ben-laufer / dmrichr Goto Github PK

A R package and executable for the preprocessing, statistical analysis, and downstream testing and visualization of differentially methylated regions (DMRs) from CpG count matrices (Bismark cytosine reports)

Home Page: https://www.benlaufer.com/DMRichR/

License: MIT License

R 98.96% Shell 1.04%

bioinformatics dna-methylation wgbs cpg biostatistics dmrs workflow rrbs em-seq tidyverse

dmrichr's Introduction

DMRichR

Enrich Your Differentially Methylated Region (DMR) Analysis with the Tidyverse

Website: ben-laufer.github.io/DMRichR/

Overview

DMRichR is an R package and executable for the preprocessing, statistical analysis, and visualization of differentially methylated regions (DMRs) and global methylation levels from CpG count matrices (Bismark cytosine reports). These files can be generated from your own pipeline or through the CpG_Me pipeline.

DMRichR enables the analysis of data from whole genome bisulfite sequencing (WGBS), enzymatic methyl-seq (EM-seq), and reduced representation bisulfite sequencing (RRBS). The overarching theme of DMRichR is the synthesis of popular Bioconductor R packages for the analysis of genomic data with the tidyverse philosophy of R programming.

DMRichR::DM.R() is a single function that performs all of the following steps:

Installation

You can install the package using the following code:

if(!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
if(!requireNamespace("remotes", quietly = TRUE))
  install.packages("remotes")
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = TRUE)
BiocManager::install("ben-laufer/DMRichR")

If you are running into installation errors with the package dependencies, make sure that you have bioconductor v3.16 and R v4.2 installed. macOS users will have to install XQuartz manually or through Homebrew using brew install xquartz --cask.

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dmrichr's Issues

style specified by 'UCSC' error

Dear Ben,
Thank you for your help.

I get an erorr using DMRichR::processBismark() as follow.

Assigning sample metadata with Diagnosis as factor of interest...
DataFrame with 4 rows and 1 column
Diagnosis

sample10 lowc
sample11 lowc
sample22 normalc
sample24 normalc
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'unique': error in evaluating the argument 'y' in selecting a method for function 'intersect': The style specified by 'UCSC' does not have a compatible entry for the species Caenorhabditis_elegans - Copy

Here is the head of my cytosine report file for one of the samples:
scaffold_135 163 + 0 0 CG CGA
scaffold_135 164 - 0 0 CG CGG
scaffold_135 479 + 0 0 CG CGA
scaffold_135 480 - 0 0 CG CGG
scaffold_135 523 + 0 0 CG CGA
scaffold_135 524 - 0 0 CG CGC
scaffold_135 572 + 0 0 CG CGG
scaffold_135 573 - 0 0 CG CGA
scaffold_135 947 + 0 0 CG CGT
scaffold_135 948 - 0 0 CG CGC

Here is the metadat xlsx file:

Name Diagnosis
sample10 lowc
sample11 lowc
sample22 normalc
sample24 normalc

Here is the tree structure for the cytosine reports and design matrix:

│ ├── sample10_bismark_bt2.deduplicated.bismark.cov.gz.CpG_report.txt.gz
│ ├── sample11_bismark_bt2.deduplicated.bismark.cov.gz.CpG_report.txt.gz
│ ├── sample22_bismark_bt2.deduplicated.bismark.cov.gz.CpG_report.txt.gz
│ ├── sample24_bismark_bt2.deduplicated.bismark.cov.gz.CpG_report.txt.gz
│ ├── sample_info.xlsx

Error during the adjustment covariate

Hi!

I am running 80 RRBS samples, with 3 covariates: Age, Gender and Batch.
this is my running code: run.txt
I tried with 2 samples but without Batch as a covariate and it worked.
The error that I get is the following:

Rscript --vanilla /ibex/scratch/covid19/dnam/dmrsrich/DM.R --genome hg38 --coverage 1 --perGroup '0.75' --minCpGs 5 --maxPerms 10 --maxBlockPerms 10 --cutoff '0.05' --testCovariate Group --adjustCovariate 'Sex;Age;Batch' --sexCheck TRUE --GOfuncR TRUE --EnsDb FALSE --cores 20

[DMRichR] Initializing 					 15-11-2023 09:08:58 PM 

[DMRichR] Processing arguments from script 		 15-11-2023 09:09:30 PM 
genome = hg38
coverage = 1
perGroup = 0.75
minCpGs = 5
maxPerms = 10
maxBlockPerms = 10
cutoff = 0.05
testCovariate = Group
adjustCovariate = Sex
adjustCovariate = Age
adjustCovariate = Batch
cores = 20
sexCheck = TRUE
EnsDb = FALSE
GOfuncR = TRUE

[DMRichR] Selecting annotation databases 		 15-11-2023 09:09:31 PM 
Loading BSgenome.Hsapiens.UCSC.hg38.masked
Loading TxDb.Hsapiens.UCSC.hg38.knownGene
Loading org.Hs.eg.db
Saving Rdata...

[DMRichR] Processing Bismark cytosine reports 		 15-11-2023 09:09:36 PM 
Selecting files...
Reading cytosine reports...
[read.bismark] Parsing files and constructing valid loci ...
Done in 117 secs
[read.bismark] Parsing files and constructing 'M' and 'Cov' matrices ...
Done in 75.1 secs
[read.bismark] Constructing BSseq object ... 
Assigning sample metadata with Group as factor of interest...
DataFrame with 80 rows and 4 columns
         Group       Age      Sex    Batch
      <factor> <numeric> <factor> <factor>
ICU01  ICU_ABS        72   Male    Batch12
ICU02  ICU_ABS        51   Female  Batch12
ICU03  ICU_ABS        64   Male    Batch1 
ICU04  ICU_ABS        71   Male    Batch1 
ICU05  ICU_ABS        50   Female  Batch1 
...        ...       ...      ...      ...
NON36   NONICU        63   Female  Batch10
NON37   NONICU        84   Female  Batch10
NON38   NONICU        33   Male    Batch10
NON39   NONICU        77   Male    Batch10
NON40   NONICU        84   Female  Batch11
Checking sex of samples...
Sex of all samples matched correctly. Sex choromosomes will now be dropped
Filtering CpGs for Group...
Assuming adjustment covariate Sex is discrete and including it for filtering...
Assuming adjustment covariate Age is continuous and excluding it from filtering...
Assuming adjustment covariate Batch is discrete and including it for filtering...
slurmstepd: error: poll(): Bad address
Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : 
  cannot coerce class '"try-error"' to a data.frame
Calls: <Anonymous> ... <Anonymous> -> as.data.frame -> as.data.frame.default
In addition: Warning messages:
1: In dir.create("RData") : 'RData' already exists
2: In parallel::mclapply(., FUN = as.matrix, mc.cores = cores) :
  scheduled cores 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 19, 20 did not deliver results, all values of the jobs will be affected
3: In parallel::mclapply(., FUN = DelayedMatrixStats::colSums2, mc.cores = cores) :
  scheduled cores 19, 18, 17, 16, 15, 12, 11, 10, 9, 8, 7, 6, 5, 4, 2, 20 encountered errors in user code, all values of the jobs will be affected
Execution halted
10502

