cole-trapnell-lab / monocle-release

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How to perform merged pseudo time analysis across individual scRNAseq data? #197

Open ondp15 opened 6 years ago

ondp15 commented 6 years ago

Hello,

Hope this finds you well. I have multiple scRNAseq data from separate experiments performed across time points (total of ~6-7 individual experiments). I want to merge these data together to perform the pseudo time analysis. I'm using Monocle 2, because Monocle 3 alpha is incompatible with R 3.5.0. -Does Monocle 2 allow me to do this? -Do I have to run individually each sample using the code below? -Or is there a way to modify the code to load each individual data set into one big newCellDataSet and continue with the http://cole-trapnell-lab.github.io/monocle-release/docs/#constructing-single-cell-trajectories?

Thanks in advance for all your help.

PS: This is so far the code. And it seems to be working.

source("http://bioconductor.org/biocLite.R") biocLite("monocle") library(monocle) packageVersion('monocle') source("http://cf.10xgenomics.com/supp/cell-exp/rkit-install-2.0.0.R") library(cellrangerRkit) packageVersion("cellrangerRkit") cellranger_pipestance_path <- "/path/to/pipestance" gbm <- load_cellranger_matrix(cellranger_pipestance_path) fd <- fData(gbm) colnames(fd)[2] <- "gene_short_name" gbm_cds <- newCellDataSet(exprs(gbm), phenoData = new("AnnotatedDataFrame", data = pData(gbm)), featureData = new("AnnotatedDataFrame", data = fd), lowerDetectionLimit = 0.5, expressionFamily = negbinomial.size()) gbm_cds <- estimateSizeFactors(gbm_cds) gbm_cds <- estimateDispersions(gbm_cds) gbm_cds <- detectGenes(gbm_cds, min_expr = 0.1)

print(head(fData(gbm_cds)))

davisidarta commented 5 years ago

Hello ondp15!

Have you found a way to do what you're intending? I'm following a very similar approach towards brain development, but merging different datasets from different labs and time points is still being a struggle. I would really appreciate it if you could give us an update on your analysis.

Best regards, Davi

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