AlvMac {GOexpress} | R Documentation

## Sample data from a RNAseq experiment.

### Description

An example `ExpressionSet` including expression data and phenotypic information about the samples.

The expression data is saved in the `assayData` slot of the `ExpressionSet`. It is a gene-by-sample matrix, containing a subset of data from an in vitro stimulation of bovine macrophages with different mycobacterial strains. Column names are sample names, and row names are Ensembl gene identifiers of the Bos taurus species. Each cell contains the log2-transformed normalised expression level of each gene in each sample.

The phenotypic information is saved in the `phenoData` slot of the `ExpressionSet`. Row names are sample names and columns contain descriptive information about each sample, including experimental factors(e.g. Treatment, Timepoint, Animal).

### Usage

    data(AlvMac)

### Details

Gene expression was measured in poly-A purified strand-specific RNA libraries using the RNA-Sequencing Illumina(R) HiSeq(R) 2000 platform as paired-end 2 x 90 nucleotide reads. Raw reads from pooled RNA libraries were first deconvoluted according to sample-specific nucleotide barcodes. Read pairs containing adapter sequence in either read mate were discarded, and similarly read pairs of low overall quality in either mate were also discarded. Paired-end reads from each filtered individual library were aligned to the Bos taurus reference genome (B. taurus UMD3.1.71 genome release) using the STAR aligner software. For each library, raw counts for each gene based on sense strand data were obtained using the featureCounts software from the Subread package. The featureCounts parameters were set to unambiguously assign uniquely aligned paired-end reads in a stranded manner to the exons of genes within the Bos taurus reference genome annotation (B. taurus UMD3.1.71 genome annotation). The gene count outputs were further processed using the edgeR Bioconductor package.

The gene expression quantitation pipeline within the edgeR package was customised to: (1) filter out all bovine rRNA genes; (2) filter out genes displaying expression levels below the minimally-set threshold of one count per million [CPM] in at least ten individual libraries (number of biological replicates); (3) calculate normalisation factors for each library using the trimmed mean of M-values method; (4) log2-transform CPM values based on the normalised library size.

To generate this test data subset, we extracted 100 genes from the original dataset of 12,121 genes. All 7 genes associated with the GO term "GO:0034142" (i.e. "toll-like receptor 4 signaling pathway") present in the original full-size filtered-normalised dataset were kept, all 3 Ensembl gene identifiers annotated to the gene symbol 'RPL36A', and finally another random 90 random genes, making a total of 100 genes measured in 117 samples. Samples include all 10 biological replicates collected at four different time-points, see `data(targets)`. The TLR4 pathway was found in the full dataset as the top-ranking biological pathway discriminating the different mycobacterial infections (unpublished observations).

### Value

`assayData` is a matrix of expression levels for 100 genes (rows) measured in 117 samples (columns).

- `rownames` are Ensembl gene identifiers of the Bos taurus species.

- `colnames` are samples identifiers.

`phenoData` is a data frame with 117 samples and 7 descriptive fields (e.g. experimental factors) in the columns listed below:

- `rownames` are unique identifiers. Here, sample names.

- `File` contains local filenames where the RNAseq counts were obtained from.

- `Sample` contains individual sample name.

- `Animal` contains the unique identifier of the animal corresponding to the biological replicate, stored as a factor.

- `Treatment` contains the infection status of the sample, stored as a factor (CN: Control, MB: M. bovis, TB: M. tuberculosis)

- `Time` contains the time of measurement in hours post-infection,stored as a factor.

- `Group` contains a combination of the Treatment and Time factors above, stored as a factor itself.

- `Timepoint` contains the time of measurement, stored as a numeric value. This field is useful to use on the X-axis of expression plots. See function `expression_plot()`.

### Source

Publication in review process.

### Examples

    # Load the data
    data(AlvMac)

    # Structure of the data
    str(AlvMac)

    # Dimensions (rows, columns) of the data
    dim(AlvMac)

    # Subset of first 5 features and 5 samples
    AlvMac[1:5, 1:5]

    # Phenotypic information
    pData(AlvMac)

    # Phenotypic information about factor "Group"
    AlvMac$Group

    # Conversion of a factor to a character vector
    as.character(AlvMac$Group)

    # Number of samples (rows) and annotations (columns)
    dim(pData(AlvMac))

[Package GOexpress version 1.20.0 Index]