Validation modified by Robert Kofler

Robert Kofler — Sun, 27 Aug 2017 17:15:43 -0000

--- v2
+++ v3
@@ -96,7 +96,8 @@

 With an FDR of <0.05 neither GoMiner nor Gowinda found a significant overrepresentation for any GO categories. The p-values (not FDR corrected) for the different GO categories obtained by Gowinda and GoMiner are highly correlated (Spearman's rank correlation; rho=0.9999005; p-value < 2.2e-16), see graph below: 

-![](http://gowinda.googlecode.com/files/cor_gowinda_gominer.png)
+[[img src=cor_gowinda_gominer.png]]
+

 Our results (including our random SNPs) for the comparision of Gowinda with GoMiner can be found here: <http: gowinda.googlecode.com="" files="" step1_valGoMiner.zip="">

@@ -159,7 +160,7 @@

 Although we randomly drew SNPs and would thus not expect an overrepresentation of any GO term, GoMiner reports a significant overrepresentation for 341 GO terms (FDR <0.05). This demonstrates that the biases of GWA datasets substantially affect GO analysis. By contrast, Gowinda correctly reports that none of the GO terms show any significant overrepresentation (FDR <0.05). The correlation between the p-values obtained with Gowinda and GoMiner (Spearman's rank correlation; rho=0.5610065; p-value < 2.2e-16) is much worse as compared to the unbiased data set. The following graph shows the correlation of the p-values obtained with Gowinda and GoMiner using the length biased dataset. 

-[![](http://gowinda.googlecode.com/files/corBiased_Gominer_Gowinda.png)] 
+[[img src=corBiased_Gominer_Gowinda.png]]

 Our results of this analysis can be found here: <http: gowinda.googlecode.com="" files="" step2_lengthBias.zip="">

Validation modified by Robert Kofler

Robert Kofler — Sun, 27 Aug 2017 17:13:24 -0000

--- v1
+++ v2
@@ -1,29 +1,4 @@
-  * Introduction
-    * Files
-      * Results for validating Gowinda
-  * Validating Gowinda by comparing the results to GoMiner
-    * Obtain a set of genes that are not overlapping and have an associated GO term
-    * Introduce five SNPs into each of the non-overlapping genes
-    * Choose 1000 random SNPs
-    * Perform GO term analysis with Gowinda
-    * Perform GO term analysis with GoMiner
-    * Merge the results of Gowinda and GoMiner
-    * Results for unbiased analysis
-  * Validating whether Gowinda eliminates the gene length bias
-    * Extract the genes having an associated GO term
-    * Introduce one SNP every 100 bp
-    * Randomly draw 1000 SNPs
-    * Perform GO term analysis with Gowinda
-    * Perform GO term analysis with GoMiner
-    * Merge the results of Gowinda and GoMiner
-    * Results for length biased analysis
-  * Validating whether Gowinda identifies preselected GO categories
-    * Identify genes having an associated GO term
-    * Introduce one SNP every 100bp for genes with an associated GO term
-    * Randomly pick five small GO categories
-    * Create 1000 candidate SNPs
-    * Perform GO term analysis with Gowinda
-    * Results for recovery of preselected GO categories
+[TOC]

 # Introduction

Validation modified by Anonymous

Anonymous — Wed, 18 Mar 2015 14:56:33 -0000

Introduction
- Files
- Results for validating Gowinda
Validating Gowinda by comparing the results to GoMiner
- Obtain a set of genes that are not overlapping and have an associated GO term
- Introduce five SNPs into each of the non-overlapping genes
- Choose 1000 random SNPs
- Perform GO term analysis with Gowinda
- Perform GO term analysis with GoMiner
- Merge the results of Gowinda and GoMiner
- Results for unbiased analysis
Validating whether Gowinda eliminates the gene length bias
- Extract the genes having an associated GO term
- Introduce one SNP every 100 bp
- Randomly draw 1000 SNPs
- Perform GO term analysis with Gowinda
- Perform GO term analysis with GoMiner
- Merge the results of Gowinda and GoMiner
- Results for length biased analysis
Validating whether Gowinda identifies preselected GO categories
- Identify genes having an associated GO term
- Introduce one SNP every 100bp for genes with an associated GO term
- Randomly pick five small GO categories
- Create 1000 candidate SNPs
- Perform GO term analysis with Gowinda
- Results for recovery of preselected GO categories

