create_sce_by_dir: create a SingleCellExperiment object from data folder...
In scPipe: pipeline for single cell RNA-seq data analysis

Description Usage Arguments Details Value Examples

after we run sc_gene_counting and finish the preprocessing step. create_sce_by_dir can be used to generate the SingleCellExperiment obeject from the folder that contains gene count matrix and QC statistics. it can also generate the html report based on the gene count and quality control statistics

1 2	create_sce_by_dir(datadir, organism = NULL, gene_id_type = NULL, pheno_data = NULL, report = FALSE)

`datadir`	the directory that contains all the data and 'stat' subfolder.
`organism`	the organism of the data. List of possible names can be retrieved using the function 'listDatasets'from 'biomaRt' package. (i.e 'mmusculus_gene_ensembl' or 'hsapiens_gene_ensembl')
`gene_id_type`	gene id type of the data A possible list of ids can be retrieved using the function 'listAttributes' from 'biomaRt' package. the commonly used id types are 'external_gene_name', 'ensembl_gene_id' or 'entrezgene'
`pheno_data`	the external phenotype data that linked to each single cell. This should be an `AnnotatedDataFrame` object
`report`	whether to generate the html report in the data folder

a SingleCellExperiment object

## Not run: 
# the sce can be created fron the output folder of scPipe
# please refer to the vignettes
sce = create_sce_by_dir(datadir="output_dir_of_scPipe",
    organism="mmusculus_gene_ensembl",
    gene_id_type="ensembl_gene_id")

## End(Not run)
# or directly from the gene count and quality control matrix:
data("sc_sample_data")
data("sc_sample_qc")
sce = SingleCellExperiment(assays = list(counts = as.matrix(sc_sample_data)))
organism(sce) = "mmusculus_gene_ensembl"
gene_id_type(sce) = "ensembl_gene_id"
QC_metrics(sce) = sc_sample_qc
demultiplex_info(sce) = cell_barcode_matching
UMI_dup_info(sce) = UMI_duplication
dim(sce)