my data (typeof S4) is look like:
> rowData(ds_res1$res)
DataFrame with 144 rows and 4 columns
cluster_id marker_id p_val p_adj
<factor> <factor> <numeric> <numeric>
B cells B cells OXO40 0.831005 0.996855
Activated Monocytes Activated Monocytes OXO40 0.664675 0.996855
NK cells CD56neg NK cells CD56neg OXO40 0.759837 0.996855
Unconventional monocytes Unconventional monocytes OXO40 0.418055 0.996855
NK cells NK cells OXO40 0.709280 0.996855
... ... ... ... ...
Naïve CD4 Naïve CD4 TIGIT NA NA
Memory CD9 Memory CD9 TIGIT NA NA
gdT cells gdT cells TIGIT NA NA
Undefined3 Undefined3 TIGIT NA NA
Monocytes Monocytes TIGIT 0.971238 0.996855
I am trying to convert the NA values in the dataframe to 0 and I have the below errors with two different approaches:
> rowData(ds_res1$res)[is.na(rowData(ds_res1$res))] <- 0
Error in mergeROWS(x, i, value) : appending gaps is not supported
In addition: Warning messages:
1: In NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
subscript is an array, passing it thru as.vector() first
2: In NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
subscript is an array, passing it thru as.vector() first
> rowData(ds_res1$res)[is.na(rowData(ds_res1$res)),] <- 0
Error in normarg_mcols(value, class(x), length(x)) :
trying to set metadata columns of length 575 on an object of length 144
In addition: Warning messages:
1: In NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
subscript is an array, passing it thru as.vector() first
2: In NSBS(i, x, exact = exact, strict.upper.bound = !allow.append, :
subscript is an array, passing it thru as.vector() first
3: In `[<-.factor`(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, :
invalid factor level, NA generated
4: In `[<-.factor`(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, :
invalid factor level, NA generated
Not knowing what rowData is or how it is defined, my guess is that it does not have a LHS assignment companion function `rowData<-`. (For example, rownames(dat) has `rownames<-` so that we can do rownames(dat) <- c("a","quux").)
In this case, if you want to fix NAs in the data returned from your rowData(..) call, then
dat <- rowData(ds_res1$res)
dat[] <- lapply(dat, function(z) { z[is.na(z)] <- 0; z; })
While there's no hint that you're using dplyr, if you were then the canonical function is dplyr::coalesce, which can be used as:
dat[] <- lapply(dat, dplyr::coalesce, 0)