ShinyOmics Report (25/07/2024)
This is a report generated by the cOmicsART application under version v0.1.0. Documentation on the user interface can be found here.
Data Selection
Info
DataInput - Uploaded Omic Type: Transcriptomics
The following data was used: DataMatrix.csv SampleAnno.csv entities annotation_showcase.csv
DataInput - The raw data dimensions are: 47643, 10
DataInput - Gene Annotation (SYMBOL and gene type) was added
DataInput - chosen Organism: Mouse genes (GRCm39)
DataSelection - The following selection was conducted:
DataSelection - Samples: DataSelection - based on: Organism: all
DataSelection - Entities: DataSelection - based on: Ensembl_ID: all
Publication Snippet
The data was uploaded to cOmicsART (v. v0.1.0) a webapp to perform explorative and statistical analysis with seamless integration to R (Seep et. al. 2024). The webapp is majorly built with the shiny package (v. 1.8.1.1) (Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2024)._shiny: Web Application Framework for R_. R package version 1.8.1.1,https://CRAN.R-project.org/package=shiny.). It is currently running on R (v. 4.2.0) (R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for StatisticalComputing, Vienna, Austria. https://www.R-project.org/.). Unless otherwise stated, all visulaizations were created using the ggplot2 package (v. 3.5.1) (Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN978-3-319-24277-4, https://ggplot2.tidyverse.org.). The Transcriptomics data was uploaded with the original dimensions of 47643 features and 10 samples. Gene annotation was added using the Mouse genes (GRCm39)mart from Ensembl implemented within the biomaRt package (v. 2.54.1) (Durinck S, Spellman P, Birney E, Huber W (2009). “Mapping identifiers for the integration of genomic datasetswith the R/Bioconductor package biomaRt.” Nature Protocols, 4, 1184-1191.Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W (2005). “BioMart and Bioconductor: apowerful link between biological databases and microarray data analysis.” Bioinformatics, 21, 3439-3440.). No sample selection was performed. No entitie selection was performed.
Pre Processing
Info
PreProcessing - Alaways done: removal of all entities which are constant over all samples
PreProcessing - Preprocessing procedure -standard (depending only on omics-type): Remove anything which row Count <= 10
PreProcessing - Preprocessing procedure -specific (user-chosen): vst_DESeq~Treatment
PreProcessing - The resulting dimensions are: 16125, 10
Publication Snippet
For the transcriptomics data, DESeq2 was used for normalization and VST transformation applied for visualisation of the normalized data (not for statistical testing)(v. 1.38.3) (Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data withDESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8https://doi.org/10.1186/s13059-014-0550-8.). The formula for analysis was ~~ Treatment~ Treatment. The resulting dataset had 16125 features and 10 samples.
Sample correlation
Info
SampleCorrelation - The correlation method used was: pearson
SampleCorrelation - The heatmap samples were colored after Treatment
SampleCorrelation - 
Publication Snippet
The correlation between samples was calculated using the pearson method. The resulting correlation matrix was visualized using the pheatmap package(v. 1.0.12) (Kolde R (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12,https://CRAN.R-project.org/package=pheatmap.). The correlation matrix was clustered with the complete linkage method using correlation distance.
PCA
Info
PCA - The PCA was computed on the entire dataset.
PCA - The following PCA-plot is colored after: Treatment
PCA - 
Publication Snippet
Principal component analysis (PCA) was performed on the centered and scaled data, implemented within the stats package (v.4.2.0) (R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for StatisticalComputing, Vienna, Austria. https://www.R-project.org/.).
PCA ScreePlot
Info
ScreePlot - The scree Plot shows the Variance explained per Principle Component
ScreePlot - 
Publication Snippet
The scree plot was generated to visualize the proportion of variance explained by each principal component.
PCA Loadings
Info
LoadingsPCA - Loadings plot for Principle Component: PC1
LoadingsPCA - Showing the the highest 10 and the lowest 10 Loadings
LoadingsPCA - The corresponding Loadingsplot - 
Publication Snippet
The top 10 positive loadings and the top 10 negative loadings were seleceted to assess an entities’ impact on the principal components
PCA Loadings Matrix
Info
PCALoadingsMatrix - Loadings plot for Principle Components 1 till PC1
PCALoadingsMatrix - Showing all entities which have an absolute Loadings value of at least0.05
PCALoadingsMatrix - The corresponding Loadings
Matrix plot - 
Publication Snippet
The loadings matrix was created by taking all absolute loading values higher than 0.05 into account for the first 1The resulting matrix allows a visual assessment of the impact of each entity accross multiple principal components.
