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Question: New gage package: Generally Applicable Gene-set/Pathway Analysis
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gravatar for Luo Weijun
8.0 years ago by
Luo Weijun1.4k
United States
Luo Weijun1.4k wrote:
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 interest! Weijun Luo
ADD COMMENTlink modified 8.0 years ago • written 8.0 years ago by Luo Weijun1.4k
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gravatar for Luo Weijun
8.0 years ago by
Luo Weijun1.4k
United States
Luo Weijun1.4k wrote:
Hello Edwin, 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! Weijun --- 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 > interest! > Weijun Luo > > > > >
ADD COMMENTlink written 8.0 years ago by Luo Weijun1.4k
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gravatar for Edwin Groot
8.0 years ago by
Edwin Groot230
Edwin Groot230 wrote:
On Tue, 19 Oct 2010 10:54:16 -0700 (PDT) Luo Weijun <luo_weijun at="" yahoo.com=""> wrote: > 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. > Nice to see it in Bioconductor now. Good show, Weijun! > 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 interest! > Weijun Luo > Because I had to analyze experiments with 3 or 4 replicates, GAGE was the GSA package of choice. GSEA requires about 8 replicates per treatment. Edwin -- Dr. Edwin Groot, postdoctoral associate AG Laux Institut fuer Biologie III Schaenzlestr. 1 79104 Freiburg, Deutschland +49 761-2032945
ADD COMMENTlink written 8.0 years ago by Edwin Groot230
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