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