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Hematopoietic stem cells population size estimate.

Source: R/population-size-estimate.R
HSC_population_size_estimate.Rd

[Stable] Hematopoietic stem cells population size estimate with capture-recapture models.

Usage

HSC_population_size_estimate(
  x,
  metadata,
  stable_timepoints = NULL,
  aggregation_key = c("SubjectID", "CellMarker", "Tissue", "TimePoint"),
  blood_lineages = blood_lineages_default(),
  timepoint_column = "TimePoint",
  seqCount_column = "seqCount_sum",
  fragmentEstimate_column = "fragmentEstimate_sum",
  seqCount_threshold = 3,
  fragmentEstimate_threshold = 3,
  nIS_threshold = 5,
  cell_type = "MYELOID",
  tissue_type = "PB",
  max_workers = 4
)

Arguments

x

An aggregated integration matrix. See details.

metadata

An aggregated association file. See details.

stable_timepoints

A numeric vector or NULL if there are no stable time points. NOTE: the vector is NOT intended as a sequence min-max, every stable time point has to be specified individually

aggregation_key

A character vector indicating the key used for aggregating x and metadata. Note that x and metadata should always be aggregated with the same key.

blood_lineages

A data frame containing information on the blood lineages. Users can supply their own, provided the columns CellMarker and CellType are present.

timepoint_column

What is the name of the time point column to use? Note that this column must be present in the key.

seqCount_column

What is the name of the column in x containing the values of sequence count quantification?

fragmentEstimate_column

What is the name of the column in x containing the values of fragment estimate quantification? If fragment estimate is not present in the matrix, param should be set to NULL.

seqCount_threshold

A single numeric value. After re-aggregating x, rows with a value greater or equal will be kept, the others will be discarded.

fragmentEstimate_threshold

A single numeric value. Threshold value for fragment estimate, see details.

nIS_threshold

A single numeric value. If a group (row) in the metadata data frame has a count of distinct integration sites strictly greater than this number it will be kept, otherwise discarded.

cell_type

The cell types to include in the models. Note that the matching is case-insensitive.

tissue_type

The tissue types to include in the models. Note that the matching is case-insensitive.

max_workers

Maximum parallel workers allowed

Value

A data frame with the results of the estimates

Input formats

Both x and metadata should be supplied to the function in aggregated format (ideally through the use of aggregate_metadata and aggregate_values_by_key). Note that the aggregation_key, aka the vector of column names used for aggregation, must contain at least the columns associated with the tags subject, cell_marker, tissue and a time point column (the user can specify the name of the column in the argument timepoint_column).

Specifying more than one group

Groups for the estimates are computed as a pair of cell type and tissue. If the user wishes to compute estimates for more than one combination of cell type and tissue, it is possible to specify them as character vectors to the fields cell_type and tissue_type respectively, noting that:

  • Vectors must have the same length or one of the 2 has to be of length 1

  • It is a responsibility of the user to check whether the combination exists in the dataset provided.

Example: Note that estimates are computed individually for each group.

On time points

If stable_timepoints is a vector with length > 1, the function will look for the first available stable time point and slice the data from that time point onward. If NULL is supplied instead, it means there are no stable time points available. Note that 0 time points are ALWAYS discarded. Also, to be included in the analysis, a group must have at least 2 distinct non-zero time points. NOTE: the vector passed has to contain all individual time points, not just the minimum and maximum

Setting a threshold for fragment estimate

If fragment estimate is present in the input matrix, the filtering logic changes slightly: rows in the original matrix are kept if the sequence count value is greater or equal than the seqCount_threshold AND the fragment estimate value is greater or equal to the fragmentEstimate_threshold IF PRESENT (non-zero value). This means that for rows that miss fragment estimate, the filtering logic will be applied only on sequence count. If the user wishes not to use the combined filtering with fragment estimate, simply set fragmentEstimate_threshold = 0.

Required tags

The function will explicitly check for the presence of these tags:

  • subject

  • tissue

  • cell_marker

See also

Other Analysis functions: CIS_grubbs(), compute_abundance(), cumulative_is(), gene_frequency_fisher(), is_sharing(), iss_source(), sample_statistics(), top_integrations(), top_targeted_genes()

Examples

data("integration_matrices", package = "ISAnalytics")
data("association_file", package = "ISAnalytics")
aggreg <- aggregate_values_by_key(
    x = integration_matrices,
    association_file = association_file,
    value_cols = c("seqCount", "fragmentEstimate")
)
aggreg_meta <- aggregate_metadata(association_file = association_file)
estimate <- HSC_population_size_estimate(
    x = aggreg,
    metadata = aggreg_meta,
    fragmentEstimate_column = NULL,
    stable_timepoints = c(90, 180, 360),
    cell_type = "Other"
)
#> Calculating number of IS for each group...