Provided normalized function about alra HOT 4 CLOSED

Got it.

Just run imputation on the subset of rows that are non-zero and then set the appropriate rows of the larger matrix to the result of imputation on the subset.

For example, something like this:

# Make a matrix that has a row of all zeros
A <- matrix(1,nrow=10,ncol = 5)
A[2,] <- 0 
print(A)

totalUMIPerCell <- rowSums(A)
toKeep <- which(totalUMIPerCell >0)
A_subset <- A[toKeep, ]

# Run imputation on A_subset. Here I'll just add the number 1, just for demo
A_subset <- A_subset + 1

A[toKeep, ]  <- A_subset
print(A)

from alra.

Not sure I fully understand your question. Are you saying the normalize_data() function is too slow for large datasets?

Or are you saying that after you run normalize_data() and you want to merge the resulting matrix back into the original matrix, you do this via a loop that is very slow?

from alra.

Not sure I fully understand your question. Are you saying the normalize_data() function is too slow for large datasets?

Or are you saying that after you run normalize_data() and you want to merge the resulting matrix back into the original matrix, you do this via a loop that is very slow?

What I mean is that, when normalizing with this function. every gene that has 0 count in every cell will be removed in if (any(totalUMIPerCell == 0)) condition

For example, I use data set that have 20,000 genes but only 3,000 genes express with at least one cell. after using provided normalized function, 17,000 genes will be removed.

I hope to know the way that efficiently merge imputation result of 3,000 genes back to original data. (20,000 genes with zero count)

from alra.

That's helpful, Thanks.

from alra.