fedeago / newwave Goto Github PK

Hello,

I saw that zinbwave::getMu becomes NewWave::newMu, so I was wondering if there is an equivalent to zinbwave::getPi. NewWave::newPi does not seems to be exported.

Thanks in advance!

Dear Federico,
Following up on a similar memory issue as the one already posted by other user, I followed your recommendations on creating a DelayedArray class for my SCE assay (14k genes x 227k cells):

My SCE object

A1Bmtx_sce
class: SingleCellExperiment
dim: 13966 271111
metadata(0):
assays(1): counts
rownames(13966): LINC00115 NOC2L ... MT-ND6 MT-CYB
rowData names(0):
colnames(271111): SRR7666705 SRR7666706 ... TTTGTCAAGCTGAACG.1
TTTGTCACAATCGGTT.1
colData names(3): cells batch Biological_Condition

Transforming the "batch" field from colData to a factor

colData(A1Bmtx_sce)$batch <- as.factor(colData(A1Bmtx_sce)$batch)

Converting the assay (counts) to a DelayedArray

library(DelayedArray)
assay(A1Bmtx_sce) = DelayedArray(assay(A1Bmtx_sce))
class(assay(A1Bmtx_sce))
[1] "DelayedMatrix"
attr(,"package")
[1] "DelayedArray"

Running newWave

A1Bzinb <- newWave(A1Bmtx_sce, K=9, X="~batch", children=24, n_gene_par=1500, n_cell_par=30000, verbose=FALSE, commondispersion=FALSE)

I submitted this code as job script to a large-memory node (768 Gb) from my cluster, and it stopped after ~30 min with the SGE exit status #37: "failed 37 : qmaster enforced h_rt, h_cpu, or h_vmem limit", reaching a maxvmem of 487 Gb, while I've asked for 720Gb.
I've also got info from my IT regarding intermediate files ( 'sharedObjectCounter', 'SO_X64_1', 'SO_X64_2', ... 'SO_X64_24') left in the /dev/shm due to the unfinished status of my newWave run.

I wonder if you would have any tip to circumvent this issue, and please let me know if you need more info.

Many thanks in advance.
Elton

Hi there,

Thank you for releasing the code for your algorithm. I have a question regarding the memory-efficiency.

My cell-gene matrix is approximately 600,000 cells x 6,000 genes. Assuming 32-bit floats, this should be around 15gb.
Despite this, I am running out of memory despite having 128gb RAM. How can I run this algorithm memory-efficient?
The command i'm using so far is:

res <- newWave(sce,X = "~site.Site", K=10, verbose = FALSE, children=6, n_gene_disp = 100, n_gene_par = 100, n_cell_par = 100)

Many thanks