I will run two samples with the Batch as a covariate. Before I also tried to run with different number of cpu-per-task (1, 10 and 20) as well as ntask (1, 20, 20) in my SBATCH params.

Using newer Ontology for enrichR

When I use the default ontology databases for the enrichR program it works fine:

(dbs <- c("GO_Biological_Process_2018",
+                  "GO_Cellular_Component_2018",
+                  "GO_Molecular_Function_2018",
+                  "KEGG_2019_Human",
+                  "Panther_2016",
+                  "Reactome_2016",
+                  "RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO")

If I change the databases to it doesn't work:

dbs <- c("GO_Biological_Process_2023",
+                  "GO_Cellular_Component_2023",
+                  "GO_Molecular_Function_2023",
+                  "KEGG_2021_Human",
+                  "Reactome_2022")Running enrichR

Welcome to enrichR
Checking connection ...
Enrichr ... Connection is Live!
FlyEnrichr ... Connection is Live!
WormEnrichr ... Connection is Live!
YeastEnrichr ... Connection is Live!
FishEnrichr ... Connection is Live!
OxEnrichr ... Connection is Live!
Annotating 13461 regions from hg38 with gene symbols
Building CpG islands...
Building CpG shores...
Building CpG shelves...
Building inter-CpG-islands...
'select()' returned 1:many mapping between keys and columns
Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2023... Done.
Querying GO_Cellular_Component_2023... Done.
Querying GO_Molecular_Function_2023... Done.
Querying KEGG_2021_Human... Done.
Querying Reactome_2022... Done.
Parsing results... Done.
Submiting results from enrichR to rrvgo...
Warning: enrichR did not finish. The website may be down or there are internet connection issues.

Is there an issue with rrvgo and new ontology databases? I am working with a human dataset

It seems to get to this line and stop as I get the previous file "Ontologies/enrichr.xlsx" but nothing else after:

        DMRichR::slimGO(tool = "enrichR",
                        annoDb = annoDb,
                        plots = FALSE) %T>%
        openxlsx::write.xlsx(file = glue::glue("Ontologies/enrichr_slimmed_results.xlsx")) %>%

Installation compatibility issues with BioCManager and [email protected]

Hi,

The installation instructions suggest using [email protected]

Should I be using BiocManager 3.14?

The instructions say to use the latest BiocManager

"make sure that you have the latest version of bioconductor"

`> if(!requireNamespace("BiocManager", quietly = TRUE))

install.packages("BiocManager")

if(!requireNamespace("remotes", quietly = TRUE))
install.packages("remotes")
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = TRUE)
BiocManager::install("ben-laufer/DMRichR")
Error: Bioconductor version '3.16' requires R version '4.2'; use
BiocManager::install(version = '3.14') with R version 4.1; see
https://bioconductor.org/install
BiocManager::install(version = "3.17")
Error: Bioconductor version '3.17' requires R version '4.3'; use
BiocManager::install(version = '3.14') with R version 4.1; see
https://bioconductor.org/install`

Large candidate regions with more than 1000 CpGs detected

Thanks for making DMRichR!

In one exploratory analysis I have, I ran into this warning:

[DMRichR] Testing for DMRs with dmrseq                   22-03-2023 10:45:04 AM
Performing a global test of H0: no difference among 4 groups (assuming the test covariate Diagnosis is a factor).
Parallelizing using 8 workers/cores (backend: BiocParallel:MulticoreParam).
Computing on 1 chromosome(s) at a time.

Detecting candidate regions with coefficient larger than 0.05 in magnitude.
...Chromosome chr1: Smoothed (1.21 min). Note: Large candidate regions with more than 1000 CpGs detected. If you'd like to detect large-scale blocks, set block=TRUE. If you'd like to detect local DMRs, it is recommended to decrease the values of bpSpan, minInSpan, and/or maxGapSmooth increase computational efficiency.

This specific warning comes from a helper function in dmrseq: https://github.com/kdkorthauer/dmrseq/blob/master/R/helper_functions.R#L573

Unfortunately, afterwards DMRichR consumes lots of RAM (~400+GB) and usually gets oomkilled.

Is there an easy way to pass through block=TRUE as suggested to dmrseq, or have you run into this issue before?

Parsing files and constructing valid loci

Hello Ben! Trying out your stuff. I had few issues that I have fixed (reported) but wonder about this one below ... My files from bismark_covarge are as follow: *.cov.gz

chr9 10397 10397 100 1 0
chr9 10403 10403 100 1 0
chr9 10468 10468 100 2 0
chr9 10469 10469 100 1 0

Cheers, Marcin

Error in processBismark call to read.bismark

Hey, I got this error in the processBismark function while running DM.R:

[DMRichR] Loading Bismark cytosine reports 06-11-2018 12:11:07 PM
Error in read.bismark(files = files, sampleNames = names, rmZeroCov = TRUE, :
unused arguments (sampleNames = names, fileType = "cytosineReport", mc.cores = mc.cores)
Calls: processBismark
Execution halted

I think sampleNames, fileType, and mc.cores are extra arguments. Could you remove them?

