johnreid/delorean

R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.

DeLorean

R package to model time series accounting for noise in the temporal dimension. Specifically designed for single cell transcriptome experiments.

Requirements

To render the vignettes you will need a working version (> 1.12.3) of pandoc on your machine with the pandoc-citeproc filter. On Ubuntu do:

sudo apt-get install pandoc pandoc-citeproc

Installation from CRAN

Just run the following in an R session:

install.packages('DeLorean')

Installation from source

If you prefer to have the very latest version you can install DeLorean from source. If you do not already have devtools installed, then install it by running:

install.packages('devtools')

Now you can install the development version of DeLorean with:

devtools::install_github('JohnReid/DeLorean')

Documentation

Read the vignette:

vignette('DeLorean')

delorean's Issues

Mismatch between arguments in vignette and latest CRAN version

Hi there

Interesting methods that I'm excited to try on my data. There is a mismatch between the code example for estimate.hyper in the vignette:

dl <- estimate.hyper(dl, sigma.tau = 0.5, length.scale = 1.5,
+ model.name = 'simple-model')

And the actual current version (v1.2.3) downloaded from CRAN today, permits exact, exact-sizes, lowrank and lowrank-sizes as valid model.name arguments in estimate.hyper.

If you're not currently building a live vignette when building the package then I would recommend doing this to avoid this kind of issue.

Best wishes
Davis

Creating pseudo time within a time point

Sorry, this is not an issue rather a question,

I am working with a mold right now without a defined list of cell cycle genes. At vegetative stage (time point 0) this organism is barely expression cell cycle genes (growing). I need to arrange cells in time point 0 by pseudo time regarding cell cycle. Is it possible only by using one time point? I have 9 time points but after time point 0 mold starts to differentiation.

could you please kindly guid me?

Thanks a lot in advance

Interpreting prior means of pseudotime plot

Dear John and developers,

I find your tool super interesting. I have a single cell data set with 12 time points that I managed to reduce to 4 thousand cells to be able to run DeLorean (I understand these are already a lot). I ran the vb method and got no issues (I only changed the tol_rel_obj parameter).

I looked at the pseudotime and the prior means are very tight between each other, while the sampled cells plotted seem more disperse as well as pseudotime trends. I think the pseudotime vs capture corresponds well, but I am not sure how to finally interpret it. Are these results okay? Do you have any advice? Is there a way to contrast different conditions? I have uploaded a screen shot of the figures (btw, the trend of the example gene is correct)

Many thanks,
Ivan

how to create a Delorean object

Check if McDavid results work with low rank model

Cannot use estimate.hyper() with dplyr/plyr conflict

In plyr, group_by(gene) fails to give a table with the gene column preserved.
In dplyr, it does preserve the column.

Other packages that run a process with R and dplyr and plyr both loaded will most likely conflict without the direct namespace reference (e.g. a Shiny app that wants to use both the Monocle and DeLorean package will cause problems).