Hi I'm just following the "getting started" tutorial and get stuck at this very beginning

Error in Create_redeemR(VariantsGTSummary): object 'meanCov' not found
Traceback:

1. Create_redeemR(VariantsGTSummary)
2. subset(attr(VariantsGTSummary, "depth")[["Cell.MeanCov"]], meanCov >=
   . qualifiedCellCut)
3. subset.default(attr(VariantsGTSummary, "depth")[["Cell.MeanCov"]],
   . meanCov >= qualifiedCellCut)

code was just

devtools::install_github("chenweng1991/redeemR")
library(redeemR)
library(ggplot2)
library(dplyr)
dir="Young1.T1.HSC.Consensus.final"
VariantsGTSummary <- redeemR.read(path=dir,thr="S",Processed=T)
example_redeemR<-Create_redeemR(VariantsGTSummary)

the VariantsGTSummary produced have the $Sensitive etc fields, but example_redeemR<-Create_redeemR(VariantsGTSummary$Sensitive) does not work either
seems to be issue with the line CellMeta<-subset(attr(VariantsGTSummary,"depth")[["Cell.MeanCov"]],meanCov>=qualifiedCellCut)
and attr(VariantsGTSummary,"depth") is NULL

Would appreciate some help
thanks in advance

hello there! thanks for making all the packages and notebooks.
I was looking at the mtSNVs in one of the rds files downloaded from here https://doi.org/10.6084/m9.figshare.23290004 and trying to do the same filtering as described in the nature paper. However after my filtering there are ~ 43k mismatches left, a number much higher than what you stated in the paper (~3 or 4k mt mutations). Could you please point me to the actual code or maybe a more specific description about how you processed the mutation calls? thank you very much!

The Cell barcode not matched between Seurat object and redeemR object is expected, because:
the Seurat object derived from CellRanger output report RNA barcode, while the redeem reports the ATAC barcode. There is an easy way to translate the barcode from both sides. I provided a function Translate_simple_RNA2ATAC and Translate_simple_ATAC2RNA in the package redeemR. These two function input a vector of cell barcode and from one and translate to the other. The source information is here 10X document The RNA and ATAC barcodes are line-by-line corresponding to each other in the two files provided.

Hi! Chen, @chenweng1991 ,
My goal is to make a mitoTracing data to creat Phylogenetic tree. Something went wrong when I tried to add "depthMatrix" for my mitoTracing object, The error code is as followed:

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 'as.matrix': dims [product 1] do not match the length of object [0]

And I tried to find the reason cause this problem, I checked the underlying code of function "Add_DepthMatrix" in "Build Tree.R":

setMethod(f="Add_DepthMatrix",
          signature="redeemR",
         definition=function(object,QualifiedTotalCts=""){
          require(reshape2)
          message("Remember to update for combined object")
          if(length(QualifiedTotalCts)==0){
          QualifiedTotalCts<-read.table(paste(object@attr$path,"/QualifiedTotalCts",sep=""))
          }
          colnames(QualifiedTotalCts)<-c("Cell","Pos","T","LS","S","VS")
          Dic<-gsub("Variants","",colnames([email protected])) %>% substr(.,1,nchar(.)-2) %>% as.integer %>% data.frame(Variants=colnames([email protected]),Pos=.)
          QualifiedTotalCts.subset<-subset(QualifiedTotalCts,Cell %in% row.names([email protected])) %>% merge(.,Dic,by="Pos") %>% .[,c("Cell","Variants",object@para["Threhold"])]
          DepthMatrix<-dcast(QualifiedTotalCts.subset,Cell~Variants) %>% tibble::column_to_rownames("Cell") %>% as.matrix
          if (all(dim([email protected])==dim(DepthMatrix))){
             [email protected]<-DepthMatrix[row.names([email protected]),colnames([email protected])]
          }else{
              print(dim([email protected]))
              print(dim(DepthMatrix))
              print("Check the input QualifiedTotalCts, the dimension cannot match")
          }
          return(object)
          })

I found the mistake is came from "QualifiedTotalCts.subset<-subset(QualifiedTotalCts,Cell %in% row.names([email protected])", My "QualifiedTotalCts" have no intersection with “[email protected]”.

My "QualifiedTotalCts" object and "mitoTracing" object is created by the data from the folder "Young1.T1. BMMC.Consensus.final" you shared on the website:"https://doi.org/10.6084/m9.figshare.24418966.v1"

I have no idea about why these two objects have no intersection. Could you tell me how to fix this problem?
Any advice is welcome. Thank you very much.

Hi! Chen
Thank you for sharing this repo! But when I 'm following the steps to produce "mitoTracing.sensitive" dataset on line 95,96,97 of the file "vignettes.md"(its position is_'redeemR/vignettes/vignettes.md_). There are some errors about three functions in redeemR package.
Like this:

> `Young1_HPC_mitoTracing.Sensitive<-Make_matrix(Young1_HPC_mitoTracing.Sensitive)`
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘Make_matrix’ for signature ‘"mitoTracing"’
> Young1_HPC_mitoTracing.Sensitive<-SeuratLSIClustering(Young1_HPC_mitoTracing.Sensitive)
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘SeuratLSIClustering’ for signature ‘"mitoTracing"’
> Young1_HPC_mitoTracing.Sensitive<-AddDatatoplot_clustering(Young1_HPC_mitoTracing.Sensitive)
Error in (function (classes, fdef, mtable)  : 
  unable to find an inherited method for function ‘AddDatatoplot_clustering’ for signature ‘"mitoTracing"’

And I have tried to use these three functions by putting "redeemR::" in front of each function, I also tried reinstall this R package, but nothing changed.
I still have no useful methods to solve this problem. Any useful advises would be appreciated!
Thank you!

I was interested in adding this to Bioconda. However, I notice that SCAVENGE has been removed as dependency since the last Release. Are there plans to make another Release soon (e.g. v1.1)? If so, this would be useful so I would not also have to add SCAVENGE to Bioconda.

Hi Chen,

I don't have priors for the recommended weighted Jaccard distance for my tumor sample, so I tried LSI or plain Jaccard distance to build the tree. Based on the results below, which distance metrics would you recommend moving forward with?

Based on LSI distance:

Based on Jaccard distance:

Also, I saw that in the source code of the SeuratLSIClustering function, there is an argument rmvariants=c("Variants310TC", "Variants3109TC", "Variants5764CT").
Could you please explain why we exclude these variants and if we should use the same list for other tissue types such as tumors?

Thank you,
Li

Hello! I'm interested in merging multiple sample data from the same individual into a single tree. using RedeemR.
Could you please provide guidance on how to achieve this?
Thanks a lot!