Thanks!

Cannot install this package "ben-laufer / DMRichR" with error information

Hi Sir,

I just want to use this package to visualise the methylation calling result.

I think your sample visualization for CpG count matrix is beautiful.

But when I tried to install it with below tutorial:

if(!requireNamespace("BiocManager", quietly = TRUE))
  install.packages("BiocManager")
if(!requireNamespace("remotes", quietly = TRUE))
  install.packages("remotes")
Sys.setenv("R_REMOTES_NO_ERRORS_FROM_WARNINGS" = TRUE)
BiocManager::install("ben-laufer/DMRichR")

The error information appeared:

> BiocManager::install("ben-laufer/DMRichR")
'getOption("repos")' replaces Bioconductor standard repositories, see 'help("repositories", package = "BiocManager")'
for details.
Replacement repositories:
    CRAN: https://cran.rstudio.com/
Bioconductor version 3.16 (BiocManager 1.30.20), R 4.2.1 (2022-06-23 ucrt)
Installing github package(s) 'ben-laufer/DMRichR'
Downloading GitHub repo ben-laufer/DMRichR@HEAD
Error in utils::download.file(url, path, method = method, quiet = quiet,  : 
  cannot open URL 'https://api.github.com/repos/ben-laufer/DMRichR/tarball/HEAD'
Installation paths not writeable, unable to update packages
  path: C:/Program Files/R/R-4.2.1/library
  packages:
    boot, class, cluster, codetools, foreign, MASS, Matrix, mgcv, nlme, nnet, rpart, spatial, survival
Old packages: 'BiocParallel', 'cli', 'XML'
Update all/some/none? [a/s/n]: 
n

I also failed to install it manually.

Can you give me some advice. I don't know if it is OK for me to use the source code of this package only.

next step after add a custom genome

Hello Ben,

Thank you very much for your DMRichR package.

I am working with a non-model species and would like to use your package. I have followed your advice (issues #37 and #63) to generate my own TxDb, BSgenome and Org.db for my species. I have modified some of the code in DMRichR: annotationDatabases() and DM.R. and run the modified scripts.I am attaching the modified scripts and have highlighted the changes in yellow.

Now, I have 2 functions in my R environment with the modifications to add my genome. I'm not sure I've understood how to proceed next ? How do I run the analysis ?

Thanks for your help,

Eva

DM_dlabrax.odt
annotationDatabases_dlabrax.odt

processBismark Error

Hello,
I get an error message when I run the command line :
DMRichR::DM.R(genome = "dlabrax2021",testCovariate = "Condition", cores = 6)

Please note that I have modified the package to add my own genome.

I get the same message when I run this part of the script:
bs.filtered <- DMRichR::processBismark(files = list.files(path = getwd(), pattern = "*.CpG_report.txt.gz"), meta = openxlsx::read.xlsx("sample_info.xlsx", colNames = TRUE) %>% dplyr::mutate_if(is.character, as.factor), testCovariate = "Condition", adjustCovariate = NULL, matchCovariate = NULL, coverage = 1, cores = 6, perGroup = 0.75, sexCheck = FALSE)

Here's the message:

[DMRichR] Processing Bismark cytosine reports 25-04-2024 12:46:37
Selecting files...
Reading cytosine reports...
[read.bismark] Parsing files and constructing valid loci ...
Done in 42.8 secs
[read.bismark] Parsing files and constructing 'M' and 'Cov' matrices ...
Done in 45.4 secs
[read.bismark] Constructing BSseq object ...
Assigning sample metadata with Condition as factor of interest...
DataFrame with 10 rows and 1 column
Condition

FW-a1 FW-a
FW-a2 FW-a
FW-a3 FW-a
FW-a4 FW-a
FW-a5 FW-a
FW-i1 FW-i
FW-i2 FW-i
FW-i3 FW-i
FW-i4 FW-i
FW-i5 FW-i
Filtering CpGs for Condition...
Making coverage filter table...
Error in MatrixGenerics:::.load_next_suggested_package_to_search(x) :
Failed to find a rowSums2() method for list objects.

How can I solve this problem? I reinstalled the MatrixGenerics package but it didn't work.

Thank you for your help

Custom genomes

Hello @El-Castor, to answer your question about custom genomes from #36, there are a few possibilities. If you can find a BSgenome, a TxDb, and a org.db on Bioconductor then you only need to add the reference to them into the "Setup annotation databases" section of DM.R by copying the style used. If those don't exist, then you can try to manually make them using other existing external databases. Alternatively, you can further modify DM.R to LiftOver to a closely related genome after the block and DMR calling. Finally, if you don't need the downstream enrichment analyses and data visualizations, and just want the coverage filtering, block calling, and DMR calling, then the simplest route is to delete the other sections from the script, since the first parts don't rely on those databases.

Error in h(simpleError(msg, call)) : error in evaluating the argument 'args' in selecting a method for function 'do.call': GRanges objects don't support [[, as.list(), lapply(), or unlist() at the moment

Dear author,
I got a error message when I tried to use this impressive package for WGBS analysis.
I wrote R scripts like this,
DMRichR::DM.R(genome=c("hg38"),testCovariate = "Diagnosis", adjustCovariate = c("Sex","Age"), sexCheck = TRUE, cores = 50, coverage = 1, perGroup = 0.8, minCpGs = 5, maxPerms = 12, maxBlockPerms = 12,cutoff = 0.05,EnsDb = TRUE, cellComposition = FALSE,GOfuncR = TRUE)

The error message in R console is,

[DMRichR] Smoothing individual methylation values 		 15-09-2021 03:59:41 AM 
  |==============================================================================================================================================| 100%

Extracting individual smoothed methylation values of DMRs...
Saving individual smoothed methylation values to DMRs/DMR_individual_smoothed_methylation.txt
Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'args' in selecting a method for function 'do.call': GRanges objects don't support [[, as.list(), lapply(), or unlist() at the moment

I think that the error occurred at the step of "ChromHMM and Reference Epigenome Enrichments."

