Contains sample data for a functional single-cell experiment.

fsce_small

Format

An object of class FunctionalSingleCellExperiment of length 2.

Source

Generated by data-raw/fsce_small.R.

Details

There are two SingleCellExperiments:

  1. rnaseq: a SingleCellExperiment object containing a single-cell mRNA sequencing (10x Genomics V2 3 prime) cell/gene counts matrix in the counts slot. Log-normalized counts are in the logcounts slot.

  2. haircut: a SingleCellExperiment object containing a Haircut experiment using DNA repair oligos. Counts are in the counts slot, and centered log-ratio normalized counts are in the logcounts slot.

Haircut repair substrates include:

  • A "normal" DNA oligo (Normal_)

  • a ribonucleotide (rG) at position 45 (Ribo_)

  • an Abasic site at position 45 (Abasic_)

  • a uracil:adenosine base-pair, with uracil at position 45 (Uracil_)

  • a uracil:guanosine base-pair, with uracil at position 45 (GU_)

Dimensionality reduction calculations are in the rnaseq experiment, including PCA, UMAP, and TSNE slots. In addition, clusters determined by cluster_kmeans() and cluster_leiden() are available as colData.

Examples

fsce_small
#> A FunctionalSingleCellExperiment object of 2 listed #> experiments with user-defined names and respective classes. #> Containing an ExperimentList class object of length 2: #> [1] rnaseq: SingleCellExperiment with 9462 rows and 250 columns #> [2] haircut: SingleCellExperiment with 426 rows and 250 columns #> Features: #> experiments() - obtain the ExperimentList instance #> colData() - the primary/phenotype DataFrame #> sampleMap() - the sample availability DataFrame #> `$`, `[`, `[[` - extract colData columns, subset, or experiment #> *Format() - convert into a long or wide DataFrame #> assays() - convert ExperimentList to a SimpleList of matrices
# Individual experiments fsce_small[["rnaseq"]]
#> class: SingleCellExperiment #> dim: 9462 250 #> metadata(1): PCA #> assays(2): counts logcounts #> rownames(9462): NOC2L HES4 ... AC004556.1 AC240274.1 #> rowData names(0): #> colnames(250): TGCGGGTGTAGAGTGC TGGTTCCCATCTATGG ... CTCGAAAAGTTCGATC #> TGACAACGTCGCATAT #> colData names(4): cell_id k_cluster leiden_cluster cell_cycle #> reducedDimNames(3): PCA UMAP TSNE #> spikeNames(0): #> altExpNames(0):
fsce_small[["haircut"]]
#> class: SingleCellExperiment #> dim: 426 250 #> metadata(0): #> assays(2): counts logcounts #> rownames(426): Abasic_1 Abasic_10 ... riboG_8 riboG_9 #> rowData names(2): hairpin position #> colnames(250): TGCGGGTGTAGAGTGC TGGTTCCCATCTATGG ... CTCGAAAAGTTCGATC #> TGACAACGTCGCATAT #> colData names(1): cell_id #> reducedDimNames(0): #> spikeNames(0): #> altExpNames(0):
# Gene and activity names (first 5 items) rownames(fsce_small[["rnaseq"]])[1:5]
#> [1] "NOC2L" "HES4" "ISG15" "TNFRSF4" "SDF4"
rownames(fsce_small[["haircut"]])[1:5]
#> [1] "Abasic_1" "Abasic_10" "Abasic_11" "Abasic_12" "Abasic_13"
# subset with `[` using `[features, cell_ids, experiments]` features <- c("Uracil_45", "TP53") cell_ids <- c("TGCGGGTGTAGAGTGC", "CTACGTCCACCACGTG") fsce_small[features, cell_ids, ]
#> A FunctionalSingleCellExperiment object of 2 listed #> experiments with user-defined names and respective classes. #> Containing an ExperimentList class object of length 2: #> [1] rnaseq: SingleCellExperiment with 1 rows and 2 columns #> [2] haircut: SingleCellExperiment with 1 rows and 2 columns #> Features: #> experiments() - obtain the ExperimentList instance #> colData() - the primary/phenotype DataFrame #> sampleMap() - the sample availability DFrame #> `$`, `[`, `[[` - extract colData columns, subset, or experiment #> *Format() - convert into a long or wide DataFrame #> assays() - convert ExperimentList to a SimpleList of matrices
# dimensionality reduction results SingleCellExperiment::reducedDimNames(fsce_small[["rnaseq"]])
#> [1] "PCA" "UMAP" "TSNE"
# UMAP results (first 5 rows) SingleCellExperiment::reducedDim(fsce_small[["rnaseq"]], "UMAP")[1:5, ]
#> [,1] [,2] #> TGCGGGTGTAGAGTGC 1.908842 0.8429150 #> TGGTTCCCATCTATGG -4.010063 0.7808292 #> CATATTCCACGTCAGC 2.501747 -0.7414021 #> TCACAAGTCCTGCTTG 0.891331 -8.8625031 #> GGAATAATCCAGGGCT -2.518112 3.6504053
# k-means cluster IDs SingleCellExperiment::colData(fsce_small[["rnaseq"]])
#> DataFrame with 250 rows and 4 columns #> cell_id k_cluster leiden_cluster cell_cycle #> <character> <character> <character> <character> #> TGCGGGTGTAGAGTGC TGCGGGTGTAGAGTGC 2 2 S #> TGGTTCCCATCTATGG TGGTTCCCATCTATGG 6 4 S #> CATATTCCACGTCAGC CATATTCCACGTCAGC 2 2 G2M #> TCACAAGTCCTGCTTG TCACAAGTCCTGCTTG 5 3 G1 #> GGAATAATCCAGGGCT GGAATAATCCAGGGCT 4 4 G1 #> ... ... ... ... ... #> CTACGTCCACCACGTG CTACGTCCACCACGTG 6 4 G1 #> TGTGGTAAGGTGCTTT TGTGGTAAGGTGCTTT 3 6 G1 #> AGCGTCGGTCGAGTTT AGCGTCGGTCGAGTTT 3 6 G1 #> CTCGAAAAGTTCGATC CTCGAAAAGTTCGATC 6 5 G2M #> TGACAACGTCGCATAT TGACAACGTCGCATAT 6 4 S