In Yelab2020/FPSOmics: Ferroptosis Score and associated multi-Omics
1. Introduction
FPSOmics is an R package to calculate ferroptosis score (FPS) and multidimensional ferroptosis-associated molecular signatures.
FPSOmics designed a novel score model based on ferroptosis-related genes(FRGs)[@Zhou2020] using single sample gene set enrichment analysis (ssGSEA)[@Hanzelmann2013]. This package also include a propensity score matching (PSM) algorithm[@Ye2019], which appropriately control the effects of clinical confounding factors, to calculate ferroptosis-associated molecular signatures.
The full FPSOmics documentation is available in the package. To reach the user’s Guide, install the FPSOmics package and load it into an R session by library (FPSOmics). And they can get help by help (FPS), help (FPSsurvival), help (FPSmultiOmicsSig) and help (FPSmultiOmicsSigEnrich) to see documentation of each function. Also description of each function is including in docs/FPSOmics_0.1.0.pdf.
2. Installation and requirement
Before installing FPSOmics, please properly install all dependencies
#library(magrittr)
#library(survival)
#library(survminer)
#library(ggplot2)
#library(grid)
#library(gridExtra)
#library(gtable)
#library(org.Hs.eg.db)
#library(KEGG.db)
#library(fgsea)
#library(enrichplot)
#library(clusterProfiler)
#library(magrittr)
#library(dummies)
#library(formula.tools)
#library(doMC)
#library(foreach)
#library(gdata)
#library(gtools)
#library(gmodels)
#library(MASS)
#library(gplots)
#library(formula.tools)
#library(Hmisc)
#library(Matrix)
#library(dplyr)
Installation of FPSOmics
You can install FPSOmics.tar.gz from local path and directly from github.
#devtools::install_github('Yelab2020/FPSOmics')
#install.packages('FPSOmics.tar.gz", repos = NULL, type = "source")
#library(FPSOmics)
3. Functional modules
3.1 Calculate ferroptosis score
We firstly filtered out to 32 core ferroptosis-related genes (FRGs) from genelist in FerrDb[@Zhou2020].Then, we calculate enrichment score (ES) of ferroptosis-promote genes (pro-FRGs) and ferroptosis-inhibit genes(anti-FRGs) using single sample gene set enrichment analysis (ssGSEA)[@Hanzelmann2013], then ferroptosis score (FPS) was defined by the ES of pro-FRGs minus anti-FRGs.
print('FPS')
#data(m1_input_mRNA,package='FPSOmics')
#FPS_score=FPSOmics::FPS(m1_input_mRNA)
#head(FPS_score)
3.2 Evaluate prognostic significance
We firstly calculate enrichment score (ES) of ferroptosis-promote genes (pro-FRGs) and ferroptosis-inhibit genes (anti-FRGs) using single sample gene set enrichment analysis (ssGSEA), then ferroptosis score (FPS) was defined by the ES of pro-FRGs minus anti-FRGs. Then Kaplan-Meier survival analyses between high-FPS and low-FPS were also assessed using log rank test.
print('FPS-survival')
#data(m1_input_mRNA,package='FPSOmics')#load mRNA matrix
#data(m2_input_survival_data,package='FPSOmics') #load survival time
#FPS_survival_result=FPSOmics::FPSsurvival(m1_input_mRNA,m2_input_survival_data)
3.3 Multidimensional ferroptosis-associated molecular signatures
A propensity score matching (PSM) algorithm[@Ye2019], which appropriately control the effects of clinical confounding factors, was used to calculate ferroptosis-associated molecular signatures between high and low ferroptosis score group.
print('FPS-multiOmics')
#data(m3_input_Clinical,package='FPSOmics')
#data(m3_input_FPS_score,package='FPSOmics')
#data(m3_input_mRNA,package='FPSOmics')
#PSM_result=FPSOmics::FPSmultiOmicsSig(m3_input_Clinical,m3_input_FPS_score,m3_input_mRNA)
3.4 Function enrichment
Study function of ferroptosis-associated molecular signatures and the differences of different ferroptosis status in cancer hallmarks, KEGG pathway by Gene Set Enrichment Analysis (GSEA)[@Sergushichev2016;@Subramanian2005] and GO, KEGG enrichment by clusterprofile[@Yu2012].
print('FPS-multiOmics-function')
#data(m4_input_gs_kegg,package='FPSOmics')
#data(m4_input_PSM,package='FPSOmics')
#enrichment_result=FPSOmics::FPSmultiOmicsSigEnrich(m4_input_PSM,m4_input_gs_kegg,'temp_folder')
4. Summary
FPS provides four function modules which allows users to analysis of ferroptosis score and their-related multi-omics molecular signatures. There may still be gaps in the toolkit, but we'll work on more features later. If you have any suggestions or questions, please feel free to contact us (yihe0902@163.com or yudong123@sjtu.edu.cn).
5. Session information
sessionInfo()
6. Citing FPSOmics
If FPSOmics R package is used in your research, please cite: Multi-omics characterization and therapeutic liability of ferroptosis in melanoma
7. REFERENCES
Yelab2020/FPSOmics documentation built on May 14, 2022, 8:59 p.m.
