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Note that the saved objects link to fragment files stored remotely on AWS servers. This enables running analyses without needing to download the entire fragment file and link it to the object locally. However, for users running more extensive analyses (re-quantifying genomic regions, for example) we recommend downloading the fragment files to your local computer and linking them to the object. For example: ``` Fragments(obj) <- NULL # remove fragment file information Fragments(obj) <- CreateFragmentObject(path = , cells = colnames(obj)) # add a new fragment object ```
These datasets were generated using single-cell NTT-seq. Please see the publication for a full description of methods: Stuart, Tim, Stephanie Hao, Bingjie Zhang, Levan Mekerishvili, Dan Landau, Silas Maniatis, Rahul Satija, and Ivan Raimondi. 2022. “Nanobody-Tethered Transposition Allows for Multifactorial Chromatin Profiling at Single-Cell Resolution.” bioRxiv. doi:10.1101/2022.03.08.483436. Associated fragment files can be found on NCBI GEO: GSE212588
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 38 | |
| downloads | 13 |

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