How to Use t-SNE Effectively

Re:How to Use t-SNE Effectively by gww on Sunday October 16, 2016 @10:47AM (#12718806)

There are so many arbitrary choices made when analyzing single cell RNA-seq data. There coverage cutoffs to decide when a gene is expressed, arbitrary QA points to decide when a cell is "good quality", the PC's chosen for t-SNE, the genes identified as more variable than estimated levels of technical noise, etc etc. is very frustrating. This leads to huge issues with reproducibility, almost every single paper uses their own in house custom analysis pipelines and they rarely make them open source.

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Re:How to Use t-SNE Effectively by daemonk on Sunday October 16, 2016 @03:06PM (#12720349)

The main reason is because no one wants to publish technical or "methods" type papers where they assess the technology. There is usually one or two initial big papers introducing the technology that makes a big splash. No one subsequently will want to assess it or improve much on it because it won't publish well and you likely will not get cited for it anyways.

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Re:How to Use t-SNE Effectively by lasso1 on Sunday October 16, 2016 @12:48PM (#12719511)

Yes i know the feeling.

Our field has some very arbitrary threshold for noise on single features, sounds like there is some slightly more principled strategy in single cell genomics?

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