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Select high-quality control replicates via TMMwsp log-CPM correlation

Source: R/select_robust_controls.R
select_robust_controls.Rd

For a given control group (e.g., DMSO) on a specific plate/batch, this function ranks samples by their average correlation (Fisher z-averaged) to all other samples using edgeR's TMMwsp-normalized log2-CPM. It returns the ranking and (optionally) plots per-sample expression distributions and sample-sample correlation heatmaps.

Usage

select_robust_controls(
  data,
  samples,
  orig_ident,
  cpm_filter = 1,
  min_samps = 16,
  corr_method = c("spearman", "pearson"),
  top_n = 5,
  make_plots = TRUE
)

Arguments

data

A tidyseurat object containing an RNA assay with a counts layer.

samples

the control/treatment label to keep in column samples (e.g., "CB_43_EP73_0"). Only cells/samples with this label are considered.

orig_ident

Character scalar: the plate/batch identifier to keep (e.g., "VH02012942"). Only cells/samples from this batch are considered.

cpm_filter

Numeric scalar; CPM threshold used for gene filtering prior to normalization (default 1).

min_samps

Integer; a gene must be expressed (CPM > cpm_filter) in at least this many samples to be retained (default 16).

corr_method

Correlation type used for ranking; one of c("spearman","pearson") (default "spearman").

top_n

Integer; the number of top-ranked samples to report in topN. Ties at the cutoff are kept (default 5).

make_plots

Logical; if TRUE, print a log2-CPM boxplot and Pearson/Spearman correlation heatmaps (default TRUE).

Value

A list with elements:

  • subset_obj: The Seurat object subset used for analysis.

  • dge: The filtered edgeR::DGEList

  • log_cpm_tmm: Matrix of TMMwsp log2-CPM.

  • boxplot_df: Long-format data frame used for the boxplot (gene, sample, log_cpm).

  • cor_pearson: Sample-sample Pearson correlation matrix.

  • cor_spearman: Sample-sample Spearman correlation matrix.

  • ranking_method: The correlation method used for ranking.

  • scores_mean_to_others: Named numeric vector of mean Fisher-z back-transformed correlations (higher = better), sorted decreasing.

  • topN: Named numeric vector of the top-ranked samples (ties at the cutoff kept).

Details

Workflow:

  1. Subset to the specified samples and orig_ident (plate/batch).

  2. Build an edgeR::DGEList, filter lowly expressed genes using CPM and min_samps.

  3. Normalize with TMMwsp and compute log2-CPM.

  4. Rank samples by mean Fisher z transformed correlation to all other samples (according to corr_method).

  5. Return the ranking, correlation matrices, the normalized matrix, and (optionally) plots for QC.

Column names of the counts matrix are rewritten to "<orig.ident>_<Well_ID>" for easier visual inspection in plots.

Examples

data(mini_mac)
res <- select_robust_controls(mini_mac,samples = "DMSO_0", orig_ident = "PMMSq033_mini")