yeast_long: Yeast data set
In nclRcourses: Functions for R courses at Newcastle University

Description Details Value Note References

The data set we are going to use in the next few chapters comes from Gillespie et al., 2010. This paper is a tutorial for analysing time-course data in Bioconductor. The paper and associated R code can be downloaded from https://github.com/csgillespie/bmc-microarray The data were collected according to the experimental protocol described in Greenal et al.. Briefly, three biological replicates were studied on each of a wild-type (WT) yeast strain and a strain carrying the cdc13-1 temperature sensitive mutation (in which telomere uncapping is induced by growth at temperatures above around 27 degrees C). These replicates were sampled initially at 23 degrees C (at which cdc13-1 has essentially WT telomeres) and then at 1, 2, 3 and 4 hours after a shift to 30 degrees C to induce telomere uncapping. The thirty resulting RNA samples were hybridised to Affymetrix yeast2 arrays. The microarray data are available in the ArrayExpress database under accession numberE-MEXP-1551.

This data comes from Cockell et al., 2013. https://github.com/csgillespie/illumina-analysis/

The two data sets contain the same data, but in wide and long data frames.

A data frame

The values in the data frame have been normalised using the rma procedure. To reduce the memory footprint, only the first 500 probes are included in this data set. The columns in data_wide are

ID.: Probe ID
value.: Normalised log expression level
type.: Mutant or wild-type
rep.: Replication number: 1, 2, 3
tps.: Time point: 0, 60, 120, 180, 240 minutes

Gillespie, C. S., et al, 2010. Analysing yeast time course microarray data using BioConductor: a case study using yeast2 Affymetrix arrays. BMC Research Notes, 3:81.

Cockell, S, Bashton, M, Gillespie, CS. Bioconductor tools for microarray data analysis. In: Microarray Image and Data Analysis: Theory and Practice. CRC Press.

nclRcourses documentation built on May 2, 2019, 5:55 p.m.