dgrun / raceid3_stemid2_package Goto Github PK

Hi,
I have read the vignettes on R-CRAN , but I feel confused with here

The resulting lineage graph can be inspected and reveals the expected trajectories connecting the stem cells (cluster 2 and 3 of cycling and quiescent cells, respectively) to enterocytes (cluster 4) via transiently amplifying progenitors (cluster 1), to Paneth cells (cluster 7), and to goblet cells (cluster 6). The StemID score suggests stem cell identity for clusters 2 and 3

What puzzles me is the StemID score of cl.1 and cl.8 is higher than cl.2 and cl.3. But the StemID score suggests stem cell identity for clusters 2 and 3.

Here is the stemid score

  | links | entropy | StemIDscore
cl.1 | 3 | 0.109705 | 0.329115
cl.2 | 3 | 0.100491 | 0.301474
cl.3 | 4 | 0.082126 | 0.328503
cl.4 | 2 | 0.098243 | 0.196486
cl.5 | 0 | 0.099537 | 0.000000
cl.6 | 1 | 0.081058 | 0.081058
cl.7 | 2 | 0.000000 | 0.000000
cl.8 | 3 | 0.131742 | 0.395227

Thank you for your help.

Hi all, when I run to this step, it showed an error like this.

ltr <- comppvalue(ltr, pthr = 0.01, sensitive = FALSE)
Error in if (quantile(x, 1 - pthr) < cm[i, j]) 0 else 0.5 :
missing value where TRUE/FALSE needed

The original matrix was extracted from a Seurat object like this:
pro='S1'
sce <- CreateSeuratObject(Read10X('./scdata/allraw/day0/'),
pro)
sce <- NormalizeData(sce, normalization.method = "LogNormalize",
scale.factor = 10000)
GetAssay(sce,assay = "RNA")
sc <- as(as.matrix(sce@assays$RNA@counts), 'sparseMatrix')
sc <- SCseq(sc)
sc <- filterdata(sc,FGenes = hbb)
hbb <- rownames(sce)[grep("^Hbb",rownames(sce))]
sc <- filterdata(sc,FGenes = hbb)
sc_p <- compdist(sc,metric="pearson")
sc_p_n20_k <- clustexp(sc_p,sat = T,clustnr = 20,FUNcluster = 'kmedoids')
plotsaturation(sc_p_n20_k,disp=FALSE)
plotjaccard(sc_p_n20_k)
sc_p_n20_k <- clustexp(sc_p_n20_k,cln=10,sat=FALSE)
sc_p_n20_k <- findoutliers(sc_p_n20_k)
plotbackground(sc_p_n20_k)
plotsensitivity(sc_p_n20_k)
plotoutlierprobs(sc_p_n20_k)
clustheatmap(sc_p_n20_k)
sc_p_n20_k <- comptsne(sc_p_n20_k)
sc_p_n20_k <- compfr(sc_p_n20_k,knn=10)
sc_p_n20_k <- compumap(sc_p_n20_k)
plotmap(sc_p_n20_k, final = T, tp = 1, fr = T, um = F, cex = 0.5)
plotexpmap(sc_p_n20_k,"Procr",logsc=TRUE,fr=F)
ltr <- Ltree(sc_p_n20_k)
ltr <- compentropy(ltr)
ltr <- projcells(ltr,cthr=5,nmode=FALSE,fr=TRUE)
ltr <- projback(ltr,pdishuf=100)
ltr <- lineagegraph(ltr)
everything is OK until comppvalue

Hi,

I am trying to analyse a scRNA-seq data-set using the VarID package and ran into errors with the pruneKnn function:

`res <- pruneKnn(d,large=TRUE,regNB=T,knn=10,alpha=10,no_cores=8, pcaComp = 100)

Error in checkForRemoteErrors(val) :
8 nodes produced errors; first error: constraints are inconsistent, no solution!`

What do you think might be the problem?
I played around with the input parameters - large, regNB, alpha, pcaComp - with no luck.
The same count matrix and SCseq object work fine with RaceID and StemID functions.
And, to check installation issues, I ran pruneKnn() using a different data-set and it ran ok.

Thank you for your help.

Also, It will be great if you can add a functionality of importing Seurat integrated objects to RaceID3_StemD2 so that I can run pseudotime and trajectory analyses by StemD2.

Thanks.

Hi 😊,

I am trying to use a count matrix (.tsv dense matrix) into this function sc <- ### SCseq() but it does not seem to be able to read it. Therefore, I uploaded it first as data.table and then passed it into the function and it seems to be fine (why?).
However, after filtering the cells and computing a distance matrix I get the following error:

Error in cluster::pam(as.dist(x), k) : NA values in the dissimilarity matrix not allowed.

What do you think is causing the issue and how do I fix it?

I attached the screenshot for more info.

Thank you
Sara

Hi,

Thanks for your developing wonderful tools for discovering the rare cell population. I have tried to use Raceid followed the online protocol (https://cran.r-project.org/web/packages/RaceID/vignettes/RaceID.html). However, I am confused that how to identify the cell cluster. For example, I tried to find a rare cell population only expressed "zbtb16", but I didn't know how to do that. I can use the command "x <- data.frame(CELLID=names(sc@cpart),cluster=sc@cpart)" to extract the cells and their clusters. But I don't know how to find the specific expressed genes in each cluster cells. In your manual, the genes expression could be visulized by heatmap. But I found it is hard to visulize in more than thousands of cells. For example, I got the heatmap as followed:

In this figure, it is that the target genes expressed only in small cell population.
Hence, is any possible way to check the target genes expression in each cell population? I though violent plot or boxplot may be a better ways to visulize in each cell cluster. Can I convert the SCseq data format into SingleCellExperiment format or Seurat format, so that I can use "plotExpression"(Scater) or "VlnPlot" (Seurat) to visulize the target gene expression?

I am appreciated for your help!
Thanks!
Best,
Garen

hi, I want to convert a seurat object to scseq object and conduct downstream analysis, do you know any method could achieve it ?

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!