 further papers cite this accession but reuse could not be confirmed.
Deep data QC
metadata only · no data-level QC for this typeStandardized, field-standard QC computed by touching the data — every metric states how it was obtained
measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently