#' Protein Atlas expression data version fdb11
#'
#' Protein Atlas expression data version fdb11
#'
#' This data contains the expression profile data from
#' https://proteinatlas.org for each gene/protein provided
#' in version fdb11.
#'
#' @format `matrix` of numeric values, following their normalization
#' methodology. The rownames indicate official Entrez gene symbols,
#' and the colnames indicate biological samples. The colnames
#' include a suffix indicating the type of sample, and include:
#' `"Tissue"`, `"Blood"`, `"Brain"`, and `"Cell"`. Note that
#' `"Blood"` and `"Brain"` samples may also be included in the
#' overall `"Tissue"` samples.
#'
#' @family pajam data
#'
"proteinatlas_expr_fdb11"
#' Protein Atlas useful genesets for visualization
#'
#' Protein Atlas useful genesets for visualization
#'
#' This object includes a list of genesets in the form of
#' character vectors of rownames aligned with
#' `proteinatlas_expr_fdb11`. The genesets are some useful
#' annotations for visualization that include:
#'
#' * `"Plasma"` - found in circulating blood plasma
#' * `"secreted_proteins"` - secreted proteins
#' * `"membrane_proteins"` - membrane-bound proteins
#' * `"FDA"` - gene targets of FDA-approved drugs
#' * `"NOT_membrane_secreted"` - non-membrane and non-secreted proteins (intracellular)
#' * `"TFs"` - transcription factors
#' * `"HKs"` - housekeeper genes
#' * `"rna_lymphoid"` - RNA-lymphoid associated genes
#'
#' The annotations are not intended to be fully exhaustive,
#' but are intended to represent some useful annotations
#' not necessarily associated directly with the biological
#' function of each gene/protein. For example, it may be useful
#' to see whether most up-regulated proteins are secreted or
#' intracellular, or perhaps most up-regulated gene transcripts
#' are transcription factors and are not secreted.
#'
#' @format `list` of `character vector`, whose values match
#' `rownames(proteinatlas_expr_fdb11)`.
#'
#' @examples
#' use_im <- c("secreted_proteins",
#' "membrane_proteins",
#' "NOT_membrane_secreted",
#' "TFs");
#' proteinatlas_im <- list2im_opt(proteinatlas_genesets_fdb11[use_im]);
#' test_genes <- c("DKK1","DKK4","CXCL12","IL6R","MET",
#' "HK2","FTL","FTH1","STAT1","STAT3","CDKN1B");
#'
#' proteinatlas_heatmap(genes=test_genes,
#' type="Blood",
#' centered=TRUE,
#' gene_im=proteinatlas_im);
#'
#' proteinatlas_heatmap(genes=test_genes,
#' type="Blood",
#' centered=TRUE,
#' column_filter=4,
#' row_cex=0.8,
#' column_names_max_height=grid::unit(12, "cm"),
#' gene_im=proteinatlas_im);
#'
#' @family pajam data
#'
"proteinatlas_genesets_fdb11"
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