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The deconvR is an R package designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model.
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.
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
Line 43 in 19a3b0f
İf probe_id_locations is a data.frame, this error returns
I came by the package home page to reference the package for my thesis and noticed the page does not show the package author information at the bottom of the page (see screenshot).
You probably just need to rebuild the page. However it seems like the version displayed on the home page differs from both the version specified in the package description, as well as the version on Bioconductor.
The BiocCheck function was used to determine if the deconvR complies with the Bioconductor package guidelines. deconvR failed in the test and, the issues needed to be solved detected
Summary:
ERROR count: 1
WARNING count: 2
NOTE count: 6
For detailed information about these checks, see the BiocCheck vignette,
https://bioconductor.org/packages/3.13/bioc/vignettes/BiocCheck/inst/doc/BiocCheck.html#interpreting-bioccheck-output
For more information : bioconductor code styling
> 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
deconvR/R/findPartialRsquare.R
Line 9 in 4587e1e
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.
For more information BiocStyle Vignette
deconvR/R/findPartialRsquare.R
Line 24 in dda6a22
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.
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