create_sce_by_dir {scPipe} | R Documentation

## create a SingleCellExperiment object from data folder generated by preprocessing step

### Description

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

### Usage

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

### Arguments

`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

### Details

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.

### Value

a SingleCellExperiment object

### Examples

    ## 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)

[Package scPipe version 1.0.6 Index]