My data are 6 control and 6 cases CpG report files which were generated from subsampling bam files by bismark and samtools view -bs function. However, I don't think that the input files are the problem because Bismark would make intact output files.

How to solve this error?

The session info is below,

R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ensembldb_2.16.4                           AnnotationFilter_1.16.0                    org.Hs.eg.db_3.13.0                       
 [4] TxDb.Hsapiens.UCSC.hg38.knownGene_3.13.0   GenomicFeatures_1.44.2                     AnnotationDbi_1.54.1                      
 [7] BSgenome.Hsapiens.UCSC.hg38.masked_1.3.993 BSgenome.Hsapiens.UCSC.hg38_1.4.3          BSgenome_1.60.0                           
[10] rtracklayer_1.52.1                         Biostrings_2.60.2                          XVector_0.32.0                            
[13] DMRichR_1.7.2                              dmrseq_1.12.0                              bsseq_1.28.0                              
[16] SummarizedExperiment_1.22.0                Biobase_2.52.0                             MatrixGenerics_1.4.3                      
[19] matrixStats_0.60.1                         GenomicRanges_1.44.0                       GenomeInfoDb_1.28.4                       
[22] IRanges_2.26.0                             S4Vectors_0.30.0                           BiocGenerics_0.38.0                       

loaded via a namespace (and not attached):
  [1] Hmisc_4.5-0                                        ps_1.6.0                                          
  [3] class_7.3-18                                       Rsamtools_2.8.0                                   
  [5] rprojroot_2.0.2                                    foreach_1.5.1                                     
  [7] crayon_1.4.1                                       MASS_7.3-53                                       
  [9] rhdf5filters_1.4.0                                 nlme_3.1-151                                      
 [11] backports_1.2.1                                    GOSemSim_2.18.1                                   
 [13] rlang_0.4.11                                       readxl_1.3.1                                      
 [15] SparseM_1.81                                       callr_3.7.0                                       
 [17] limma_3.48.3                                       minfi_1.38.0                                      
 [19] filelock_1.0.2                                     plyranges_1.12.1                                  
 [21] BiocParallel_1.26.2                                hablar_0.3.0                                      
 [23] rjson_0.2.20                                       bit64_4.0.5                                       
 [25] glue_1.4.2                                         pheatmap_1.0.12                                   
 [27] rngtools_1.5                                       processx_3.5.2                                    
 [29] regioneR_1.24.0                                    R2HTML_2.3.2                                      
 [31] tcltk_4.1.0                                        DOSE_3.18.2                                       
 [33] tidyselect_1.1.1                                   XML_3.99-0.7                                      
 [35] tidyr_1.1.3                                        zoo_1.8-9                                         
 [37] GenomicAlignments_1.28.0                           xtable_1.8-4                                      
 [39] magrittr_2.0.1                                     PerformanceAnalytics_2.0.4                        
 [41] ggplot2_3.3.5                                      cli_3.0.1                                         
 [43] sm_2.2-5.7                                         zlibbioc_1.38.0                                   
 [45] ggbiplot_0.55                                      rstudioapi_0.13                                   
 [47] doRNG_1.8.2                                        rpart_4.1-15                                      
 [49] wordcloud_2.6                                      fastmatch_1.1-3                                   
 [51] treeio_1.16.2                                      shiny_1.6.0                                       
 [53] xfun_0.26                                          tm_0.7-8                                          
 [55] askpass_1.1                                        pkgbuild_1.2.0                                    
 [57] multtest_2.48.0                                    cluster_2.1.0                                     
 [59] caTools_1.18.2                                     tidygraph_1.2.0                                   
 [61] KEGGREST_1.32.0                                    tibble_3.1.4                                      
 [63] interactiveDisplayBase_1.30.0                      ggrepel_0.9.1                                     
 [65] base64_2.0                                         mapplots_1.5.1                                    
 [67] ape_5.5                                            scrime_1.3.5                                      
 [69] GOfuncR_1.12.0                                     png_0.1-7                                         
 [71] permute_0.9-5                                      reshape_0.8.8                                     
 [73] withr_2.4.2                                        slam_0.1-48                                       
 [75] bitops_1.0-7                                       ggforce_0.3.3                                     
 [77] RBGL_1.68.0                                        cellranger_1.1.0                                  
 [79] plyr_1.8.6                                         e1071_1.7-8                                       
 [81] LOLA_1.22.0                                        coda_0.19-4                                       
 [83] pillar_1.6.2                                       bumphunter_1.34.0                                 
 [85] biocViews_1.60.0                                   gplots_3.1.1                                      
 [87] GlobalOptions_0.1.2                                cachem_1.0.6                                      
 [89] NLP_0.2-1                                          GetoptLong_1.0.5                                  
 [91] RUnit_0.4.32                                       DelayedMatrixStats_1.14.3                         
 [93] xts_0.12.1                                         vctrs_0.3.8                                       
 [95] ellipsis_0.3.2                                     generics_0.1.0                                    
 [97] outliers_0.14                                      tools_4.1.0                                       
 [99] foreign_0.8-81                                     munsell_0.5.0                                     
[101] tweenr_1.0.2                                       proxy_0.4-26                                      
[103] fgsea_1.18.0                                       emmeans_1.6.3                                     
[105] DelayedArray_0.18.0                                fastmap_1.1.0                                     
[107] compiler_4.1.0                                     httpuv_1.6.3                                      
[109] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2            beanplot_1.2                                      
[111] gt_0.3.1                                           GenomeInfoDbData_1.2.6                            
[113] gridExtra_2.3                                      enrichR_3.0                                       
[115] edgeR_3.34.1                                       lattice_0.20-41                                   
[117] utf8_1.2.2                                         later_1.3.0                                       
[119] dplyr_1.0.7                                        BiocFileCache_2.0.0                               
[121] rrvgo_1.4.4                                        jsonlite_1.7.2                                    
[123] methylCC_1.6.0                                     scales_1.1.1                                      
[125] graph_1.70.0                                       lsmeans_2.30-0                                    
[127] tidytree_0.3.5                                     sparseMatrixStats_1.4.2                           
[129] estimability_1.3                                   genefilter_1.74.0                                 
[131] lazyeval_0.2.2                                     promises_1.2.0.1                                  
[133] latticeExtra_0.6-29                                R.utils_2.10.1                                    
[135] checkmate_2.0.0                                    openxlsx_4.2.4                                    
[137] nor1mix_1.3-0                                      cowplot_1.1.1                                     
[139] vioplot_0.3.7                                      siggenes_1.66.0                                   
[141] forcats_0.5.1                                      treemap_2.4-3                                     
[143] igraph_1.2.6                                       HDF5Array_1.20.0                                  
[145] survival_3.2-7                                     yaml_2.2.1                                        
[147] plotrix_3.8-1                                      Glimma_2.2.0                                      
[149] htmltools_0.5.2                                    memoise_2.0.0                                     
[151] BiocIO_1.2.0                                       locfit_1.5-9.4                                    
[153] graphlayouts_0.7.1                                 quadprog_1.5-8                                    
[155] viridisLite_0.4.0                                  digest_0.6.27                                     
[157] assertthat_0.2.1                                   mime_0.11                                         
[159] rappdirs_0.3.3                                     wesanderson_0.3.6                                 
[161] RSQLite_2.2.8                                      yulab.utils_0.0.2                                 
[163] remotes_2.4.0                                      data.table_1.14.0                                 
[165] blob_1.2.2                                         R.oo_1.24.0                                       
[167] preprocessCore_1.54.0                              splines_4.1.0                                     
[169] Formula_1.2-4                                      ggsci_2.9                                         
[171] Rhdf5lib_1.14.2                                    illuminaio_0.34.0                                 
[173] AnnotationHub_3.0.1                                ProtGenerics_1.24.0                               
[175] FlowSorted.Blood.450k_1.30.0                       RCurl_1.98-1.4                                    
[177] broom_0.7.9                                        hms_1.1.0                                         
[179] rhdf5_2.36.0                                       colorspace_2.0-2                                  
[181] base64enc_0.1-3                                    BiocManager_1.30.16                               
[183] Boruta_7.0.0                                       aplot_0.1.0                                       
[185] nnet_7.3-15                                        sass_0.4.0                                        
[187] GEOquery_2.60.0                                    Rcpp_1.0.7                                        
[189] mclust_5.4.7                                       mvtnorm_1.1-2                                     
[191] enrichplot_1.13.1.992                              fansi_0.5.0                                       
[193] ChIPseeker_1.29.1                                  R6_2.5.1                                          
[195] grid_4.1.0                                         lifecycle_1.0.0                                   
[197] zip_2.2.0                                          curl_4.3.2                                        
[199] DO.db_2.9                                          Matrix_1.3-2                                      
[201] qvalue_2.24.0                                      RColorBrewer_1.1-2                                
[203] iterators_1.0.13                                   stringr_1.4.0                                     
[205] IlluminaHumanMethylation450kmanifest_0.4.0         htmlwidgets_1.5.4                                 
[207] polyclip_1.10-0                                    biomaRt_2.48.3                                    
[209] purrr_0.3.4                                        shadowtext_0.0.8                                  
[211] gridGraphics_0.5-1                                 CMplot_3.6.2                                      
[213] openssl_1.4.5                                      htmlTable_2.2.1                                   
[215] patchwork_1.1.1                                    codetools_0.2-18                                  
[217] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.0 GO.db_3.13.0                                      
[219] gtools_3.9.2                                       prettyunits_1.1.1                                 
[221] dbplyr_2.1.1                                       gridBase_0.4-7                                    
[223] R.methodsS3_1.8.1                                  gtable_0.3.0                                      
[225] DBI_1.1.1                                          ggfun_0.0.3                                       
[227] httr_1.4.2                                         KernSmooth_2.23-18                                
[229] stringi_1.7.4                                      progress_1.2.2                                    
[231] reshape2_1.4.4                                     farver_2.1.0                                      
[233] annotate_1.70.0                                    viridis_0.6.1                                     
[235] rGREAT_1.24.0                                      ggtree_3.0.4                                      
[237] xml2_1.3.2                                         boot_1.3-26                                       
[239] sigFeature_1.10.0                                  restfulr_0.0.13                                   
[241] readr_1.4.0                                        geneplotter_1.70.0                                
[243] ggplotify_0.1.0                                    BiocVersion_3.13.1                                
[245] DESeq2_1.32.0                                      bit_4.0.4                                         
[247] scatterpie_0.1.7                                   jpeg_0.1-9                                        
[249] ggraph_2.0.5                                       annotatr_1.18.1                                   
[251] pkgconfig_2.0.3                                    knitr_1.34

