SDY–over–Control

Settings

	Setting
Analysis:	NGS two group analysis
Reference:	Sparus_aurata/Ensembl/fSpaAur1.1/Annotation/Release_113-2025-01-13
Feature level:	gene
Data Column Used:	matchCounts
Method:	deseq2
Comparison:	SDY–over–Control
Normalization:	DESeq2_MedianRatio
Log2 signal threshold:	3.322
Linear signal threshold:	10

Result summary

	Number
reference:	Sparus_aurata/Ensembl/fSpaAur1.1/Annotation/Release_113-2025-01-13
Number of features:	24752
Number of features with counts above threshold:	12974

Number of significants by p-value and fold-change

	#significants	FDR	fc >= 1	fc >= 1.5	fc >= 2	fc >= 3	fc >= 4	fc >= 8	fc >= 10
p < 0.1	2260	0.574000	2260	1001	339	110	53	12	7
p < 0.05	1448	0.447400	1448	811	298	99	50	12	7
p < 0.01	525	0.245800	525	410	194	76	41	11	6
p < 0.001	139	0.092410	139	130	82	40	24	8	5
p < 1e-04	40	0.032150	40	40	34	20	13	4	2
p < 1e-05	17	0.006481	17	17	15	10	7	2	1

Full result table in xlsx format for opening with a spreadsheet program (e.g. Excel).

result–SDY–over–Control.xlsx

Live Report and Visualizations

Inspection of significant genes

Between-group comparison

	Number
P-value threshold:	p <= 0.01
Log ratio threshold:	log ratio >= 0.5
Number of significant genes:	455

Subsequent plots highlight significant genes in blue.

Interactive table of significant genes

Interactive comparison plot (64-bit Chrome or Safari is recommended for best performance!)

Inspection of significant genes (Advanced plots)

Intra-group Comparison: Control

Intra-group Comparison: SDY

Clustering of significant features

	Number
Significance threshold:	0.01
log2 Ratio threshold:	0.5
Number of significant features:	455

Cluster plot

GO categories of feature clusters
	BP	MF	CC
Cluster 1	Cluster-BP-1.html	Cluster-MF-1.html	Cluster-CC-1.html
Cluster 2
Cluster 3	Cluster-BP-3.html	Cluster-MF-3.html	Cluster-CC-3.html
Cluster 4
Cluster 5		Cluster-MF-5.html
Cluster 6
Note:
Cluster font color corresponds to the row colors in the heatmap plot.

GO cluster tables

Enrichr

Description

Enrichr is a web server which collects in one website many different databases that can be interrogated using gene lists. It takes as input a subset of genes passing certain p-value/fold-change thresholds (i.e., differentially expressed genes). It is recommended to search in one go various databases such as cell type, transcription factors, mutations, diseases. Further details: Visit the enrichr bioconductor page

## 
## Sparus_aurata is not supported by Enrichr.

Overrepresentation Analysis (ORA)

Overview

Description

The Over Representation Analysis (ORA), also known as the hypergeometric test, gives an estimate of whether a set of selected genes is enriched for genes in specific Gene Ontology categories. It takes as input a subset of genes passing certain p-value/fold-change thresholds (i.e., differentially expressed genes). It is recommended when the difference between groups is large (e.g., 500+ genes above p-value/fold-change thresholds). There are many implementations of this test and in SUSHI we use the R package clusterProfiler. Further details: Visit the clusterProfiler bioconductor page

Cut Offs:

Gene Selection: p <= 0.01, Up: log2 ratio >0/Down: log2 ratio < 0
Candidate Terms: fdr <= 0.05

Hypergeometric Over-representation Test
	Number of Significant Terms
BP: upGenes	1
BP: downGenes	1
BP: bothGenes	1
MF: upGenes	3
MF: downGenes	0
MF: bothGenes	2
CC: upGenes	1
CC: downGenes	0
CC: bothGenes	1

BP

upGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0006418	tRNA aminoacylation for protein translation	14/173	35/6436	8.624064e-14	7.157973e-12	cars1/eprs1/mars1/tars1/vars1/hars/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1

downGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0035162	embryonic hemopoiesis	4/99	19/6436	0.0001710021	0.008721109	alas2/tal1/slc4a1a/sptb

bothGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0006418	tRNA aminoacylation for protein translation	14/272	35/6436	4.35287e-11	4.65757e-09	cars1/eprs1/mars1/tars1/vars1/hars/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1

