W20: Single-Cell RNA-Seq Analysis with Python - Day 1

[@sfmambero]

1:10 learning objectives 4:10 workshop overview 5:55 intro/motivation for scRNAseq 1:18:40 scRNAseq analysis workflow 2:26:20 scanpy

[@abraaoandrade141]

Amazing content! Where could I download that dataset?

[@megaland4542]

Can you please share the material

[@BreanneSparta]

happy to know people have found this helpful! here is a link to the course materials drive.google.com/drive/u/1/folders/1kuGHpbJFs2qy-Q2YzHXpsuqk7Wfe6hAT

[@iaquine]

Thank you

[@nazim5980]

Thank you ;)

[@musasiziabdul8370]

Thank you

[@mihirswimmer]

Really amazing video. Like others even I am interested in downloading the data , scripts etc . Could you please share the link

[@ariannali7888]

Thank you soooo much my UCLA sister. Im your neighbor from UCSD :D This content is awesome, very comprehensive and clearly-explained. Would it be possible for you to share the slides? I totally understand if there's copyright issue. These are such great learning materials

[@conlosjuguetesdemihijo6310]

please share data ...

[@otaviocoutinho2855]

Share the repository please.

[@chrisjmolina]

Wonderful video! Thank you so much for sharing!

[@fateme7799]

Thanks. could you please share the python scripts?

[@esa3296]

Thanks for the workshop, it is really good introductory workshop for scRNA-Seq Analysis.

[@yfg7691]

Amazing content! Can you please share the slide?

[@hasithasampath1191]

Amazing content. In-depth breakdown. Really interesting with my favorite subject area.

[@utkukaba]

Can you share the codes?

[@jiangxu3895]

Flow cytometry's result (protein expression level) is not directly linked to single cell sequencing result, and that's where cite-seq or related technique come into play.

[@MegaGmail007]

Thanks for this amazing classes.