Option to not run DMRichR::GOfuncR() in DM.R

DMRichR::GOfuncR is my preferred Gene Ontology (GO) approach; however, it is a low resource function that takes a long time and doesn't support parallel processing, which means including it in DM.R isn't ideal for some users given the way their resources allocated. The next version of DM.R will have option to not run this function. In the meantime, users can signficantly reduce their run time by removing this low resource step by deleting lines 670-688. This function can be run later on with a much smaller resource allocation (~3 cores and a few GB of ram), through load("RData/DMRs.RData") and then by running lines 620-696 of DM.R.

help for DMRrichR

Hi, ben:
In the R package of DMRrichR, it did not define parameter of compare direction. How the R package defiine the direction of difference combination of the output ?

Thank you！

sampleNames(bs) == as.character(meta$Name) are not all TRUE

processBismark(cores = 4, testCovariate = "Condition")

[DMRichR] Processing Bismark cytosine reports 23-09-2023 02:58:15 PM
Selecting files...
Reading cytosine reports...
[read.bismark] Parsing files and constructing valid loci ...
Done in 52.2 secs
[read.bismark] Parsing files and constructing 'M' and 'Cov' matrices ...
Done in 31.6 secs
[read.bismark] Constructing BSseq object ...
Assigning sample metadata with Condition as factor of interest...
Error in processBismark(cores = 4, testCovariate = "Condition") :
sampleNames(bs) == as.character(meta$Name) are not all TRUE

These are my files:

This is my sample_info.xlsx

I am on ubuntu 20.04 and using libreoffice calc. Could that be causing issues?

