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1. In the RaceID2 algorithm, is there a possibility to account for mitochondrial genes (whose high counts often indicate cell death)? Or is it even necessary for this algorithm?
2. Is there a possibility to enhance the boundaries of my clusters on the tsne plots of the RaceID2 algorithm? With RaceID2 I am able to see the two clusters previously identified with Seurat but I wish to see a clearer demarcation on the tsne plot.
3. Markers of my fibrotic cluster such as Il1rl1 show an overlap between the two clusters when I try to visualize the expression of this gene with RaceID2. The overlap is so strong that I can almost not differentiate between the two clusters again. Is there a possibility for me to improve this? In Seurat the demarcations are somehow clearer and I wish to have a comparably similar feature on RaceID2 for publication purposes.

Hi,
This was my data:
scRNAseq data：42 cells x 12000 genes；
vset included two vectors : gCC(24 genes) and gCP(116 genes);

When I run the command:

CCcorrect(sc@fdata,vset=vset,CGenes=NULL,ccor=.4,nComp=NULL,pvalue=.05,quant=.01,mode="pca")
the error occurred:

Traceback：
3. stop("all entries of 'x' must be nonnegative and finite") 
2. fisher.test(matrix(c(ncol(y) - length(nq), length(nq), length(m) - 
   length(nqm), length(nqm)), ncol = 2), alternative = "g") at E:\20180122desktop\FateID\RaceID3_StemID2-master\RaceID3_StemID2_class.R#1023
1. CCcorrect(sc@fdata, vset = vset, CGenes = NULL, ccor = 0.4, nComp = NULL, 
   pvalue = 0.05, quant = 0.01, mode = "pca") 

This error may be related with fisher.test in the CCcorrect function. Can you tell me how can I solve this problem?

I'm Looking forward to your reply.

Dear authors,

I am running ascend function CCcorrect with "ica" dimension reduction as follows:
scs <- SCseq(data.frame(data.matrix))
scs <- RaceID::filterdata(scs, minexpr = 5, minnumber = 1)
RaceID::CCcorrect(scs, mode="ica", dimR=TRUE)

and I am getting the following error:

Error in dimnames(x) <- dn :
length of 'dimnames' [2] not equal to array extent

With PCA the error doesn't occur. Do you have some suggestion what can be wrong?

Best,
Monika

Hello I wish to cluster my single cell 10x data using RaceID3. However, I cannot load my 10x data into RaceID using their function SCseq

10x gave me 3 files:

1. barcodes.tsv.gz
2. features.tsv.gz
3. matrix.mtx.gz

I tried using the Matrix package in R as given on the 10x website.

library(Matrix)
matrix_dir = "C:/Users/s/Downloads/"
barcode.path <- paste0(matrix_dir, "barcodes.tsv.gz")
features.path <- paste0(matrix_dir, "features.tsv.gz")
matrix.path <- paste0(matrix_dir, "matrix.mtx.gz")
mat <- readMM(file = matrix.path)
feature.names = read.delim(features.path, 
                       header = FALSE,
                       stringsAsFactors = FALSE)
barcode.names = read.delim(barcode.path, 
                       header = FALSE,
                       stringsAsFactors = FALSE)
colnames(mat) = barcode.names$V1
rownames(mat) = feature.names$V1

And it fails to allocate a huge amount of memory

> sc <- SCseq(mat)
Error: cannot allocate vector of size 6823.1 Gb

I also tried using Seurat's Read10X function :

library(Seurat)
library(RaceID)
pbmc.data <- Read10X(data.dir = "C:/Users/s/Downloads/")
sc <- SCseq(pbmc.data)

Here is my pbmc.data

> pbmc.data
33694 x 27179520 sparse Matrix of class "dgCMatrix"

This is the error i get :

sc <- SCseq(pbmc.data)
Error in asMethod(object) : Cholmod error 'problem too large' at file ../Core/cholmod_dense.c, line 105

I understand that RaceID requires a sparse matrix which I am already providing. Can you please explain? Thank you!