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To end up with a single table made by merging all your tables, you can give a try with reduce() of purrr: The first argument .x would be the list of tables to merge (in your case the annotation file …

There are actually two questions. The first one is about the memory issue. I believe merge from data.table would solve that issue. The second one would be aggregating or summarizing the identifiers. …

You can create a vector consisting of values that you would like to keep (or discard): to_keep <- paste0("chr", seq(1,22)) > to_keep [1] "chr1" "chr2" "chr3" "chr4" "chr5" "chr6" "chr7" "chr …

custom.cn.data$new_column <- ifelse(custom.cn.data$CN < 0, "Del", "Amp") In this scenario CN == 0 would be Amp

EDIT: Wheres the OP accepted this answer, @zx8754's answer below, as well as the ones in the link they gave, are much more elegant and straightforward. Here is a work-around with table(), lapply() …

If you do ?devtools::install, you will see that the install() function from the devtools package, not surprisingly, requires a package to be specified: install(pkg = ".", reload = TRUE, quick = FALSE …

You could give a try with dplyr's full_join() and purrr's reduce(). full_join() will make sure that you won't lose any data and reduce() will merge tables in your list from left to right, left being t …

sub() is the way to go. Your pattern is your regular expression: Check for "_vs_" followed by any character (provided with .) for an undefined length (provided with *). my_text <- "LP2000107-DNA_A01 …

Type of data you need depends on the downstream applications and since you would like to carry out DEA with DESeq2, you would need raw counts (non-normalized). There are many ways to import/download T …

One option is to use one of the apply family functions. If you enclose your code above (which generates errors by the way) in a function, you can then "apply" this function on the rows or columns of y …

You are trying to "map" mutation data to your heatmap, however, there is no mutation data in your code as far as I can see! You specify sample names within a data frame called anno but this data frame …

Seurat is expecting individual datasets to be normalized separately prior to data integration. In that respect lists of objects corresponding to different datasets are handy to manipulate each object/ …

I think you forgot to load (or install) the doParallel package as the error warning message is complaining about the lack of a parallel backend. library("doParallel") should do the trick.

You can use ifelse(): anno_maf$COSMIC <- ifelse(anno_maf$Hugo_Symbol %in% your_gene_list, yes = "yes", no = "no")

The upper part, where you try to use lapply() over your list of files with your custom function does not quite make sense to me: You declare your files but you do not use this in your lapply() call. I …