yanailab / enhance Goto Github PK

Hi,

First of all, thank you for you package and for the great manuscript that is linked to it!
I'm trying to run Enhance on different datasets and some of them returned an error:

denois <- enhance (data)
[1] "Calculating number of neighbors to aggregate to aim for 2e+05 transcripts"
[1] "Number of neighbors to aggregate: 1"
[1] "Number of principal components to use: 50"

 Error in base::rowSums(x, na.rm = na.rm, dims = dims, ...) : 
  'x' must be an array of at least two dimensions
 
10.stop("'x' must be an array of at least two dimensions")
 
9.base::rowSums(x, na.rm = na.rm, dims = dims, ...)
 
8.rowSums(D[, indices])
 
7.rowSums(D[, indices]) at
 enhance.R#147
6.FUN(X[[i]], ...)
 
5.lapply(X = X, FUN = FUN, ...)
 
4.sapply(nn, function(indices) {
    rowSums(D[, indices])
})
 
3.sapply(nn, function(indices) {
    rowSums(D[, indices])
}) at
 enhance.R#146
2.aggregate_nearest_neighbors(D = data_raw, nn = nn_1) at
 enhance.R#223

1.enhance(data)
 

Strangely, I only got this error message with my bigger datasets (>1e+06 transcripts per cells). I am surprised that k_nn estimation is 1 and I guess it is the source of the error.

Looking at the code I see that k_nn, the number of neighbors to aggregate, is defined as

  med_raw = median(colSums(data_raw))
  k_nn = ceiling(target_transcripts / med_raw)

Maybe I miss an important point but shouldn't be k_nn equal to ceiling(med_raw / target_transcripts ) instead, so that the number of neighbors to aggregate increases with the number of transcripts per cells ?

In every cases, do you know how I could avoid an error when k_nn = 1 ? Should I increase k_nn to 2 or does it mean that the dataset is too big/small for the method to work ?

[EDIT]
Sorry, I posted this issue in the Python repository instead of the R one. However the calculation of knn is the same and I guess my questions are still relevant here

k = int(ceil(target_transcript_count / transcript_count))

Thanks,
Anthony

Really interesting work. I'm just wondering about the labels in the figure of the Readme. Shouldn't "Raw data" and "ENHANCE" be written on the side rather than on top?

And since I'm here: Am I right to assume that the output of ENHANCE won't be a sparse matrix anymore?

ENHANCE is so fantastically simply and fast that I had to try it immediately.

Since I'm using the Seurat for most of my analyses, I used it to see the impact of ENHANCE on cluster and dimensional reduction. After using ENHANCE, I find many more clusters and noticed some strange looking shapes in the UMAP. I'm not sure I understood the procedure you described in the supplementary methods, but could it be that normalizing, PCA transformation and scaling the data introduces too smoothing (or whatever you might call it) which leads to these shapes?

Here is the code and UMAP made with the raw counts:

seurat_raw <- CreateSeuratObject(raw_sparse)
seurat_raw <- NormalizeData(seurat_raw)
seurat_raw <- FindVariableFeatures(seurat_raw)
seurat_raw <- ScaleData(seurat_raw)
seurat_raw <- RunPCA(seurat_raw)
seurat_raw <- FindNeighbors(seurat_raw)
seurat_raw <- FindClusters(seurat_raw)
seurat_raw <- RunUMAP(seurat_raw, dims = 1:30)

Here the code and UMAP after using ENHANCE:

seurat_enhance <- CreateSeuratObject(enhance_sparse)
seurat_enhance <- NormalizeData(seurat_enhance)
seurat_enhance <- FindVariableFeatures(seurat_enhance)
seurat_enhance <- ScaleData(seurat_enhance)
seurat_enhance <- RunPCA(seurat_enhance)
seurat_enhance <- FindNeighbors(seurat_enhance)
seurat_enhance <- FindClusters(seurat_enhance)
seurat_enhance <- RunUMAP(seurat_enhance, dims = 1:30)

I'm using R 3.5.2 and Seurat v3 but I don't think that matters a lot.