MF

upGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0004812	aminoacyl-tRNA ligase activity	15/227	39/8465	2.389568e-14	2.150611e-12	cars1/eprs1/mars1/tars1/vars1/hars/aars1/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1
GO:0000166	nucleotide binding	16/227	95/8465	3.553403e-09	1.599031e-07	cars1/eprs1/MX1/mars1/tars1/vars1/aars1/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1/ube2nb
GO:0140662	ATP-dependent protein folding chaperone	5/227	24/8465	3.716689e-04	1.115007e-02	hsc70/hspa9/hspd1/hspa4a/hsp90aa1.2

downGenes

No significant GO terms detected.

bothGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0004812	aminoacyl-tRNA ligase activity	15/362	39/8465	2.164482e-11	2.467509e-09	cars1/eprs1/mars1/tars1/vars1/hars/aars1/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1
GO:0000166	nucleotide binding	16/362	95/8465	2.255011e-06	1.285356e-04	cars1/eprs1/MX1/mars1/tars1/vars1/aars1/sars2/qars1/LARS1/rars1/yars1/gars1/NARS1/WARS1/ube2nb

CC

upGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0005737	cytoplasm	18/84	232/3383	9.326661e-06	0.0001678799	eprs1/parn/MX1/psma5/tars1/hars/pfkla/aars1/qars1/rars1/dpp3//ppa1b/eif4e1c/gars1/galk1/ddit4/aldh18a1

downGenes

No significant GO terms detected.

bothGenes

ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	geneName
GO:0005737	cytoplasm	20/138	232/3383	0.000942829	0.02074224	eprs1/parn/MX1/psma5/tars1/hars/pfkla/aars1/qars1/rars1/dpp3//ppa1b/eif4e1c/gars1/galk1/ddit4/aldh18a1/ALDH1L1/amph

Gene set enrichment analysis

Overview

Description

Gene Set Enrichment Analysis (GSEA) calculates an enrichment score for each annotation category (e.g., those in GO BP) by screening all the genes from a differential expression analysis and their associated fold-changes. It does not require a pre-selection based on p-value/fold-change. It is recommended when the difference between groups is small (i.e., applying thresholds would result in very few genes selected) or when combining results from different experiments. Similar to the ORA test, we use the package clusterProfiler to perform GSEA analysis in SUSHI. Further details: Visit the clusterProfiler bioconductor page

Cut Off:

Candidate Terms: fdr <= 0.05

Gene Ranking:

all present genes are sorted by log2 ratio

BP

## Picking joint bandwidth of 0.137

ID	Description	setSize	enrichmentScore	NES	pvalue	p.adjust	geneName
GO:0006418	tRNA aminoacylation for protein translation	35	0.6871014	1.957973	9.016757e-05	0.02064837	gars1/WARS1/rars1/vars1/yars1/cars1/tars1/hars/NARS1/mars1/LARS1/rars1/eprs1/iars1/sars1/qars1/sars2

MF

No significant GO terms detected.

CC

No significant GO terms detected.

MetaCore

## Error in .Hub_get1(x[i], force = force, verbose = verbose): no records found for the given index

## Error in `geneAnno[, c("gene_id", "entrez_id")]`:
## ! Can't subset columns that don't exist.
## ✖ Column `entrez_id` doesn't exist.

Metacore

Metacore is a commercial software that performs gene-set based analysis using a manually curated, proprietary database. It takes as input a subset of genes passing certain p-value/fold-change thresholds (i.e., differentially expressed genes). It is recommended for the analysis of human, mouse, and rat data. It is best used to gain drug/disease-related information and comprehensive pathway map figures.

Registration

Using MetaCore requires an account. One can be obtained by emailing sequencing@fgcz.ethz.ch.

How to use Metacore: quick start

Right Click and Save File to Download Expression File for MetaCore (p <= 0.01)
Go to the MetaCore website
Upload the Expression File to MetaCore, selecting three columns: Gene ID, log ratio, and p-value
Select Pathway Maps from the One-click Analysis tab.

Important notes

When uploading the data into MetaCore, select three columns:

A gene identifier, pick one of:
- Ensembl Gene IDs – ENSEMBL IDs
- Entrez Gene IDs – EntrezGene (LocusLink) IDs
Log fold changes of the genes – log ratio
P values

You must ensure that the data type drop-down menu above each column is correct.