Single Entitie
Info
Single Entitie - The following single entitie was plotted:
Single Entitie - Values shown are: data input
Single Entitie - Values are grouped for all levels within: ()
Single Entitie - Test for differences:
Single Entitie - pairwise tested
Single Entitie - 
Publication Snippet
The expression of, Ppbp, was plotted. The values shown represent the preprocessed data. If the a group of entities is selected through their shared annotation, the median value is used as representative for those entities for the respectice sampleValues are grouped for all levels within the condition: Treatment). A test for differences was performed using the t.test method. Pairwise tests were performed. The dotted line represents the global mean. Boxplots are only shown if there are more than 3 samples per group. The plot was extended to include and visualize the statistical results with the R packge ggpubr(v. 0.6.0) (Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.6.0,https://CRAN.R-project.org/package=ggpubr.).
Single Entitie
Info
Single Entitie - The following single entitie was plotted:
Single Entitie - Values shown are: data input
Single Entitie - Values are grouped for all levels within: ()
Single Entitie - Test for differences:
Single Entitie - pairwise tested
Single Entitie - 
Publication Snippet
The expression of, Osm, was plotted. The values shown represent the preprocessed data. If the a group of entities is selected through their shared annotation, the median value is used as representative for those entities for the respectice sampleValues are grouped for all levels within the condition: Treatment). A test for differences was performed using the t.test method. Pairwise tests were performed. The dotted line represents the global mean. Boxplots are only shown if there are more than 3 samples per group. The plot was extended to include and visualize the statistical results with the R packge ggpubr(v. 0.6.0) (Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.6.0,https://CRAN.R-project.org/package=ggpubr.).
Single Entitie
Info
Single Entitie - The following single entitie was plotted:
Single Entitie - Values shown are: data input
Single Entitie - Values are grouped for all levels within: ()
Single Entitie - Test for differences:
Single Entitie - pairwise tested
Single Entitie - 
Publication Snippet
The expression of, Fos, was plotted. The values shown represent the preprocessed data. If the a group of entities is selected through their shared annotation, the median value is used as representative for those entities for the respectice sampleValues are grouped for all levels within the condition: Treatment). A test for differences was performed using the t.test method. Pairwise tests were performed. The dotted line represents the global mean. Boxplots are only shown if there are more than 3 samples per group. The plot was extended to include and visualize the statistical results with the R packge ggpubr(v. 0.6.0) (Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.6.0,https://CRAN.R-project.org/package=ggpubr.).
Single Entitie
Info
Single Entitie - The following single entitie was plotted:
Single Entitie - Values shown are: data input
Single Entitie - Values are grouped for all levels within: ()
Single Entitie - Test for differences:
Single Entitie - pairwise tested
Single Entitie - 
Publication Snippet
The expression of, Dusp1, was plotted. The values shown represent the preprocessed data. If the a group of entities is selected through their shared annotation, the median value is used as representative for those entities for the respectice sampleValues are grouped for all levels within the condition: Treatment). A test for differences was performed using the t.test method. Pairwise tests were performed. The dotted line represents the global mean. Boxplots are only shown if there are more than 3 samples per group. The plot was extended to include and visualize the statistical results with the R packge ggpubr(v. 0.6.0) (Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.6.0,https://CRAN.R-project.org/package=ggpubr.).
Single Entitie
Info
Single Entitie - The following single entitie was plotted:
Single Entitie - Values shown are: data input
Single Entitie - Values are grouped for all levels within: ()
Single Entitie - Test for differences:
Single Entitie - pairwise tested
Single Entitie - 
Publication Snippet
The expression of, Ppbp, was plotted. The values shown represent the preprocessed data. If the a group of entities is selected through their shared annotation, the median value is used as representative for those entities for the respectice sampleValues are grouped for all levels within the condition: Stimulation_Treatment). A test for differences was performed using the t.test method. Pairwise tests were performed. The dotted line represents the global mean. Boxplots are only shown if there are more than 3 samples per group. The plot was extended to include and visualize the statistical results with the R packge ggpubr(v. 0.6.0) (Kassambara A (2023). ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.6.0,https://CRAN.R-project.org/package=ggpubr.).