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1. Introduction
FPSOmics is an R package to calculate ferroptosis score (FPS) and multidimensional ferroptosis-associated molecular signatures. FPSOmics designed a novel score model based on ferroptosis-related genes(FRGs)[@Zhou2020] using single sample gene set enrichment analysis (ssGSEA)[@Hanzelmann2013]. This package also include a propensity score matching (PSM) algorithm[@Ye2019], which appropriately control the effects of clinical confounding factors, to calculate ferroptosis-associated molecular signatures. The full FPSOmics documentation is available in the package. To reach the user’s Guide, install the FPSOmics package and load it into an R session by library (FPSOmics). And they can get help by help (FPS), help (FPSsurvival), help (FPSmultiOmicsSig) and help (FPSmultiOmicsSigEnrich) to see documentation of each function. Also description of each function is including in docs/FPSOmics_0.1.0.pdf.
2. Installation and requirement
Before installing FPSOmics, please properly install all dependencies
#library(magrittr) #library(survival) #library(survminer) #library(ggplot2) #library(grid) #library(gridExtra) #library(gtable) #library(org.Hs.eg.db) #library(KEGG.db) #library(fgsea) #library(enrichplot) #library(clusterProfiler) #library(magrittr) #library(dummies) #library(formula.tools) #library(doMC) #library(foreach) #library(gdata) #library(gtools) #library(gmodels) #library(MASS) #library(gplots) #library(formula.tools) #library(Hmisc) #library(Matrix) #library(dplyr)
Installation of FPSOmics You can install FPSOmics.tar.gz from local path and directly from github.
#devtools::install_github('Yelab2020/FPSOmics') #install.packages('FPSOmics.tar.gz", repos = NULL, type = "source") #library(FPSOmics)
3. Functional modules
3.1 Calculate ferroptosis score
We firstly filtered out to 32 core ferroptosis-related genes (FRGs) from genelist in FerrDb[@Zhou2020].Then, we calculate enrichment score (ES) of ferroptosis-promote genes (pro-FRGs) and ferroptosis-inhibit genes(anti-FRGs) using single sample gene set enrichment analysis (ssGSEA)[@Hanzelmann2013], then ferroptosis score (FPS) was defined by the ES of pro-FRGs minus anti-FRGs.
print('FPS') #data(m1_input_mRNA,package='FPSOmics') #FPS_score=FPSOmics::FPS(m1_input_mRNA) #head(FPS_score)
3.2 Evaluate prognostic significance
We firstly calculate enrichment score (ES) of ferroptosis-promote genes (pro-FRGs) and ferroptosis-inhibit genes (anti-FRGs) using single sample gene set enrichment analysis (ssGSEA), then ferroptosis score (FPS) was defined by the ES of pro-FRGs minus anti-FRGs. Then Kaplan-Meier survival analyses between high-FPS and low-FPS were also assessed using log rank test.
print('FPS-survival') #data(m1_input_mRNA,package='FPSOmics')#load mRNA matrix #data(m2_input_survival_data,package='FPSOmics') #load survival time #FPS_survival_result=FPSOmics::FPSsurvival(m1_input_mRNA,m2_input_survival_data)
3.3 Multidimensional ferroptosis-associated molecular signatures
A propensity score matching (PSM) algorithm[@Ye2019], which appropriately control the effects of clinical confounding factors, was used to calculate ferroptosis-associated molecular signatures between high and low ferroptosis score group.
print('FPS-multiOmics') #data(m3_input_Clinical,package='FPSOmics') #data(m3_input_FPS_score,package='FPSOmics') #data(m3_input_mRNA,package='FPSOmics') #PSM_result=FPSOmics::FPSmultiOmicsSig(m3_input_Clinical,m3_input_FPS_score,m3_input_mRNA)
3.4 Function enrichment
Study function of ferroptosis-associated molecular signatures and the differences of different ferroptosis status in cancer hallmarks, KEGG pathway by Gene Set Enrichment Analysis (GSEA)[@Sergushichev2016;@Subramanian2005] and GO, KEGG enrichment by clusterprofile[@Yu2012].
print('FPS-multiOmics-function') #data(m4_input_gs_kegg,package='FPSOmics') #data(m4_input_PSM,package='FPSOmics') #enrichment_result=FPSOmics::FPSmultiOmicsSigEnrich(m4_input_PSM,m4_input_gs_kegg,'temp_folder')
4. Summary
FPS provides four function modules which allows users to analysis of ferroptosis score and their-related multi-omics molecular signatures. There may still be gaps in the toolkit, but we'll work on more features later. If you have any suggestions or questions, please feel free to contact us (yihe0902@163.com or yudong123@sjtu.edu.cn).
5. Session information
sessionInfo()
6. Citing FPSOmics
If FPSOmics R package is used in your research, please cite: Multi-omics characterization and therapeutic liability of ferroptosis in melanoma
7. REFERENCES
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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