gse16873: GSE16873: a breast cancer microarray dataset

In gage: Generally Applicable Gene-set Enrichment for Pathway Analysis

Description

GSE16873 is a breast cancer study (Emery et al, 2009) downloaded from Gene Expression Omnibus (GEO). GSE16873 covers twelve patient cases, each with HN (histologically normal), ADH (ductal hyperplasia), and DCIS (ductal carcinoma in situ) RMA samples. Due to the size limit of this package, we split this GSE16873 into two halves, each including 6 patients with their HN and DCIS but not ADH tissue types. This gage package only includes the first half dataset for 6 patients as this example dataset gse16873. Most of our demo analyses are done on the first half dataset, except for the advanced analysis where we use both halves datasets with all 12 patients. Details section below gives more information.

Usage

data(gse16873)

Details

Raw data for these two half datasets were processed separately using two different methods, FARMS and RMA, respectively to generate the non-biological data heterogeneity. The first half dataset is named as gse16873, the second half dataset named gse16873.2. We also have the full dataset, gse16873.full, which includes all HN, ADH and DCIS samples of all 12 patients, processed together using FARMS. The second half dataset plus the full dataset and the original BMP6 dataset used in GAGE paper and earlier versions of gage is supplied with another package, gageData.

Source

GEO Dataset GSE16873: <URL: http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16873>

References

Emery LA, Tripathi A, King C, Kavanah M, Mendez J, Stone MD, de las Morenas A, Sebastiani P, Rosenberg CL: Early dysregulation of cell adhesion and extracellular matrix pathways in breast cancer progression. Am J Pathol 2009, 175:1292-302.

Examples

data(gse16873)
#check the heterogenity of the two half datasets
boxplot(data.frame(gse16873))

#column/smaple names
cn=colnames(gse16873)
hn=grep('HN',cn, ignore.case =TRUE)
adh=grep('ADH',cn, ignore.case =TRUE)
dcis=grep('DCIS',cn, ignore.case =TRUE)
print(hn)
print(dcis)

data(kegg.gs)
lapply(kegg.gs[1:3],head)
head(rownames(gse16873))
gse16873.kegg.p <- gage(gse16873, gsets = kegg.gs,
    ref = hn, samp = dcis)

Example output

[1]  1  3  5  7  9 11
[1]  2  4  6  8 10 12
$`hsa00010 Glycolysis / Gluconeogenesis`
[1] "10327" "124"   "125"   "126"   "127"   "128"  

$`hsa00020 Citrate cycle (TCA cycle)`
[1] "1431" "1737" "1738" "1743" "2271" "3417"

$`hsa00030 Pentose phosphate pathway`
[1] "2203"   "221823" "226"    "229"    "22934"  "230"   

[1] "10000"     "10001"     "10002"     "10003"     "100048912" "10004"    

gage documentation built on Dec. 13, 2020, 2:01 a.m.