Significance analysis - Volcano
Info
VOLCANO - Underlying Volcano Comparison: HSD vs NSD
VOLCANO - 
Publication Snippet
Differential expression analysis was performed using the DESeq2 package (v. 1.38.3) (Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data withDESeq2.” Genome Biology, 15, 550. doi:10.1186/s13059-014-0550-8https://doi.org/10.1186/s13059-014-0550-8.). The reported adjusted p-values were adjusted by . The significance level was set to 0.05. There were a total of 1 comparison done, precisely: HSD:NSD, from which all were visualized within the set comparison. For each comparison, their set of entities of interest ( based on the Significant p-values) were visualized. Note, that multiple testing correction is done for each comparison separately.
HEATMAP
Info
HEATMAP - The heatmap was constructed based on the following row selection: Select based on Annotation
HEATMAP - The rows were subsetted based on Ensembl_ID :ENSMUSG00000044786,ENSMUSG00000052684,ENSMUSG00000053560,ENSMUSG00000020423,ENSMUSG00000052837,ENSMUSG00000021250,ENSMUSG00000038418,ENSMUSG00000021123,ENSMUSG00000031431,ENSMUSG00000024190
HEATMAP - The selection was reduced to the top entities. Total Number: 20
HEATMAP - Note that the order depends on Select based on Annotation
HEATMAP - The heatmap samples were colored after Treatment
HEATMAP - The heatmap entities were colored after None
HEATMAP - columns were clustered based on: euclidean-distance & agglomeration method: complete
HEATMAP - rows were clustered based on: euclidean-distance & agglomeration method: complete
HEATMAP - 
Publication Snippet
The heatmap shows all entities which Ensembl_ID is part of the set of ENSMUSG00000044786,ENSMUSG00000052684,ENSMUSG00000053560,ENSMUSG00000020423,ENSMUSG00000052837,ENSMUSG00000021250,ENSMUSG00000038418,ENSMUSG00000021123,ENSMUSG00000031431,ENSMUSG00000024190. The heatmap samples were colored after Treatment. The columns were clustered based on euclidean-distance with complete-linkage. The rows were clustered based on euclidean-distance with complete-linkage. The rows were scaled to visualise relative difference. The heatmap was created using the pheatmap package(v. 1.0.12) (Kolde R (2019). pheatmap: Pretty Heatmaps. R package version 1.0.12,https://CRAN.R-project.org/package=pheatmap.).
Enrichment
Info
Enrichment general The analysed gene set size: 10
Enrichment general Chosen Organism (needed for translation): Mouse genes (GRCm39)
Enrichment general The following sets to check an enrichment: Hallmarks,KEGG,GO_CC
ORA Overrepresentation analysis was perfomed.
ORA The genes were taken from: LFC
ORA The adj. p-value threshold was set to 0.05, whereby mutliple testing correction was : Benjamini-Hochberg
Publication Snippet
The analysis included a gene set size of 10. When necassary the provided IDs were translated to entrezID for , utilizing the R package biomaRt (v. 2.54.1) (Durinck S, Spellman P, Birney E, Huber W (2009). “Mapping identifiers for the integration of genomic datasetswith the R/Bioconductor package biomaRt.” Nature Protocols, 4, 1184-1191.Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W (2005). “BioMart and Bioconductor: apowerful link between biological databases and microarray data analysis.” Bioinformatics, 21, 3439-3440.). The predefined sets to test enrichment for were: Hallmarks, KEGG, GO_CC. Over-Representation Analysis (ORA) was performed as implemented in the R package clusterProfilfer (v. 4.6.2) (Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu x, Liu S, Bo X, Yu G (2021).“clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.” The Innovation, 2(3),100141. doi:10.1016/j.xinn.2021.100141 https://doi.org/10.1016/j.xinn.2021.100141.Yu G, Wang L, Han Y, He Q (2012). “clusterProfiler: an R package for comparing biological themes among geneclusters.” OMICS: A Journal of Integrative Biology, 16(5), 284-287. doi:10.1089/omi.2011.0118https://doi.org/10.1089/omi.2011.0118.).ORA identifies whether predefined sets of genes are overrepresented among the differentially expressed genes. It compares the proportion of genes of interest within the dataset to what would be expected by chance within a so-called universe. Here the universe was chosen as the set of genes present in the genes that were present after pre-processing. Resulting in a total of 16125 genes. The genes were obtained from LFC. The adjusted p-value threshold was set to 0.05, with multiple testing correction applied using Benjamini-Hochberg.
