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infercnv 10x
GeorgescuC edited this page Feb 20, 2020
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Below provides an example of how you might generate a counts matrix for use with inferCNV, starting with 10x data.
Here, we'll use Seurat for converting 10x count data to a compatible matrix format.
counts_matrix = as.matrix(seurat_obj@assays$RNA@counts[,colnames(seurat_obj)])
library(Seurat)
data = Read10X(data.dir = "10x_data_dir/")
seurat_obj = CreateSeuratObject(raw.data=data, min.cells=3, min.genes=200)
counts_matrix = as.matrix([email protected][,[email protected]])
# use more palatable column names (cell identifiers)
cell.names <- sapply(seq_along(colnames(counts_matrix)), function(i) paste0("cell_", i), USE.NAMES = F)
colnames(counts_matrix) = cell.names
# write the output table
write.table(round(counts_matrix, digits=3), file='sc.10x.counts.matrix', quote=F, sep="\t")
Note, if the regular tab-delimited data matrix file is going to be too large, you could save the matrix as a sparse Matrix object, and use this sparse Matrix object as input to inferCNV. (documentation forthcoming)
Now, the data is ready for use with InferCNV.
When using infercnv::run(), set 'cutoff=0.1' with 10xGenomics data, instead of the default (1) we tend to use with smartSeq2 and less sparse data.
- InferCNV Home
- Quick Start
- Installing inferCNV
- Running InferCNV
- Applying Noise Filters
- Predicting CNV via HMM
- Bayesian Mixture Model
- Tumor heterogeneity - define tumor subclusters
- Interpreting the Figure
- Inputs to InferCNV
- Outputs from InferCNV
- More inferCNV example data sets
- Using 10x data
- Interactively navigating data using the Next Generation Heatmap Viewer
- Extracting HMM features
- FAQ and common issues