|
15 | 15 | #' for senescent samples, "young" for proliferative).} |
16 | 16 | #' } |
17 | 17 | #' |
| 18 | +#' |
18 | 19 | #' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63577} |
19 | 20 | #' |
20 | 21 | #' @references Marthandan S, Priebe S, Baumgart M, Groth M et al. Similarities |
|
24 | 25 | #' @references Marthandan S, Baumgart M, Priebe S, Groth M et al. Conserved |
25 | 26 | #' Senescence Associated Genes and Pathways in Primary Human Fibroblasts |
26 | 27 | #' Detected by RNA-Seq. PLoS One 2016;11(5):e0154531. PMID: 27140416 |
27 | | -#' |
28 | | -#' @keywords datasets |
| 28 | +#' |
| 29 | +#' @usage data(metadata_example) |
29 | 30 | "metadata_example" |
30 | 31 |
|
31 | 32 | #' Gene Expression Counts for Marthandan et al. (2016) RNA-Seq Data |
32 | 33 | #' |
33 | | -#' A numeric matrix containing filtered and normalized gene expression data from |
34 | | -#' the Marthandan et al. (2016) study (GEO accession GSE63577). |
| 34 | +#' A numeric matrix containing filtered and normalized (non log-transformed) |
| 35 | +#' gene expression data from the Marthandan et al. (2016) study (GEO accession |
| 36 | +#' GSE63577). |
35 | 37 | #' |
36 | 38 | #' Raw FASTQ files were downloaded using `fasterq-dump` (v2.11.0) and processed |
37 | 39 | #' in a reproducible conda environment (Python v3.11.5). Quality control was |
38 | 40 | #' conducted using FastQC (v0.12.1) and summarised with MultiQC (v1.14). |
39 | 41 | #' Pseudo-alignment to the RefSeq transcriptome (NCBI release 109) was performed |
40 | 42 | #' using kallisto (v0.44.0). Genes with low expression (mean count < 70 in all |
41 | 43 | #' conditions) were filtered out. Count normalization factors were calculated |
42 | | -#' with `edgeR::calcNormFactors`, and log2-transformed values were obtained via |
43 | | -#' `limma::voom`. |
| 44 | +#' with `edgeR::calcNormFactors`. |
44 | 45 | #' |
45 | 46 | #' Intermediate time points for HFF and MRC5 cell lines were excluded, resulting |
46 | 47 | #' in a final dataset with 45 high-quality samples across proliferative, |
|
52 | 53 | #' |
53 | 54 | #' @format A numeric matrix with rows as genes (gene symbols) and columns as |
54 | 55 | #' samples (sample IDs). |
55 | | -#' |
| 56 | +#' |
56 | 57 | #' @source \url{https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63577} |
57 | 58 | #' |
58 | 59 | #' @references Marthandan S, Priebe S, Baumgart M, Groth M et al. Similarities |
|
63 | 64 | #' Senescence Associated Genes and Pathways in Primary Human Fibroblasts |
64 | 65 | #' Detected by RNA-Seq. |
65 | 66 | #' *PLoS One* 2016;11(5):e0154531. PMID: 27140416 |
66 | | -#' |
67 | | -#' @keywords datasets |
| 67 | +#' |
| 68 | +#' @usage data(counts_example) |
68 | 69 | "counts_example" |
69 | 70 |
|
70 | 71 | #' Example Gene Sets for Cellular Senescence |
|
75 | 76 | #' curated gene set of commonly reported senescence markers, |
76 | 77 | #' with directionality (+1 or -1).} |
77 | 78 | #' \item{REACTOME_Senescence}{Character vector of gene symbols. The |
78 | | -#' REACTOME_CELLULAR_SENESCENCE from MSigDB pathway. No directionality.} |
| 79 | +#' REACTOME_CELLULAR_SENESCENCE from MSigDB database No directionality.} |
79 | 80 | #' \item{HernandezSegura}{A data frame with columns `gene` and `direction`. |
80 | 81 | #' A gene set from Hernandez-Segura et al. (2017), with directionality (+1 or -1).} |
81 | 82 | #' } |
82 | | -#' |
| 83 | +#' |
83 | 84 | #' @references Hernandez-Segura A, de Jong TV, Melov S, Guryev V, Campisi J, |
84 | 85 | #' Demaria M. Unmasking Transcriptional Heterogeneity in Senescent Cells. |
85 | 86 | #' *Curr Biol.* 2017 Sep 11;27(17):2652-2660.e4. doi: 10.1016/j.cub.2017.07.033. |
86 | 87 | #' Epub 2017 Aug 30. PMID: 28844647; PMCID: PMC5788810. |
87 | | -#' @keywords datasets |
| 88 | +#' @usage data(genesets_example) |
88 | 89 | "genesets_example" |
89 | 90 |
|
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