Enrichment results
Hallmarks_ENRICHMENT
- The number of found enriched terms (p.adj <0.05): 4
Hallmarks ENRICHMENT - 
- The top 5 terms are the following (sorted by adj. p.val)
|
ID |
Description |
GeneRatio |
BgRatio |
pvalue |
p.adjust |
qvalue |
geneID |
Count |
|---|---|---|---|---|---|---|---|---|
|
HALLMARK_TNFA_SIGNALING_VIA_NFKB |
HALLMARK_TNFA_SIGNALING_VIA_NFKB |
8/8 |
179/3573 |
0.0000000 |
0.0000000 |
0.0000000 |
12227/14281/19252/13653/22695/16476/16477/15936 |
8 |
|
HALLMARK_HYPOXIA |
HALLMARK_HYPOXIA |
4/8 |
163/3573 |
0.0002532 |
0.0020254 |
0.0013325 |
14281/19252/22695/16476 |
4 |
|
HALLMARK_UV_RESPONSE_UP |
HALLMARK_UV_RESPONSE_UP |
3/8 |
135/3573 |
0.0025697 |
0.0137052 |
0.0090166 |
12227/14281/16477 |
3 |
|
HALLMARK_P53_PATHWAY |
HALLMARK_P53_PATHWAY |
3/8 |
182/3573 |
0.0060208 |
0.0240831 |
0.0158441 |
12227/14281/16476 |
3 |
|
HALLMARK_APOPTOSIS |
HALLMARK_APOPTOSIS |
2/8 |
139/3573 |
0.0360682 |
0.1039880 |
0.0684131 |
12227/16476 |
2 |
Enrichment
Info
Enrichment general The analysed gene set size: 16125
Enrichment general Chosen Organism (needed for translation): Mouse genes (GRCm39)
Enrichment general The following sets to check an enrichment: Hallmarks,KEGG,GO_BP
GSEA Gene Set enrichment analysis was perfomed.
GSEA The genes were sorted by: LFC
GSEA Calculation based on Treatment: HSD vs. NSD
GSEA The adj. p-value threshold was set to 0.05, whereby mutliple testing correction was : Benjamini-Hochberg
Publication Snippet
The analysis included a gene set size of 16125. When necassary the provided IDs were translated to entrezID for , utilizing the R package biomaRt (v. 2.54.1) (Durinck S, Spellman P, Birney E, Huber W (2009). “Mapping identifiers for the integration of genomic datasetswith the R/Bioconductor package biomaRt.” Nature Protocols, 4, 1184-1191.Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W (2005). “BioMart and Bioconductor: apowerful link between biological databases and microarray data analysis.” Bioinformatics, 21, 3439-3440.). The predefined sets to test enrichment for were: Hallmarks, KEGG, GO_BP. Gene Set Enrichment Analysis (GSEA) was performed as implemented in the R package clusterProfilfer (v. 4.6.2) (Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, Fu x, Liu S, Bo X, Yu G (2021).“clusterProfiler 4.0: A universal enrichment tool for interpreting omics data.” The Innovation, 2(3),100141. doi:10.1016/j.xinn.2021.100141 https://doi.org/10.1016/j.xinn.2021.100141.Yu G, Wang L, Han Y, He Q (2012). “clusterProfiler: an R package for comparing biological themes among geneclusters.” OMICS: A Journal of Integrative Biology, 16(5), 284-287. doi:10.1089/omi.2011.0118https://doi.org/10.1089/omi.2011.0118.). GSEA evaluates whether predefined sets of genes show statistically significant differences in expression between two biological states. It considers the entire ranked list of genes, thus providing insights into pathways that might be enriched even if individual genes do not reach significance. The genes were sorted by LFC, whereby the calculation was done for Treatment for HSD vs. NSD. The adjusted p-value threshold was set to 0.05, with multiple testing correction applied using Benjamini-Hochberg.
