matchprobes

[…] [] and 19 (u133a) samples from gse37317 []. we considered only probe sets that were present in both array types u133a and u133plus2. we combined the u133plus2 samples and the u133a samples using the matchprobes bioconductor package []. we normalized the microarray samples using rma and quantile normalization [] using log2 expression intensities for each probe set. as a summary statistic […]

[…] and microarray processing were reported by roessler et al. , and are available at http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse14520. hsp gene expression levels were calculated using the matchprobes package in the r programming environment and the log2 rma-calculated signal intensity was reported. details of the experiment protocols and data processing are available […]

[…] for preprocessing. thus, the robust multi-array average (rma) method was used for background correction. quantile normalization was then done. we also introduced a specific step for pm (perfect matchprobes) adjustment, using the pm-only model-based expression index (option ‘pmonly’). finally, for the summarization step, the median polish method was used. next, variance filtering by iqr […]

[…] genechip hg-u133plus2. we normalized and combined information across different platforms using the standard functions provided by bioconductor packages. we used the combineaffybatch function in the matchprobes library to merge data from hg-u133a and hg-u133plus2 genechips and we applied rma normalization to the merged dataset. the mirna expression profiling panel was performed by integragen […]

[…] mas's work at http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=gse14323. the data from the two affymetrix platforms, hg-u133a and hg-u133a 2.0, were combined by means of the r package matchprobes . the robust multi-array (rma) method was utilized to process the data . duplicated probes for each gene were averaged and the processed data were normalized with the quantile method . […]