Confused about DMR annotation

Hi,

One of the DMRs we got is located at chr17:8243028-8243946. In the excel file, it has Open.Sea as "yes", annotation is equal to "Promoter" and distanceToTSS is "0". But the DMRs plot looks like:

It seems like this DMR is within the intron and far from TSS if we look at genome browser, this region seems to be outside of promoter:

Am I reading this wrong? How can distanceToTSS be 0 here? If it was 0, shouldn't the first plot contain the first exon?

REVIGO website update creating errors for DMRichR::REVIGO() and DMRichR::GOplot()

REVIGO has recently updated their website, which has temproarily broken DMRichR::REVIGO() and DMRichR::GOplot(). The fix for this isn't straightforward since their website now works very differently under the hood.

In order to prevent this issue from interfering with a DM.R run, some lines of code should be removed from DM.R: lines 655-662, 684-691, and 719-726. Additionally, the pipe operator %>% should be removed from lines: 654, 683, and 718.

I have contacted the developers of REVIGO and am hoping they will have some suggestions about how to access a mirror of their old site or see if it's possible to properly access the new one through R. If neither of those work out, I will implement a new method to slim GO terms (possibly the rrvgo package from Biocondcutor) in the next version of DMRichR, which should be released in the near future.

can this program be used for genome hg19？

HI,
I´m new working with DMRichR. I would lik to know if the DMRichR Suitable for reference hg19?
can I modify it to handle the Bismark cytosine reports with reference hg19？
Thanks for helping

For the coverage?

Hello,

Nice tools for BS-seq data analysis.

IMy question is can I use >4x CpG sites for DMR analysis ?

The best!

Can I use this package for RRBS data & issue with running DMRichR::DM.R

Hello,

Thank you for developing this convenient package for defining DMRs!

My first question is that if this package applies to RRBS data.

My second question is that I tried to run the following code:
DMRichR::DM.R(genome=c("mm10"),testCovariate = "Treatment", adjustCovariate = c("Exposure"), sexCheck = FALSE, cores = 20, coverage = 2, perGroup = 0.75, minCpGs = 5,cutoff = 0.05,EnsDb = FALSE, GOfuncR = TRUE,cellcompostion = FALSE).

I kept getting the error "Error in DMRichR::DM.R(genome = c("mm10"), testCovariate = "Treatment", : unused argument (cellcompostion = FALSE)".

Do you have any advice for how to resolve this error?

Thank you for your time!

MethylDackel implementation

Is it also possible to use MethylDacker files output instead of Bismark? https://github.com/dpryan79/MethylDackel
Maybe there is a way that DMRichR-compatible cytosine reports from methyldackel already?
In the Bismark cytosine report file, you have the following columns: "chromosome, position, strand, count methylated, count non-methylated, C-context, trinucleotide context". Methyldackel bedGraph file already contains columns 1, 2, 3 and 4.

By the way one of the link is not active in the repro: https://github.com/FelixKrueger/Bismark/tree/master/Docs#optional-genome-wide-cytosine-report-output

Error in enrichment testing

Dear author,

I got an error during enrichment test.

R code :

DMRichR::DM.R(genome=c("hg38"),testCovariate = "Diagnosis", adjustCovariate = c("Sex","Age"),
              sexCheck = TRUE, cores = 20, coverage = 6, perGroup =  0.8, minCpGs =  5,
              maxPerms =  10, maxBlockPerms = 10,cutoff = 0.05,EnsDb = FALSE,
              cellComposition = FALSE,GOfuncR = TRUE)

and the error :

[DMRichR] Performing gene ontology analyses 			 29-10-2021 04:41:21 AM 
Running GREAT
Saving and plotting GREAT results
The default enrichment tables contain no associated genes for the input
regions. You can set `download_by = 'tsv'` to download the complete
table, but note only the top 500 regions can be retreived. See the
following link:

https://great-help.atlassian.net/wiki/spaces/GREAT/pages/655401/Export#Export-GlobalExport
Submiting results from rGREAT to rrvgo...
rrvgo is now slimming BP GO terms from rGREAT
preparing gene to GO mapping data...
preparing IC data...
There are 158 clusters in your GO BP terms from rGREAT
rrvgo is now slimming CC GO terms from rGREAT
preparing gene to GO mapping data...
preparing IC data...
There are 46 clusters in your GO CC terms from rGREAT
rrvgo is now slimming MF GO terms from rGREAT
preparing gene to GO mapping data...
preparing IC data...
There are 57 clusters in your GO MF terms from rGREAT
Joining, by = c("Gene Ontology", "go")
Running GOfuncR
Selecting annotation databases...
Obtaining UCSC gene annotations...
  1613 genes were dropped because they have exons located on both strands
  of the same reference sequence or on more than one reference sequence,
  so cannot be represented by a single genomic range.
  Use 'single.strand.genes.only=FALSE' to get all the genes in a
  GRangesList object, or use suppressMessages() to suppress this message.
25713 unique genes will be utilized for GOfuncR...
Performing enrichment testing...
Error in check_regions(genes, circ_chrom) : 
  No background region for chromosomes: chrM.
  With circ_chrom=TRUE only background regions on the same chromosome as a candidate region are used.

I think that change of some arg can solve this problem.

Error selecting significant DMRs: "Error in beta/pi : non-numeric argument to binary operator"

Hey, ran into this bug with a job -- after finishing testing for DMRs with dmrseq (about 10 days of runtime :/ ), the execution crashes and run isn't saved.

Running DMRichR 1.7.1 in R 4.0.5.

Here's the crash:

...
* 8 out of 8 permutations completed (344616 null candidates)
Selecting significant DMRs...
Error in beta/pi : non-numeric argument to binary operator
Calls:  ... mutate.Ranges -> mutate_rng -> overscope_eval_update -> eval_tidy
In addition: Warning messages:
1: In parallel::mccollect(wait = FALSE, timeout = 1) :
  1 parallel job did not deliver a result
2: In parallel::mccollect(wait = TRUE) :
  9 parallel jobs did not deliver results
Execution halted

Error in CMplot: :CMplot

Hi,

I get the following error when running the workflow:

Was the analysis interrupted by the error, or was it ignored? I mean, would I get all the results regardless (I don't know what to expect in the outputs since I'm running it for the first time)?

