run_quantiseq {quantiseqr} | R Documentation

## Run the quanTIseq algorithm

### Description

Use quanTIseq to deconvolute a gene expression matrix.

### Usage

    run_quantiseq(
      expression_data,
      signature_matrix = "TIL10",
      is_arraydata = FALSE,
      is_tumordata = FALSE,
      scale_mRNA = TRUE,
      method = "lsei",
      column = "gene_symbol",
      rm_genes = NULL,
      return_se = is(expression_data, "SummarizedExperiment")
    )

### Arguments

`expression_data` | The gene expression information, containing the TPM values for the measured features. Can be provided as a simple gene expression matrix, or a data frame (with HGNC gene symbols as row names and sample identifiers as column names) an `ExpressionSet` object (from the Biobase package), where the HGNC gene symbols are provided in a column of the `fData` slot - that is specified by the `column` parameter below a `SummarizedExperiment` object, or any of the derivative classes (e.g. DESeq2's `DESeqDataSet`), in which the assay (default: "abundance") is containing the TPMs as expected. Internally, `quantiseqr` handles the conversion to an object which is used in the deconvolution procedure.
`signature_matrix` | Character string, specifying the name of the signature matrix. At the moment, only the original `TIL10` signature can be selected.
`is_arraydata` | Logical value. Should be set to TRUE if the expression data are originating from microarray data. For RNA-seq data, this has to be FALSE (default value). If set to TRUE, the `rmgenes` parameter (see below) is set to "none".
`is_tumordata` | Logical value. Should be set to TRUE if the expression data is from tumor samples. Default: FALSE (e.g. for RNA-seq from blood samples)
`scale_mRNA` | Logical value. If set to FALSE, it disables the correction of cell-type-specific mRNA content bias. Default: TRUE
`method` | Character string, defining the deconvolution method to be used: `lsei` for constrained least squares regression, `hampel`, `huber`, or `bisquare` for robust regression with Huber, Hampel, or Tukey bisquare estimators, respectively. Default: `lsei`.
`column` | Character, specifies which column in the `fData` slot (for the ExpressionSet object) contains the information of the HGNC gene symbol identifiers
`rm_genes` | Character vector, specifying which genes have to be excluded from the deconvolution analysis. It can be provided as a vector of gene symbols (contained in the `expression_data`) a single string among the choices of "none" (no genes are removed) and "default" (a list of genes with noisy expression RNA-seq data is removed, as explained in the quanTIseq paper). Default: "default" for RNA-seq data, "none" for microarrays.
`return_se` | Logical value, controls the format of how the quantification is returned. If providing a `SummarizedExperiment`, it can simply extend its `colData` component, without the need to create a separate data frame as output.

### Details

The values contained in the `expression_data` need to be provided as TPM values, as this is the format also used to store the `TIL10` signature, upon which quanTIseq builds to perform the immune cell type deconvolution. Expression data should not be provided in logarithmic scale.

If providing the `expression_data` as a `SummarizedExperiment`/`DESeqDataSet` object, it might be beneficial that this has been created via `tximport` - if this is the case, the assay named "abundance" will be automatically created upon importing the transcript quantification results.

### Value

A data.frame containing the quantifications of the cell type proportions, or alternatively, if providing `expression_data` as `SummarizedExperiment` and setting `return_se` to TRUE, a `SummarizedExperiment` with the quantifications included by expanding the `colData` slot of the original object

### References

F. Finotello, C. Mayer, C. Plattner, G. Laschober, D. Rieder, H. Hackl, A. Krogsdam, Z. Loncova, W. Posch, D. Wilflingseder, S. Sopper, M. Jsselsteijn, T. P. Brouwer, D. Johnsons, Y. Xu, Y. Wang, M. E. Sanders, M. V. Estrada, P. Ericsson-Gonzalez, P. Charoentong, J. Balko, N. F. d. C. C. de Miranda, Z. Trajanoski. "Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data". Genome Medicine 2019;11(1):34. doi: 10.1186/s13073-019-0638-6.

C. Plattner, F. Finotello, D. Rieder. "Chapter Ten - Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq". Methods in Enzymology, 2020. doi: 10.1016/bs.mie.2019.05.056.

### Examples

    data(dataset_racle)
    dim(dataset_racle$expr_mat)
    res_quantiseq_run <- quantiseqr::run_quantiseq(
      expression_data = dataset_racle$expr_mat,
      signature_matrix = "TIL10",
      is_arraydata = FALSE,
      is_tumordata = TRUE,
      scale_mRNA = TRUE
    )

    # using a SummarizedExperiment object
    library("SummarizedExperiment")
    se_racle <- SummarizedExperiment(
      assays = List(
        abundance = dataset_racle$expr_mat
      ),
      colData = DataFrame(
        SampleName = colnames(dataset_racle$expr_mat)
      )
    )

    res_run_SE <- quantiseqr::run_quantiseq(
        expression_data = se_racle,
        signature_matrix = "TIL10",
        is_arraydata = FALSE,
        is_tumordata = TRUE,
        scale_mRNA = TRUE
    )

[Package quantiseqr version 1.2.0 Index]