Introduction

We validated Gowinda with the following approaches:

first we compared the results of Gowinda with GoMiner using an unbiased dataset (no gene length bias and no overlapping genes). We found that the p-values reported by Gowinda and GoMiner are highly correlated (Spearman's rank correlation; rho=0.99).
second we assessed the impact of the gene length bias and estimated whether Gowinda efficiently deals with the bias. We found that with a biased dataset GoMiner reports an significant enrichment for ~300 GO categories, whereas Gowinda correctly reports none.
and third we tested whether Gowinda correctly identifies a significant enrichment for 5 preselected GO terms and found that Gowinda indeed correctly identified these preselected GO categories (FDR<0.01).

Files

The data and scripts of following file allow to repeat the whole validation of Gowinda:

http://gowinda.googlecode.com/files/validation_files.zip

This archive contains:

the Python scripts used in the validation
the annotation of D. melanogaster (v5.43)
the GO associations obtained from GoMiner for 'CG' gene_ids

Results for validating Gowinda

As our approach for validating Gowinda contains several random drawing steps we also provide the results we obtained for these random procedures, in order to allow an exact reproduction of our results.

biased dataset: http://gowinda.googlecode.com/files/step1_valGoMiner.zip
unbiased dataset: http://gowinda.googlecode.com/files/step2_lengthBias.zip
known recovery: http://gowinda.googlecode.com/files/step3_knownRecovery.zip

Validating Gowinda by comparing the results to GoMiner

As GoMiner does not correct for the gene length bias nor consider overlapping genes it was necessary to use a dataset not showing these two biases. We therefore (i) filtered for not-overlapping genes (ii) filtered for genes that have an associated GO term and (iii) introduced exactly 5 SNPs in each of the thus obtained genes. Subsequently we randomly picked 1000 SNPs and compared the p-values obtained with Gowinda to the p-values obtained with GoMiner (without FDR correction).

Obtain a set of genes that are not overlapping and have an associated GO term

First obtain a set of non-overlapping genes (gene IDs)

python ../scripts/get_nonoverlapping_geneids.py --gtf Flybase.gtf > nonoverlapping_genes.txt

Than filter this set for genes that have an associated GO term

python ../scripts/get_geneids_havinggocategory.py --go association_gominer.txt --genelist nonoverlapping_genes.txt > geneids_withgoterm_nooverlap.txt

Introduce five SNPs into each of the non-overlapping genes

python ../scripts/create_snps_for_genes.py --genelist geneids_withgoterm_nooverlap.txt --gtf Flybase.gtf > snps_5pgene.txt

Choose 1000 random SNPs

cat snps_5pgene.txt|perl -ne 'print if rand()<0.03'|head -1000 > rand_snps_1k.txt

Perform GO term analysis with Gowinda

/System/Library/Frameworks/JavaVM.framework/Versions/1.6.0/Commands/java -Xmx4g -jar /Users/robertkofler/dev/PopGenTools/gowinda/Gowinda.jar --snp-file snps_5pgene.txt  --candidate-snp-file rand_snps_1k.txt --gene-set-file association_gominer.txt --annotation-file Flybase.gtf --simulations 1000000 --min-significance 1 --gene-definition exon --threads 8 --output-file gowinda_res.txt --mode gene --min-genes 5

Perform GO term analysis with GoMiner

For the analysis with GoMiner we need two lists of genes (gene IDs). The first list with must contain the total set of genes and the second list must contain the subset of genes that should be tested for enrichment.

cat snps_5pgene.txt| awk '{print $3}'|sort |uniq > gominer_total.txt
cat rand_snps_1k.txt| awk '{print $3}'|sort |uniq > gominer_totest.txt

Subsequently use High-Troughput GoMiner: http://discover.nci.nih.gov/gominer/GoCommandWebInterface.jsp

Following the settings that we used with GoMiner

totalfile=gominer_total.txt
changedfile=gominer_totest.txt
datasource=FB
organism=7227
evidencecode=all
crossref=true
synonym=true
thresholdtype=BOTH
timeseriesthreshold=0.05
randomization=100
categorymin=5
cim=1
categorymax=10000
rootcategory=all
tf=0
email=xxx@xxx

