Re: [R] algorithms that cluster time series data

2020-07-06 Thread Bert Gunter
And since this is about RNA expression data, you would do better posting on
the Bioconductor Help site rather than here. You are more likely to find
the expertise and interest you seek there.

Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Mon, Jul 6, 2020 at 10:22 AM Sarah Goslee  wrote:

> Hi,
>
> Unsupervised classification (clustering) is a huge field. There's an
> entire task view devoted to it, where you can see many of the large
> array of R packages that perform some sort of clustering.
>
> https://cran.r-project.org/web/views/Cluster.html
>
> Since that is an overwhelming list, you may be best served by looking
> at how others in your field have approached similar problems, and then
> look for R packages that perform the relevant analyses.
>
> Sarah
>
> On Mon, Jul 6, 2020 at 1:11 PM Bogdan Tanasa  wrote:
> >
> > Dear all,
> >
> > please may I ask for a suggestion regarding the algorithms to cluster the
> > expression data in single cells (scRNA-seq) at multiple time points :
> >
> > we do have expression data for 30 000 genes  in 10 datasets that have
> been
> > collected at multiple time points,
> >
> > and i was wondering if you could please recommend *any algorithms/R
> > packages that could help with the clustering of the gene expression at
> > different time points.* thanks a lot, and all the best,
> >
> > -- bogdan
> >
> > [[alternative HTML version deleted]]
> >
> > __
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Sarah Goslee (she/her)
> http://www.numberwright.com
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

[[alternative HTML version deleted]]

__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


Re: [R] algorithms that cluster time series data

2020-07-06 Thread Bogdan Tanasa
Dear Sarah,

thank you very much for pointing to the list of available packages and
algorithms.

On Mon, Jul 6, 2020 at 10:20 AM Sarah Goslee  wrote:

> Hi,
>
> Unsupervised classification (clustering) is a huge field. There's an
> entire task view devoted to it, where you can see many of the large
> array of R packages that perform some sort of clustering.
>
> https://cran.r-project.org/web/views/Cluster.html
>
> Since that is an overwhelming list, you may be best served by looking
> at how others in your field have approached similar problems, and then
> look for R packages that perform the relevant analyses.
>
> Sarah
>
> On Mon, Jul 6, 2020 at 1:11 PM Bogdan Tanasa  wrote:
> >
> > Dear all,
> >
> > please may I ask for a suggestion regarding the algorithms to cluster the
> > expression data in single cells (scRNA-seq) at multiple time points :
> >
> > we do have expression data for 30 000 genes  in 10 datasets that have
> been
> > collected at multiple time points,
> >
> > and i was wondering if you could please recommend *any algorithms/R
> > packages that could help with the clustering of the gene expression at
> > different time points.* thanks a lot, and all the best,
> >
> > -- bogdan
> >
> > [[alternative HTML version deleted]]
> >
> > __
> > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
> Sarah Goslee (she/her)
> http://www.numberwright.com
>

[[alternative HTML version deleted]]

__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.


Re: [R] algorithms that cluster time series data

2020-07-06 Thread Sarah Goslee
Hi,

Unsupervised classification (clustering) is a huge field. There's an
entire task view devoted to it, where you can see many of the large
array of R packages that perform some sort of clustering.

https://cran.r-project.org/web/views/Cluster.html

Since that is an overwhelming list, you may be best served by looking
at how others in your field have approached similar problems, and then
look for R packages that perform the relevant analyses.

Sarah

On Mon, Jul 6, 2020 at 1:11 PM Bogdan Tanasa  wrote:
>
> Dear all,
>
> please may I ask for a suggestion regarding the algorithms to cluster the
> expression data in single cells (scRNA-seq) at multiple time points :
>
> we do have expression data for 30 000 genes  in 10 datasets that have been
> collected at multiple time points,
>
> and i was wondering if you could please recommend *any algorithms/R
> packages that could help with the clustering of the gene expression at
> different time points.* thanks a lot, and all the best,
>
> -- bogdan
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.



-- 
Sarah Goslee (she/her)
http://www.numberwright.com

__
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.