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deconvr's Issues

`nu` parameter space in `deconvModel.R` may be misspecified

In deconvModel.R, the parameter space for nu is specified as seq(0.25, 0.5, 0.75) (line). However, this will only return 0.25 as 0.5 is the end of the sequence and 0.75 is the step size. It looks like the intent was that this is the other way round. I will submit a pull request to fix this.

BiocCheck takes too long

We may need to upload the data as a new format or, create an ExperimentHub data package or, update from external sources. The current size is now over 9MB

Vignette

  • The doParallel package needs to be added to the deconvRManual.Rmd file
    vignette

findSignatures problem

findSignatures <- function(samples, sampleMeta, atlas = NULL,

I don't fully understand what this function does? it looks like its filtering based on variation cutoff but that's not what we usually mean by signatures

Code Formatting

  • The codes needed to be formatted
  • Comments needed to be organized
  • BiocCheck warning don't use is() instead of class(), is there any other way to solve this warning?
  • BSmeth2Probe name is appropriate or not? If not, need to be changed
  • CRAN allows documentation size up to 5 MB, deconvR has documentation size 6.3 MB. Is this an issue, if it is, how it can be solved?

For more information : bioconductor code styling

Using data.table object as reference in simulateCellmix returns different simulation result

> set.seed(123)
> simulateCellMix(numberOfSamples = 1,reference = HumanCellTypeMethAtlas)$simulated %>% head
         IDs   Sample 1
1 cg08169020 0.03366977
2 cg25913761 0.22689863
3 cg26955540 0.12154620
4 cg25170017 0.09571837
5 cg12827637 0.04874315
6 cg19442545 0.03669916
> set.seed(123)
> simulateCellMix(numberOfSamples = 1,reference = as_tibble(HumanCellTypeMethAtlas))$simulated %>% head
         IDs   Sample 1
1 cg08169020 0.03366977
2 cg25913761 0.22689863
3 cg26955540 0.12154620
4 cg25170017 0.09571837
5 cg12827637 0.04874315
6 cg19442545 0.03669916
> set.seed(123)
> simulateCellMix(numberOfSamples = 1,reference = as.data.table(HumanCellTypeMethAtlas))$simulated %>% head
         IDs Sample 1
1 cg08169020 14.23628
2 cg25913761 14.23628
3 cg26955540 14.23628
4 cg25170017 14.23628
5 cg12827637 14.23628
6 cg19442545 14.23628

better documentation for findPartialRsquared

#' merged on the ID column (with NAs removed).

in the details section, we should have a text similar to the one below:

We have this function to check if deconvolution brings anything useful on top of the basic bimodal methylation profiles. in case of methylation, the reference matrix usually follows a bimodal distribution an taking the average of the rows of methylation matrix might give a pretty similar profile to the bulk methylation profile you are trying to deconvolute. If the deconvolution is useful partial R-squared is high.

edit DESCRIPTION

please add irem to the authors. If you are going to submit this to bioconductor please add one more maintainer. If that's not the aim, leave it as is.

bugs need to be fixed within BSmeth2Probe function

  • 1. unable to find an inherited method for function ‘findOverlaps’ for signature ‘"GRanges", "data.frame"’
    (This error is returned if probe_id_locations is a data.frame)
  • 2. print(paste(...)) argumants must be replaced with appropriate function
  • 3. simulateCellMix returns 0 bug need to be fixed
  • 4. deconvolute nnls executing %dopar% sequentially: no parallel backend registered
  • 5. since the dependencies took so long to install, find a way to reduce this time
  • 6. Supported methylkit objects needed to be checked
  • 7. Get rid of the for loops, they took so much time

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