Select ---ignore--- from the drop down for all other columns in the table that are not to be used.

Please refer to the FGCZ Wiki for a more comprehensive guide to using MetaCore.

MetaCore has numerous tutorials online, including a useful YouTube page: https://www.youtube.com/watch?v=_J_ViIw9wIM&list=PL2tDZtDsVy5xqUibWp-mfl9xuZP4Bhi-v&index=2

MetaCore Compatibility

MetaCore currently has full pathway support for three core species: Human; mouse, and; rat. Additionally, it can use Entrez IDs as orthologs to analyse a series of non-core species:

Cow
Chimpanzee
Dog
Zebra fish
Chicken
Fly
Mosquito
Worm
Arabidopsis
Rice
Blast of rice (fungal species)
Macaca mulatta
Mold
Bread mold
Candida sphaerica
Fission yeast
Baker’s yeast

Technical bias

We define 4 gene sets

high GC: the 5% of the genes with the highest GC content
low GC: the 5% of the genes with the lowest GC content
long genes: the 5% of the genes with the biggest length
short genes: the 5% of the genes with the smalles length

And we test if the up- or down-regulated genes are associated with one of those gene sets. If there is a significant association, some of the significant genes are potentially false positives due to a technical bias.

Tests where the association p-value is below 0.001 are highlighted in red. The column “overlapping/total genes” shows the number of overlapping genes and the total number of genes in that category.

Association test: 293 up-regulated and 162 down-regulated genes
	overlapping/total genes	odds ratio	p-value
low GC – Up-regulation	17/647	1.178	0.2942
low GC – Down-regulation	4/647	0.479	0.9641
high GC – Up-regulation	14/647	0.955	0.6047
high GC – Down-regulation	11/647	1.395	0.1857
short genes – Up-regulation	20/648	1.406	0.0973
short genes – Down-regulation	11/648	1.392	0.1870
long genes – Up-regulation	6/649	0.391	0.9972
long genes – Down-regulation	11/649	1.390	0.1883