Merge the results of Gowinda and GoMiner

From the GoMiner results you need to extract the file: gominer_totest.txt.change

cat gowinda_res.txt| awk '{print $1,$4}'|sort -k1,1 > merged/tm_gowinda.txt
cat GoMiner/gominer_totest.txt.change |awk 'NF>5{print $1,10^$5}'|perl -pe 's/_[^ ]+//' |sort -k1,1> merged/tm_gominer.txt
join -1 1 -2 1 tm_gominer.txt tm_gowinda.txt > merged_min_win.txt

The resulting file contains for every GO category (column 1) the p-value obtained with GoMiner (column 2) and Gowinda (column 3) and may easily be analyzed with R.

Results for unbiased analysis

With an FDR of <0.05 neither GoMiner nor Gowinda found a significant overrepresentation for any GO categories. The p-values (not FDR corrected) for the different GO categories obtained by Gowinda and GoMiner are highly correlated (Spearman's rank correlation; rho=0.9999005; p-value < 2.2e-16), see graph below:

Our results (including our random SNPs) for the comparision of Gowinda with GoMiner can be found here: http://gowinda.googlecode.com/files/step1_valGoMiner.zip

Validating whether Gowinda eliminates the gene length bias

In GWAS studies longer genes typically have more SNPs and thus also have a higher probability of containing a candidate SNP, which could lead to an overrepresentation of GO categories having longer genes. To test the extent of the bias of GWA datasets and whether Gowinda efficiently corrects for this bias, we introduced one SNP every 100bp into all genes (having a GO category). As overlapping genes may be an additional source of bias in GWA datesets, overlapping genes were not discarded in this analysis. Finally we randomly picked 1000 SNPs and compared the results obtained with GoMiner to the results obtained with Gowinda.

Extract the genes having an associated GO term

python ../scripts/extract_geneids_fromgoaassociation.py --go association_gominer.txt > total_genes.txt

Introduce one SNP every 100 bp

python ../scripts/create_length_biased_genes.py --gtf Flybase.gtf --genelist total_genes.txt > snps_lengthbiased.txt

Randomly draw 1000 SNPs

cat snps_lengthbiased.txt|perl -ne 'print if rand()<0.004'|head -1000> rand_1k_snps.txt

Perform GO term analysis with Gowinda

/System/Library/Frameworks/JavaVM.framework/Versions/1.6.0/Commands/java -Xmx4g -jar /Users/robertkofler/dev/PopGenTools/gowinda/Gowinda.jar --snp-file snps_lengthbiased.txt  --candidate-snp-file rand_1k_snps.txt --gene-set-file association_gominer.txt --annotation-file Flybase.gtf --simulations 1000000 --min-significance 1 --gene-definition exon --threads 8 --output-file gowinda_res.txt --mode gene --min-genes 5

Perform GO term analysis with GoMiner

First obtain the gene_ids from the randomly drawn SNPs

cat rand_1k_snps.txt|awk '{print $3}'|sort |uniq > subset_genes.txt

Than perform the analysis for GO term enrichment with GoMiner: http://discover.nci.nih.gov/gominer/htgm.jsp

We used the following parameters

totalfile=total_genes.txt
changedfile=subset_genes.txt
datasource=FB
organism=7227
evidencecode=all
crossref=true
synonym=true
thresholdtype=BOTH
timeseriesthreshold=0.05
randomization=100
categorymin=5
cim=1
categorymax=10000
rootcategory=all
tf=0
email=rokofler@gmail.com

Merge the results of Gowinda and GoMiner

From the GoMiner results you need to extract the file subset_genes.txt.change

cat gowinda_res.txt| awk '{print $1,$4}'|sort -k1,1 > merged/tm_gowinda.txt 
cat GoMiner/subset_genes.txt.change |awk 'NF>5{print $1,10^$5}'|perl -pe 's/_[^ ]+//' |sort -k1,1> merged/tm_gominer.txt
join -1 1 -2 1 tm_gominer.txt tm_gowinda.txt > merged_min_win.txt

Results for length biased analysis

Although we randomly drew SNPs and would thus not expect an overrepresentation of any GO term, GoMiner reports a significant overrepresentation for 341 GO terms (FDR <0.05). This demonstrates that the biases of GWA datasets substantially affect GO analysis. By contrast, Gowinda correctly reports that none of the GO terms show any significant overrepresentation (FDR <0.05). The correlation between the p-values obtained with Gowinda and GoMiner (Spearman's rank correlation; rho=0.5610065; p-value < 2.2e-16) is much worse as compared to the unbiased data set. The following graph shows the correlation of the p-values obtained with Gowinda and GoMiner using the length biased dataset.

