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Processing single-cell RNA-seq datasets using SingCellaR

<ul> <li>cellranger_output.zip : Zipped files for cellranger output</li> <li>Code.zip: This contains the code for Step2-7</li> <li>Human_genesets.zip: This includes the geneset signatures (.gmt) files we used in the protocol and original study (Roy et al, 2021)</li> <li>Human_HSPC_All.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 4 on a local computer&nbsp;</li> <li>ABM_1.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 3&nbsp;on a local computer&nbsp;</li> <li>eFL_All.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 3&nbsp;on a local computer&nbsp;</li> <li>FBM_All.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 3&nbsp;on a local computer&nbsp;</li> <li>FL_All.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 3&nbsp;on a local computer&nbsp;</li> <li>PBM_All.SingCellaR.rdata:&nbsp;The SingCellaR objects generated in Step 3&nbsp;on a local computer&nbsp;</li> <li>meta.data.txt: meta data includes the donor and batch information&nbsp;</li> </ul>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0