Enrichment results
Hallmarks_ENRICHMENT
- The number of found enriched terms (p.adj <0.05): 6
Hallmarks ENRICHMENT - 
- The top 5 terms are the following (sorted by adj. p.val)
|
ID |
Description |
setSize |
enrichmentScore |
NES |
pvalue |
p.adjust |
qvalue |
rank |
leading_edge |
core_enrichment |
|---|---|---|---|---|---|---|---|---|---|---|
|
HALLMARK_TNFA_SIGNALING_VIA_NFKB |
HALLMARK_TNFA_SIGNALING_VIA_NFKB |
178 |
0.5550914 |
2.633560 |
0.0000000 |
0.0000000 |
0.0000000 |
1253 |
tags=26%, list=8%, signal=24% |
19252/14282/13653/14281/18626/16476/15370/22695/17691/12227/12608/15936/16477/11852/16176/16598/211770/19225/20620/16193/230738/13654/21664/17872/56706/20310/18035/227659/15205/12515/50723/18578/21815/23872/16197/16160/23849/230734/16601/17118/17210/54446/12044/21930/12522/20971 |
|
HALLMARK_OXIDATIVE_PHOSPHORYLATION |
HALLMARK_OXIDATIVE_PHOSPHORYLATION |
197 |
-0.3660331 |
-1.791360 |
0.0000064 |
0.0001596 |
0.0001311 |
3687 |
tags=36%, list=23%, signal=28% |
18597/12369/64655/66052/30055/16922/11973/51798/68194/67834/16828/13063/73834/12859/17448/66091/30059/67264/11740/225887/22272/66152/71679/12866/68198/12034/57423/12861/30057/11974/11957/67126/69833/269951/54405/11958/18105/228033/11946/14297/72900/110323/11950/66495/17992/66046/15526/28185/28080/66377/11739/66290/214952/231086/110446/15926/12868/17713/66335/12856/66142/407785/66525/67530/69772/109672/68375/56451/17993/11655/67942 |
|
HALLMARK_MTORC1_SIGNALING |
HALLMARK_MTORC1_SIGNALING |
196 |
-0.3607102 |
-1.764161 |
0.0000232 |
0.0003872 |
0.0003179 |
3905 |
tags=41%, list=24%, signal=32% |
17768/107272/18107/67895/56088/18655/56418/16993/12317/17252/20775/66249/53333/107513/21753/16828/20135/74117/15277/16414/73834/13361/56480/14884/19324/15357/11938/104112/67890/74205/56305/74185/23996/68275/15528/26941/20878/21991/22256/208715/192193/68801/68278/13595/70699/72157/20893/15452/14385/59029/78925/13806/14433/665563/15526/103963/93692/54353/22433/56529/11639/12450/22027/235293/16011/15926/12330/20491/27407/667034/14381/64136/26432/27966/74754/112407/107476/18817/20525/20867/18451 |
|
HALLMARK_MYC_TARGETS_V1 |
HALLMARK_MYC_TARGETS_V1 |
197 |
-0.3381885 |
-1.655089 |
0.0000990 |
0.0012379 |
0.0010164 |
4465 |
tags=38%, list=28%, signal=28% |
53607/67204/14208/20588/12462/18148/12464/231872/20382/13690/70247/20174/18655/67097/22630/78655/381760/57296/16828/17220/14113/13204/433702/105148/12566/27041/26445/12261/19385/110074/11777/230908/26440/103573/20383/23996/15528/19988/12034/74326/26446/16898/18972/233870/17218/108062/18263/20639/23983/15452/59029/11792/19166/22327/28185/12428/99138/11431/19384/12237/22195/19826/56150/106344/12330/50995/68092/22627/27966/20641/110639/85305/68011/56351/22171 |
|
HALLMARK_COAGULATION |
HALLMARK_COAGULATION |
87 |
-0.4145408 |
-1.788772 |
0.0003339 |
0.0033390 |
0.0027415 |
3089 |
tags=37%, list=19%, signal=30% |
12759/17385/227753/14058/17395/11502/54368/18787/223864/16416/229445/14723/11812/12334/22388/16784/16952/76453/108078/12258/11843/19128/18441/12371/76467/20196/21859/18792/67603/14066/17390/56744 |