[]

Our results of this analysis can be found here: http://gowinda.googlecode.com/files/step2_lengthBias.zip

Validating whether Gowinda identifies preselected GO categories

To validate that Gowinda correctly identifies overrepresentation of GO terms, we proceeded in two steps. First we randomly picked five GO categories with a small number of genes (between 5 and 10). We choose small GO categories in order to get conservative estimates of the reliability of Gowinda. We than introduced one candidate SNP for ever gene associated with these preselected GO categories. We added randomly drawn candidate SNPs from a length biased SNP dataset (see above) until a total of 1000 candidate SNPs was obtained. Finally we performed an analysis for gene set enrichment using Gowinda and checked whether the five randomly preselected GO categories correctly showed a significant overrepresentation.

Identify genes having an associated GO term

python ../scripts/extract_geneids_fromgoaassociation.py --go association_gominer.txt > total_genes.txt

Introduce one SNP every 100bp for genes with an associated GO term

python ../scripts/create_length_biased_genes.py --gtf Flybase.gtf --genelist total_genes.txt > snps_lengthbiased.txt

Randomly pick five small GO categories

We randomly picked five small GO categories having 5 - 10 genes.

python ../scripts/pick_random_gocats.py --input association_gominer.txt > random_gocategories.txt

In our example we picked the following categories, where the first column is the number of genes in this category:

cat random_gocategories.txt|awk '{print $2}' |sort |uniq -c
   5 GO:0007289
   6 GO:0009074
   5 GO:0030173
   5 GO:0040020
   8 GO:0046364

Create 1000 candidate SNPs

We than introduce candidate SNPs into the genes associated with the preselected GO categories and add randomly drawn candidate SNPs from the biased dataset until a total of 1000 candidate SNPs was obtained

python ../scripts/pick_random_snps_excepttargetlist.py --snp snps_lengthbiased.txt --genelist random_gocategories.txt > random_targetsnps.txt

Perform GO term analysis with Gowinda

/System/Library/Frameworks/JavaVM.framework/Versions/1.6.0/Commands/java -Xmx4g -jar /Users/robertkofler/dev/PopGenTools/gowinda/Gowinda.jar --snp-file snps_lengthbiased.txt --candidate-snp-file random_targetsnps.txt --gene-set-file association_gominer.txt --annotation-file Flybase.gtf --simulations 1000000 --min-significance 1 --gene-definition exon --threads 8 --output-file gowinda.res --mode gene --min-genes 1

Results for recovery of preselected GO categories

With an FDR <0.01 we identified 19 significantly enriched GO categories (see below). All five preselected GO categories were successfully identified (marked with an arrow). However we also found an additional 14 not targeted GO categories, which can be explained by the nesting of GO categories. For example the not preselected GO category GO:0046165 contains the genes cg32220,cg17725,cg8890,cg10688,cg3495,cg10924,cg8251,cg1516 which are all associated with the preselected GO category: GO:0046364