Thanks in advance!

EDIT:

I don't get any of the enrichment plots, so I guess the program finishes prematurely.

Problem in testing for blocks of differential methylation

Hello,
I am having some troubles with the function dmrseq in your code when it comes to create the blocks. It got stuck on this function for 11 hours. I tried to increase the number of cores and the number of chrsPerChunks, but this does not change the situation. Do you have any suggestions?
This is the error:

Searching for large scale blocks with at least 5000 basepairs.
Assuming the test covariate Diagnosis is a factor.
Condition: cancer vs healthy
Parallelizing using 50 workers/cores (backend: BiocParallel:MulticoreParam).
Computing on 1 chromosome(s) at a time.

Detecting candidate regions with coefficient larger than 0.05 in magnitude.
...Chromosome 1: Error in mcexit(0L) : ignoring SIGPIPE signal
Calls: dmrseq ... bploop.lapply -> .send_to -> .send_to -> -> mcexit
Error in mcexit(0L) : ignoring SIGPIPE signal
Calls: dmrseq ... bploop.lapply -> .send_to -> .send_to -> -> mcexit
Smoothed (0.25 min)

Thank you for your time!

Error with read.bismark ?

Hi Ben,

I'm having trouble with the very beginning of your Pipeline. When DMRichR is calling read.bismark() I am getting an error message that files cannot contain duplicate lines. ("'files' cannot have duplicate entries.").

I didn't use CpG_Me to generate the Cytosine Reports but just took my dedup'ed bam files from Bismark and generated them manually. Whats odd is that I can load in my Cytosine reports by calling read.bismark() separately. I'm wondering if theres then something in the current version of the script that is causing this issue?

Maybe something related to this (#1) ?

Thanks for any suggestions!

ensembldb option

In order to use ensembldb, you need to install the latest version of ChIPseeker, which hasn't yet made it to Biocondcutor. You can do this through: BiocManager::install("YuLab-SMU/ChIPseeker")

Install R package issue

Hi,

Thanks for this work!
I want to install your R package but I have some issue when it launch the install of the 'sm' R package as you can below:

** R
** inst
** byte-compile and prepare package for lazy loading
Error: package or namespace load failed for ‘sm’:
 .onLoad a échoué dans loadNamespace() pour 'tcltk', détails :
  appel : fun(libname, pkgname)
  erreur : (converti depuis l'avis) couldn't connect to display "localhost:10.0"
Erreur : le package ‘sm’ ne peut être chargé
Exécution arrêtée
ERROR: lazy loading failed for package ‘GOfuncR’
* removing ‘/opt/share/FLOCAD/userspace/cpichot/miniconda3/envs/DMRichR/lib/R/library/GOfuncR’
Erreur : Failed to install 'DMRichR' from GitHub:
  (converti depuis l'avis) installation of package ‘GOfuncR’ had non-zero exit status

but when I try just to install 'sm' only it work but with the same issue as warning :

> BiocManager::install(c("sm"))
Bioconductor version 3.11 (BiocManager 1.30.10), R 4.0.1 (2020-06-06)
Installing package(s) 'sm'
essai de l'URL 'https://cran.rstudio.com/src/contrib/sm_2.2-5.6.tar.gz'
Content type 'application/x-gzip' length 256355 bytes (250 KB)
==================================================
downloaded 250 KB

* installing *source* package ‘sm’ ...
** package ‘sm’ correctement décompressé et sommes MD5 vérifiées
** using staged installation
** libs
...
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
Warning in fun(libname, pkgname) :
  couldn't connect to display "localhost:10.0"
** help
*** installing help indices
** building package indices
** testing if installed package can be loaded from temporary location
Warning in fun(libname, pkgname) :
  couldn't connect to display "localhost:10.0"
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
Warning in fun(libname, pkgname) :
  couldn't connect to display "localhost:10.0"
** testing if installed package keeps a record of temporary installation path
* DONE (sm)

The downloaded source packages are in
	‘/tmp/RtmprNURcs/downloaded_packages’
Updating HTML index of packages in '.Library'
Making 'packages.html' ... done
>

So I have install the package with conda-forge channel in conda environment, but when I try to install you R package I have the same issue.

Do you have any suggestion please ?

Thanks in advance

An error showed "'error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': 'mc.cores' > 1 is not supported on Windows"

I am using a Windows system but I get the message "mc.cores is not supported on Windows"
Can I bypass this issue?
thank you for the help

DMRichR::DM.R (genome = "hg38",

```
           maxPerms = 7, 
```
```
           maxBlockPerms = 7, 
```
```
           sexCheck = TRUE,
```
```
           adjustCovariate = "Age",
```

           testCovariate = "Diagnosis")

genome = hg38
coverage = 1
perGroup = 0.75
minCpGs = 5
maxPerms = 7
maxBlockPerms = 7
cutoff = 0.05
testCovariate = Diagnosis
adjustCovariate = Age
cores = 20
sexCheck = TRUE
EnsDb = FALSE
GOfuncR = TRUE

[DMRichR] Selecting annotation databases 10-12-2023 上午 08:50:23
Loading BSgenome.Hsapiens.UCSC.hg38.masked
Loading TxDb.Hsapiens.UCSC.hg38.knownGene
Loading org.Hs.eg.db
Saving Rdata...

