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Now on BioConductor! DepecheR is an R package for clustering cytometry and single-cell RNA sequencing data according to a minimal set of biological markers. This is done using repeated application of a penalized k-means algorithm, which penalizes partitions that depend on a large number of markers. DepecheR tunes the level of penalization by testing a range of penalization levels, thus minimizing the need for assumptions and arbitrary decisions.

Home Page: https://bioconductor.org/packages/release/bioc/html/DepecheR.html

License: Other

R 88.45% C++ 11.55%

depecher's Introduction

DepecheR is now on Bioconductor! Please go to https://bioconductor.org/packages/release/bioc/html/DepecheR.html to get access to vinjettes, and to install the latest release or development versions.

DepecheR is an R package for clustering cytometry and single-cell RNA sequencing data according to a minimal set of biological markers. This is done using repeated application of a penalized k-means algorithm, which penalizes partitions that depend on a large number of markers. DepecheR tunes the level of penalization by testing a range of penalization levels, thus minimizing the need for assumptions and arbitrary decisions. DepecheR however also contains a function suite for statistical interpretation of group differences, and for visualization of said group differences, in addition to population densities, marker expressions, et cetera.

depecher's People

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atheorell avatar jtheorell avatar nturaga avatar zimoun avatar

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yochannah zimoun

depecher's Issues

R version ?

Dear,

Thank you for the package and sharing it.

Before investing some time, I would to know if the package really requires R>=3.6 or if it should work with older version of R. Because R>=3.6 is not in Debian stable yet, for example.

Thank you in advance for any comment.

A question about transformation

Hello, I knew DEPECHE from a paper A comparison framework and guideline of clustering methods for mass cytometry data, and have just read your paper.
I think this method is excellent, but for transformation, I knew flow and mass cytometry data are with narrow range in low expression and wide range in high expression, so almost all other methods use logicle or asinh or so to transform data at first. But in your paper, it said DEPECHE only does log transformation when the kurtosis of data is high. So, my question is, why can DEPECHE be different from other methods in transformation? What if we do logicle or asinh transformation before DEPECHE?
Thank you!

Parameters must be able to be forced to be a non-negative integers

Hello, I tried depeche with a piece of mass cytometry data, but it didn't goes well.
The output:

As the dataset has less than 100 columns, peak centering is
                    applied.
Set 1 with 10 iterations completed in 47 seconds.
Set 2 with 10 iterations completed in 40 seconds.
The optimization was iterated 20 times.
Error in seq_len(ncol(clusterCentersMultSd)) :
  参数必需能被强制改变成非负整数
此外: Warning message:
In seq_len(ncol(clusterCentersMultSd)) : 只用'length.out'参数里的第一个元素

the first Error Chinese line means: Parameters must be able to be forced to be a non-negative integers
the last Warning Chinese line: Only use the first element of parameter 'length.out'

I guess there is something wrong in clusterCentersMultSd, but I don't know how to check.
I pre-transformed data by asinh. Should I use other transforming method?

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