KEGG_ENRICHMENT
- The number of found enriched terms (p.adj <0.05): 4
KEGG ENRICHMENT - 
- The top 5 terms are the following (sorted by adj. p.val)
|
ID |
Description |
setSize |
enrichmentScore |
NES |
pvalue |
p.adjust |
qvalue |
rank |
leading_edge |
core_enrichment |
|---|---|---|---|---|---|---|---|---|---|---|
|
KEGG_PARKINSONS_DISEASE |
KEGG_PARKINSONS_DISEASE |
108 |
-0.4935969 |
-2.253620 |
0.0000000 |
0.0000024 |
0.0000023 |
4959 |
tags=53%, list=31%, signal=37% |
17722/333182/22273/11949/11951/67680/230075/67130/17708/66594/67273/67003/66108/66052/68194/13063/12859/66091/67264/11740/225887/78330/22272/66152/71679/12866/68198/20617/12861/11957/67126/54405/228033/11946/72900/110323/11950/66495/17992/66046/12367/28080/66377/11739/12862/22195/12868/56791/66142/66218/407785/67738/67530/68375/12371/17993/67942 |
|
KEGG_OXIDATIVE_PHOSPHORYLATION |
KEGG_OXIDATIVE_PHOSPHORYLATION |
109 |
-0.4895433 |
-2.240331 |
0.0000001 |
0.0000061 |
0.0000057 |
5437 |
tags=62%, list=34%, signal=42% |
12858/12864/68197/66916/11966/76429/66237/17722/333182/22273/11949/11951/67680/230075/67130/17708/66594/67273/67003/66144/66108/67895/11972/66052/11973/68194/73834/12859/66091/67264/225887/78330/22272/66152/71679/12866/68198/57423/12861/11974/11957/67126/69875/54405/11958/228033/11946/72900/110323/11950/66495/17992/66046/28080/66377/11964/12862/66290/12868/66335/12856/66142/66218/407785/67530/68375/17993/67942 |
|
KEGG_ALZHEIMERS_DISEASE |
KEGG_ALZHEIMERS_DISEASE |
143 |
-0.4065408 |
-1.925576 |
0.0000021 |
0.0000931 |
0.0000880 |
4738 |
tags=52%, list=29%, signal=37% |
19164/12314/11949/11951/67680/230075/67130/12015/15925/17708/66594/18795/67273/67003/19056/66108/16956/12369/19058/66052/68194/78943/13063/12859/11785/66091/67264/11938/225887/78330/12370/22272/66152/71679/12313/12866/68198/20617/14811/12861/12315/11957/12568/67126/59287/54405/228033/11946/72900/110323/14433/12334/11950/66495/17992/66046/12367/14812/12122/28080/18798/20192/66377/12862/11820/12868/66142/66218/407785/54652/67530/68375/12371/17993/67942 |
|
KEGG_HUNTINGTONS_DISEASE |
KEGG_HUNTINGTONS_DISEASE |
154 |
-0.4001709 |
-1.914644 |
0.0000021 |
0.0000931 |
0.0000880 |
4988 |
tags=51%, list=31%, signal=35% |
15194/69654/333182/237336/22273/11949/11773/11951/67680/230075/67130/17708/66594/18795/67273/67003/217864/66108/13191/66052/68194/13385/20466/13063/12859/20021/74325/66091/67264/21780/11740/327954/54152/225887/78330/12370/22272/66152/71679/12866/68198/12064/12861/69241/11957/67126/69833/54405/105000/228033/11946/72900/110323/11950/66495/17992/26427/66046/12367/14812/28080/18798/208647/66377/11739/12862/67710/12868/66142/66218/407785/67738/67530/68375/12371/17993/67942/21817 |
|
KEGG_PROTEIN_EXPORT |
KEGG_PROTEIN_EXPORT |
22 |
-0.5704451 |
-1.822860 |
0.0030073 |
0.1058553 |
0.1000307 |
2880 |
tags=45%, list=18%, signal=37% |
20813/67398/69019/66384/56529/66212/53421/66541/66624/20335 |
GO_BP_ENRICHMENT
- The number of found enriched terms (p.adj <0.05): 24