GO:0046165      1.278   9       0.0000010000    0.0002654000    9       14      14      alcohol_biosynthetic_process    cg32220,cg17725,cg10118,cg8890,cg10688,cg3495,cg10924,cg8251,cg1516
GO:0019319      0.714   7       0.0000010000    0.0002654000    7       7       7       hexose_biosynthetic_process     cg17725,cg8890,cg10688,cg3495,cg10924,cg8251,cg1516
=>GO:0009074      0.339   6       0.0000010000    0.0002654000    6       6       6       aromatic_amino_acid_family_catabolic_process    cg7399,cg1555,cg4779,cg9363,cg9362,cg2155
=>GO:0046364      0.740   8       0.0000010000    0.0002654000    8       8       8       monosaccharide_biosynthetic_process     cg32220,cg17725,cg8890,cg10688,cg3495,cg10924,cg8251,cg1516
=>GO:0007289      0.483   5       0.0000010000    0.0002654000    5       5       5       spermatid_nucleus_differentiation       cg3354,cg6998,cg5648,cg12284,cg8827
GO:0009225      0.152   4       0.0000020000    0.0003176667    4       4       4       nucleotide-sugar_metabolic_process      cg32220,cg8890,cg10688,cg3495
GO:0009226      0.152   4       0.0000020000    0.0003176667    4       4       4       nucleotide-sugar_biosynthetic_process   cg32220,cg8890,cg10688,cg3495
GO:0019439      0.464   6       0.0000020000    0.0003176667    6       7       7       aromatic_compound_catabolic_process     cg7399,cg1555,cg4779,cg9363,cg9362,cg2155
GO:0051445      0.966   7       0.0000020000    0.0003176667    7       9       9       regulation_of_meiotic_cell_cycle        cg18543,cg6513,cg9900,cg13584,cg4727,cg4336,cg7719
=>GO:0030173      0.517   5       0.0000040000    0.0005274545    5       5       5       integral_to_Golgi_membrane      cg10580,cg4871,cg10772,cg2448,cg12366
GO:0031228      0.517   5       0.0000040000    0.0005274545    5       5       5       intrinsic_to_Golgi_membrane     cg10580,cg4871,cg10772,cg2448,cg12366
GO:0006558      0.227   4       0.0000070000    0.0008063077    4       4       4       L-phenylalanine_metabolic_process       cg7399,cg4779,cg9363,cg9362
GO:0006559      0.227   4       0.0000070000    0.0008063077    4       4       4       L-phenylalanine_catabolic_process       cg7399,cg4779,cg9363,cg9362
=>GO:0040020      0.622   5       0.0000110000    0.0011997857    5       5       5       regulation_of_meiosis   cg9900,cg13584,cg4727,cg4336,cg7719
GO:0009072      1.411   8       0.0000230000    0.0024266667    8       18      18      aromatic_amino_acid_family_metabolic_process    cg7399,cg10118,cg1555,cg4779,cg17870,cg9363,cg9362,cg2155
GO:0034637      2.181   9       0.0000870000    0.0094014375    9       21      21      cellular_carbohydrate_biosynthetic_process      cg32220,cg17725,cg8890,cg9485,cg10688,cg3495,cg10924,cg8251,cg1516
GO:0006572      0.142   3       0.0000970000    0.0096698421    3       3       3       tyrosine_catabolic_process      cg4779,cg9363,cg9362
GO:0046395      3.068   11      0.0001050000    0.0096698421    11      38      38      carboxylic_acid_catabolic_process       cg7399,cg4586,cg1555,cg4779,cg3626,cg6638,cg9709,cg9527,cg9363,cg9362,cg2155
GO:0016054      3.068   11      0.0001050000    0.0096698421    11      38      38      organic_acid_catabolic_process  cg7399,cg4586,cg1555,cg4779,cg3626,cg6638,cg9709,cg9527,cg9363,cg9362,cg2155

Our results for this analysis can be found here: http://gowinda.googlecode.com/files/step3_knownRecovery.zip

Recent changes to Validation

Validation modified by Robert Kofler

Validation modified by Robert Kofler

Validation modified by Anonymous

Introduction

Files

Results for validating Gowinda

Validating Gowinda by comparing the results to GoMiner

Obtain a set of genes that are not overlapping and have an associated GO term

Introduce five SNPs into each of the non-overlapping genes

Choose 1000 random SNPs

Perform GO term analysis with Gowinda

Perform GO term analysis with GoMiner

Merge the results of Gowinda and GoMiner

Results for unbiased analysis

Validating whether Gowinda eliminates the gene length bias

Extract the genes having an associated GO term

Introduce one SNP every 100 bp

Randomly draw 1000 SNPs

Perform GO term analysis with Gowinda

Perform GO term analysis with GoMiner

Merge the results of Gowinda and GoMiner

Results for length biased analysis

Validating whether Gowinda identifies preselected GO categories

Identify genes having an associated GO term

Introduce one SNP every 100bp for genes with an associated GO term

Randomly pick five small GO categories

Create 1000 candidate SNPs

Perform GO term analysis with Gowinda

Results for recovery of preselected GO categories