[BioC] New gage package: Generally Applicable Gene-set/Pathway Analysis
luo_weijun at yahoo.com
Wed Oct 20 16:29:35 CEST 2010
Good to see you here.
Thanks for the comments. Indeed, one the major advantages of GAGE is the consistent performance across all range of sample sizes, from no replicate (single sample per condition) to arbitrary large number of replicates. This makes it particular useful for the most microarray studies with small sample sizes. In the mean time, it is aware of the sample size changes as the significance increases steadily with larger sample size. In this second major release version, we add a lot more options, functions and supportive data, which makes GAGE even more useable and flexible. I hope you find it useful. Thanks for your long time interest!
--- On Tue, 10/19/10, Luo Weijun <luo_weijun at yahoo.com> wrote:
> From: Luo Weijun <luo_weijun at yahoo.com>
> Subject: New gage package: Generally Applicable Gene-set/Pathway Analysis
> To: bioconductor at stat.math.ethz.ch
> Cc: luo_weijun at yahoo.com
> Date: Tuesday, October 19, 2010, 1:54 PM
> Dear Bioconductor users,
> I’d like to introduce my gage package newly released with
> Bioc 2.7. Although the first version of gage package came
> out about two years ago, this is its first release with
> Bioc. Please take a look at gage package at http://bioconductor.org/help/bioc-views/release/bioc/html/gage.html,
> if you are doing gene set analysis, general microarray or
> sequencing data analysis.
> Gene set analysis (GSA, also called or pathway analysis) is
> a powerful strategy to infer functional and mechanistic
> changesfrom high through microarray data. However, classical
> GSA methodsonly have limited usage to a small number of
> microarray studies as they cannot handle datasets of
> different sample sizes, experimental designs, microarray
> platforms, and other types of heterogeneity. To address
> these limitations, we developed and published a new method
> called Generally Applicable Gene-set Enrichment (GAGE).
> Besides general applicability, we’ve also showed that GAGE
> consistently achieves superior or similar performance over
> other frequently used methods.
> In gage package, we provide functions for basic GAGE
> analysis, result processing and presentation. We have also
> built pipeline routines for of multiple GAGE analyses in a
> batch, comparison between parallel analyses, and combined
> analysis of heterogeneous data from different
> sources/studies. In addition, we provide demo microarray
> data and commonly used gene set data based on KEGG pathways
> and GO terms. These funtions and data are also useful for
> gene set analysis using other methods.
> We also release a supportive data package, gageData, which
> includes two full microarray datasets and gene set data
> based on KEGG pathways and GO terms for major research
> species, including human, mouse, rat and budding yeast.
> Please let me know if you have any
> questions/comments/suggestions. Thank you for your
> Weijun Luo
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