GO_BP ENRICHMENT - 
- The top 5 terms are the following (sorted by adj. p.val)
|
ID |
Description |
setSize |
enrichmentScore |
NES |
pvalue |
p.adjust |
qvalue |
rank |
leading_edge |
core_enrichment |
|---|---|---|---|---|---|---|---|---|---|---|
|
GOBP_POSITIVE_REGULATION_OF_ACUTE_INFLAMMATORY_RESPONSE |
GOBP_POSITIVE_REGULATION_OF_ACUTE_INFLAMMATORY_RESPONSE |
19 |
0.7484267 |
2.234386 |
1.45e-05 |
0.0153045 |
0.014562 |
251 |
tags=26%, list=2%, signal=26% |
18413/16176/19225/16193/11501 |
|
GOBP_SKELETAL_MUSCLE_CELL_DIFFERENTIATION |
GOBP_SKELETAL_MUSCLE_CELL_DIFFERENTIATION |
43 |
0.6058505 |
2.230243 |
7.60e-06 |
0.0153045 |
0.014562 |
1575 |
tags=26%, list=10%, signal=23% |
13653/14281/15370/12227/13654/17260/17261/13813/77578/13207/224640 |
|
GOBP_PROTON_TRANSMEMBRANE_TRANSPORT |
GOBP_PROTON_TRANSMEMBRANE_TRANSPORT |
107 |
-0.4253468 |
-1.899171 |
1.71e-05 |
0.0153045 |
0.014562 |
3291 |
tags=40%, list=20%, signal=32% |
11973/68073/68055/12859/57738/66114/11740/68020/66152/71679/26941/212933/12034/57423/12861/56632/11974/11957/236794/67126/83429/11958/228033/11946/331004/110323/105675/11950/269356/17992/57816/28080/11964/11739/12862/66290/12868/66335/12856/66142/109672/67942/22229 |
|
GOBP_NUCLEOSIDE_PHOSPHATE_BIOSYNTHETIC_PROCESS |
GOBP_NUCLEOSIDE_PHOSPHATE_BIOSYNTHETIC_PROCESS |
197 |
-0.3555194 |
-1.753945 |
1.07e-05 |
0.0153045 |
0.014562 |
3328 |
tags=35%, list=21%, signal=28% |
30963/14913/68073/20135/68055/71743/66114/269614/237823/67054/54391/110074/11637/104112/353172/74205/11534/71679/71562/20617/11566/68870/192185/57423/56632/106564/11957/67126/68801/22169/73836/11958/228033/11946/15452/74559/13806/665563/11950/108147/19063/28080/80914/11964/70456/56348/69225/11639/11821/67993/70789/110446/319945/11541/75456/236900/667034/171567/266645/110639/107476/85305/223646/15930/54195/22171/67942/22271/79059 |
|
GOBP_CHROMATIN_ORGANIZATION |
GOBP_CHROMATIN_ORGANIZATION |
488 |
0.3046313 |
1.600807 |
4.80e-06 |
0.0153045 |
0.014562 |
3731 |
tags=31%, list=23%, signal=24% |
16598/224836/360198/100683/216848/50708/252838/228790/108829/116848/407823/320790/22589/192285/277250/53892/97908/214162/231051/214899/18602/15184/218850/622675/73251/381022/20230/244059/328572/104248/67772/72895/68094/21652/20926/108155/212712/71458/14055/192195/67155/110958/53890/20185/68142/18193/235134/103554/75751/94246/224826/15081/233532/494448/57261/107976/235626/56335/107932/207165/238247/320538/67246/57749/20591/233490/66867/69188/14462/232811/20613/268564/22289/170787/69386/17257/216850/231386/75410/12005/233545/114642/66505/234135/75560/233875/217578/19651/270058/242466/68968/223828/52609/110147/74016/13018/93760/17954/229675/53325/17345/17450/320795/193796/208043/101612/225876/320713/237339/224903/244349/54343/75605/20664/230936/69612/233900/76719/20918/227867/52808/70645/12418/73247/104263/73884/17192/15260/225888/105787/68845/59035/21415/19820/71330/71389/214133/68703/12648/81601/14534/109275/170644/320376/16969/19650/20184/319156/671535 |