[DMRichR] Processing Bismark cytosine reports 10-12-2023 上午 08:50:23
Selecting files...
Reading cytosine reports...
[read.bismark] Parsing files and constructing valid loci ...
Done in 1422.8 secs
[read.bismark] Parsing files and constructing 'M' and 'Cov' matrices ...
Done in 1843 secs
[read.bismark] Constructing BSseq object ...
Assigning sample metadata with Diagnosis as factor of interest...
DataFrame with 7 rows and 3 columns
Diagnosis Age Sex

sample1MQA1.CpG Disease 21 F
sample2MQA3.CpG Disease 22 F
sample3MQA5.CpG Disease 20 F
sample4MQA7.CpG Disease 22 F
sample5MQA4.CpG Control 22 F
sample6MQA6.CpG Control 20 F
sample7MQA8.CpG Control 22 F
Checking sex of samples...
Sex of samples match correctly as all male or all female.
Filtering CpGs for Diagnosis...
Assuming adjustment covariate Age is continuous and excluding it from filtering...
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': 'mc.cores' > 1 is not supported on Windows

error Block analysis has produced an error

Hello!
This is my run:
call="Rscript
--vanilla
/home/aleksa120396/miniconda3/envs/r4/lib/R/library/DMRichR/exec/DM.R
--genome hg38
--coverage 2
--perGroup '0.75'
--minCpGs 5
--maxPerms 10
--maxBlockPerms 10
--cutoff '0.05'
--testCovariate Diagnosis
--sexCheck FALSE
--GOfuncR FALSE
--EnsDb FALSE
--cores 65"

I had a permanent error with the chrM - "...Chromosome chrM: Warning: Block analysis has produced an error". I removed chrM from the data, only 22+X+Y in my data. The error was repeated, but with a different chromosome:

Beginning permutation 1
...Chromosome chr1: Warning: Block analysis has produced an error

<...>

Beginning permutation 1
...Chromosome chr1: Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 't': singular matrix in 'backsolve'. First zero in diagonal [3]
Calls: <Anonymous> ... t -> tcrossprod -> backsolve -> .handleSimpleError -> h
In addition: Warning message:
In dir.create("RData") : 'RData' already exists
Execution halted
53275

what is the problem?
and a small question: why is no intermediate data saved?..

Error in createCommonDmrsHeatmap / hclust

Thanks for the awesome software!

In the dataset I'm trying this out on, I'm getting this crash in createCommonDmrsHeatmap

Running this with roughly:
Rscript --vanilla exec/DM.R --genome mm10 --coverage 10 --minCpGs 5 --maxPerms 8 --maxBlockPerms 8 --cutoff '0.1' --testCovariate Diagnosis --sexCheck FALSE --ensembl FALSE --cores 32 --GOfuncR TRUE --cellComposition FALSE

Error:

[DMRichR] Learning features of DMRs for Diagnosis 01-04-2021 10:05:29 PM
Training random forest (RF) model for DMR ranking...Done.
Training support vector machine (SVM) model for DMR ranking...Done.
1% of 769 total DMRs is 8 and less than 10.
Finding common DMRs in top 10 DMRs (instead of in top 1%) in RF and SVM predictor ranking lists.
Beginning DMR annotation...
... preparing features information... 2021-04-01 10:05:41 PM
... identifying nearest features... 2021-04-01 10:05:41 PM
... calculating distance from peak to TSS... 2021-04-01 10:05:42 PM
... assigning genomic annotation... 2021-04-01 10:05:42 PM
... adding gene annotation... 2021-04-01 10:05:44 PM
'select()' returned 1:1 mapping between keys and columns
... assigning chromosome lengths 2021-04-01 10:05:44 PM
... done... 2021-04-01 10:05:44 PM
Generating HTML report...
Beginning DMR annotation...
... preparing features information... 2021-04-01 10:05:44 PM
... identifying nearest features... 2021-04-01 10:05:44 PM
... calculating distance from peak to TSS... 2021-04-01 10:05:44 PM
... assigning genomic annotation... 2021-04-01 10:05:44 PM
... adding gene annotation... 2021-04-01 10:05:46 PM
'select()' returned 1:1 mapping between keys and columns
... assigning chromosome lengths 2021-04-01 10:05:46 PM
... done... 2021-04-01 10:05:47 PM
Beginning DMR annotation...
... preparing features information... 2021-04-01 10:05:47 PM
... identifying nearest features... 2021-04-01 10:05:47 PM
... calculating distance from peak to TSS... 2021-04-01 10:05:47 PM
... assigning genomic annotation... 2021-04-01 10:05:47 PM
... adding gene annotation... 2021-04-01 10:05:49 PM
'select()' returned 1:1 mapping between keys and columns
... assigning chromosome lengths 2021-04-01 10:05:49 PM
... done... 2021-04-01 10:05:49 PM
Error in hclust(d, method = method) : must have n >= 2 objects to cluster
Calls: ... createCommonDmrsHeatmap -> -> cluster_mat -> hclust
In addition: There were 17 warnings (use warnings() to see them)
Execution halted

cavPor genome

Dear Ben,

Thank you very much for the great package.
I would like to use DMRichR for Guinea pig RRBS dataset, however I can not find the genome in BSgenome database. I was wondering if there is a way to add the cavPor3 genome to DMRichR?
Thank you!

How do I point to files?

Hi!

Thanks for the package! I can't figure out how to point to the files though. I've ran nf-core/methylseq pipeline and that outputs coverage2cytosine Bismark results like these:

Here's how sample_info.xlsx looks like:

I change the working directory of the R session to folder with these files and run:
DM.R(genome = "mm10", testCovariate = "Age", cores = 8)
However, there's an error:

[DMRichR] Processing Bismark cytosine reports            19-09-2023 09:23:47 PM
Selecting files... 
Reading cytosine reports...    
Error in bsseq::read.bismark(files = files, rmZeroCov = FALSE, strandCollapse = TRUE,  : 
These files cannot be found: 
NA

What am I missing?

DMRichR::annotationDatabases()

Hi Ben,
We needed to make one custom genome that was missing for the tool.

In DMRichR, all the necessary packages and dependencies are being installed when the following command is being executed:
BiocManager::install("ben-laufer/DMRichR") and all subsequent calls of functions are done as such: e.g. DMRichR::annotationDatabases(). So we imagine that it is referring every time to DMRichR that was installed by BiocManager. If so, then when we edit the code lines of annotationDatabases.R for the custom genome, how do we tell DMRichR to read the file we edited, and not the file it downloaded from BiocManager? Kindly let us know if we explained it well enough. Maybe a small description could be great to add. Thank you,

Best wishes,
Marcin & Wassim