Input dataset

SessionInfo

## ezRun tag: ed29b51d935a7281b59ff7ca5e23d187e8f449d3 
## ezRun github link: https://github.com/uzh/ezRun/tree/ed29b51d935a7281b59ff7ca5e23d187e8f449d3 
##  
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Debian GNU/Linux 12 (bookworm)
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Zurich
## tzcode source: system (glibc)
## 
## attached base packages:
##  [1] parallel  tools     stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] AnnotationHub_3.14.0        BiocFileCache_2.14.0       
##  [3] dbplyr_2.5.0                gplots_3.2.0               
##  [5] annotate_1.84.0             XML_3.99-0.17              
##  [7] GOstats_2.72.0              graph_1.84.0               
##  [9] Category_2.72.0             pheatmap_1.0.12            
## [11] RColorBrewer_1.1-3          ggrepel_0.9.6              
## [13] htmltools_0.5.8.1           SingleCellExperiment_1.28.1
## [15] Matrix_1.7-1                tidyselect_1.2.1           
## [17] writexl_1.5.1               DT_0.33                    
## [19] DOSE_4.0.0                  BiocParallel_1.40.0        
## [21] readxl_1.4.3                htmlwidgets_1.6.4          
## [23] webshot_0.5.5               plotly_4.10.4              
## [25] kableExtra_1.4.0            knitr_1.49                 
## [27] qs2_0.1.4                   clusterProfiler_4.14.4     
## [29] GO.db_3.20.0                AnnotationDbi_1.68.0       
## [31] DESeq2_1.46.0               rtracklayer_1.66.0         
## [33] SummarizedExperiment_1.36.0 Biobase_2.66.0             
## [35] MatrixGenerics_1.18.0       matrixStats_1.4.1          
## [37] ezRun_3.20.1                lubridate_1.9.4            
## [39] forcats_1.0.0               stringr_1.5.1              
## [41] dplyr_1.1.4                 purrr_1.0.2                
## [43] readr_2.1.5                 tidyr_1.3.1                
## [45] tibble_3.2.1                ggplot2_3.5.1              
## [47] tidyverse_2.0.0             GenomicRanges_1.58.0       
## [49] Biostrings_2.74.0           GenomeInfoDb_1.42.1        
## [51] XVector_0.46.0              IRanges_2.40.1             
## [53] S4Vectors_0.44.0            BiocGenerics_0.52.0        
## [55] data.table_1.16.4          
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.4.2            BiocIO_1.16.0            filelock_1.0.3          
##   [4] bitops_1.0-9             ggplotify_0.1.2          BiasedUrn_2.0.12        
##   [7] R.oo_1.27.0              cellranger_1.1.0         httr2_1.0.7             
##  [10] lifecycle_1.0.4          lattice_0.22-6           vroom_1.6.5             
##  [13] crosstalk_1.2.1          magrittr_2.0.3           openxlsx_4.2.7.1        
##  [16] sass_0.4.9               rmarkdown_2.29           jquerylib_0.1.4         
##  [19] yaml_2.3.10              ggtangle_0.0.5           zip_2.3.1               
##  [22] cowplot_1.1.3            DBI_1.2.3                abind_1.4-8             
##  [25] zlibbioc_1.52.0          R.utils_2.12.3           RCurl_1.98-1.16         
##  [28] yulab.utils_0.1.8        rappdirs_0.3.3           GenomeInfoDbData_1.2.13 
##  [31] enrichplot_1.26.3        AnnotationForge_1.48.0   tidytree_0.4.6          
##  [34] genefilter_1.88.0        svglite_2.1.3            codetools_0.2-20        
##  [37] DelayedArray_0.32.0      xml2_1.3.6               aplot_0.2.3             
##  [40] UCSC.utils_1.2.0         farver_2.1.2             goseq_1.58.0            
##  [43] GenomicAlignments_1.42.0 jsonlite_1.8.9           ggridges_0.5.6          
##  [46] survival_3.7-0           systemfonts_1.1.0        progress_1.2.3          
##  [49] treeio_1.30.0            Rcpp_1.0.13.6            glue_1.8.0              
##  [52] SparseArray_1.6.0        mgcv_1.9-1               geneLenDataBase_1.42.0  
##  [55] xfun_0.49                qvalue_2.38.0            withr_3.0.2             
##  [58] BiocManager_1.30.25      fastmap_1.2.0            caTools_1.18.3          
##  [61] digest_0.6.37            mime_0.12                timechange_0.3.0        
##  [64] R6_2.5.1                 gridGraphics_0.5-1       colorspace_2.1-1        
##  [67] gtools_3.9.5             biomaRt_2.62.0           RSQLite_2.3.9           
##  [70] R.methodsS3_1.8.2        generics_0.1.3           prettyunits_1.2.0       
##  [73] httr_1.4.7               S4Arrays_1.6.0           pkgconfig_2.0.3         
##  [76] gtable_0.3.6             blob_1.2.4               fgsea_1.32.0            
##  [79] RBGL_1.82.0              GSEABase_1.68.0          scales_1.3.0            
##  [82] png_0.1-8                ggfun_0.1.8              rstudioapi_0.17.1       
##  [85] tzdb_0.4.0               reshape2_1.4.4           rjson_0.2.23            
##  [88] nlme_3.1-166             curl_6.0.1               cachem_1.1.0            
##  [91] BiocVersion_3.20.0       KernSmooth_2.23-24       restfulr_0.0.15         
##  [94] pillar_1.10.0            grid_4.4.2               vctrs_0.6.5             
##  [97] stringfish_0.16.0        xtable_1.8-4             Rgraphviz_2.50.0        
## [100] evaluate_1.0.1           GenomicFeatures_1.58.0   cli_3.6.3               
## [103] locfit_1.5-9.10          compiler_4.4.2           Rsamtools_2.22.0        
## [106] rlang_1.1.4              crayon_1.5.3             labeling_0.4.3          
## [109] plyr_1.8.9               fs_1.6.5                 stringi_1.8.4           
## [112] viridisLite_0.4.2        txdbmaker_1.2.1          munsell_0.5.1           
## [115] lazyeval_0.2.2           GOSemSim_2.32.0          hms_1.1.3               
## [118] patchwork_1.3.0          bit64_4.5.2              KEGGREST_1.46.0         
## [121] igraph_2.1.2             memoise_2.0.1            RcppParallel_5.1.9      
## [124] bslib_0.8.0              ggtree_3.14.0            fastmatch_1.1-4         
## [127] bit_4.5.0.1              ape_5.8                  gson_0.1.0