README.md
In omtarful/pcoskbR:
pcoskbR
pcoskbR provides an R interface to access datasets related to PCOS in PCOSKBR2. As well as, data mining tools for comorbidity prediction and identification of enriched pathways and hub genes.
Installation
You can install the developer version from github.
devtools::install_github("omtarful/pcoskbR")
Intoduction
PCOSKB is a manually curated knowledgebase on PCOS. PCOSKBR allows us. Information on associated genes, SNPs, diseases, gene ontologies and pathways along with supporting reference literature is collated and integrated in PCOSKB. Various tools are embedded in the database such as Comorbidity analysis for estimating the risk of diseases to co-occur with PCOS; Network analysis for identifying enriched pathways and hub genes and Venn analysis for finding common and unique genes, pathways and ontologies. The r "pcoskbR" package, provides an R interface to PCOSKB. This vignette details pcoskbR functionalities and provides a number of example usecases that can be used as the basis for specifying your own queries.
Selecting page and filters in pcoskbR
The R tool allows you to find information on genes, SNPs, diseases, gene ontologies and pathways associated with PCOS. The function listPages()will display information you can browse.
library(pcoskbR)
pages = listPages()
The function displays what kind of information associated to PCOS we can query in the database. The function listFilters() lists the filters you can apply to a given page.
gene_filters = listFilters("Genes")
gene_filters
#> [1] "all" "Manually curated" "Expression study" "Other sources"
Now we can retrieve information on PCOS associated genes using the browse() function, using above filters.
gene_dataset = browse(page = "Genes", filter = "Manually curated")
head(gene_dataset)
#> Gene Symbol Entrez ID Aliases
#> row ABCA1 19 ABC-1, ABC1, CERP, HDLCQTL13, HDLDT1, HPALP1, TGD
#> row.1 ACACA 31 ACAC, ACACAD, ACC, ACC1, ACCA
#> row.2 ACE 1636 ACE1, CD143, DCP, DCP1
#> row.3 ACTA2 59 ACTSA
#> row.4 ACTB 60 BRWS1, PS1TP5BP1
#> row.5 ACTG1 71 ACT, ACTG, DFNA20, DFNA26, HEL-176
#> Gene Name Chromosomal Location
#> row ATP binding cassette subfamily A member 1 9q31.1
#> row.1 Acetyl-CoA carboxylase alpha 17q12
#> row.2 Angiotensin I converting enzyme 17q23.3
#> row.3 Actin alpha 2, smooth muscle 10q23.31
#> row.4 Actin beta 7p22.1
#> row.5 Actin gamma 1 17q25.3
#> Record type
#> row Manually curated
#> row.1 Manually curated
#> row.2 Manually curated
#> row.3 Manually curated
#> row.4 Manually curated
#> row.5 Manually curated
Tools available for analysis
Comorbidity Analysis
The comorbidity Analysis tool allows us to check the comorbidity for PCOS and one or more diseases. We can call it using the function generateComorbidityAnalysis(). This function accepts a containing one or more diseases and the algorithm to perform the analysis. We can check a list of the available algorithms using the listCAlgorithms() function. For more details on how are scores are calculated refer to .
listCAlgorithms()
#> [1] "Shared genes" "Uniqueness of shared genes"
#> [3] "Shared ontologies" "Network-based seperation"
In this example we will use the algorithm based on shared genes.
generateComorbidityAnalysis(disease_list = c("Anemia", "Kawasaki disease"), algorithm = "Shared genes")
#> Warning: Removed 1 rows containing missing values (geom_text).
#> Anemia Kawasaki disease
#> PCOS 5.328597 0.8880995
#> Anemia NA 3.3333333
The function returns a matrix with scores in each cell indicating the risk of comorbidity between two diseases and generates a heatmap with the risks.
Network Analysis
Disease-Disease Network
This tools allows us to illustrate a disease-disease network through shared genes. It can be called using the function getNetworkAnalysis(). It returns a dataframe with all input diseases and associated genes and displays a network of diseases where size of the node shows the size of a node is proportional to the number of genes associated with the disease and the width of each edge is proportional to the number the shared genes.
getNetworkAnalysis(disease_list = c("Cardiovascular Abnormalities","Xerotic keratitis", "Anemia",
"Breast Cancer"), dataset = "Genes")
If this your first time using , you might wonder how to find diseases associated with PCOS. The function listDiseases() can be used to see all diseases associated with PCOS in the database.
listDiseases(dataset = "Genes", disease_group = "Renal Disorder")
#> [1] "Albuminuria" "Allanson Pantzar McLeod syndrome"
#> [3] "Alport Syndrome" "Ascites"
#> [5] "Atypical Hemolytic Uremic Syndrome" "Azotemia"
#> [7] "Balkan Nephropathy" "Bladder Neck Obstruction"
#> [9] "CAKUT" "Diabetic Kidney Disease"
#> [11] "Diabetic Nephropathy" "Glomerular Hyalinosis"
#> [13] "Glomerular Hypertrophy" "Glomerulonephritis"
#> [15] "Glomerulopathy" "Glomerulosclerosis"
#> [17] "Hematuria" "Hemolytic-Uremic Syndrome"
#> [19] "Hydronephrosis" "Hyperuricemic Nephropathy"
#> [21] "Hypospadias" "Interstitial Cystitis"
#> [23] "Kidney Calculi" "Kidney Failure"
#> [25] "Kidney Insufficiency" "Kidney Tubular Necrosis"
#> [27] "Nephritis" "Nephritis, Interstitial"
#> [29] "Nephrolithiasis" "Nephropathy"
#> [31] "Nephrosis" "Nephrotic Syndrome"
#> [33] "Oliguria" "Overactive Bladder"
#> [35] "Oxalosis" "Painful Bladder Syndrome"
#> [37] "Polycystic Kidney Diseases" "Renal Fibrosis"
#> [39] "Renal Osteodystrophy" "Uremia"
#> [41] "Ureteral Calculi" "Urinary Bladder Diseases"
The argument can either have "Genes" or "miRNAs" as value. To see the list of disease groups that can be used as input, you can use tbe 'listDiseaseGroup()' function. Default values are "Genes" and "all" for the and arguments.
listDiseaseGroup(dataset = "Genes")
#> [1] "all"
#> [2] "Blood Disorders"
#> [3] "Cardiovascular Diseases"
#> [4] "Congenital, Hereditary, and Neonatal Diseases and Abnormalities"
#> [5] "Digestive System Diseases"
#> [6] "Ear Or Mastoid Diseases"
#> [7] "Endocrine System Diseases"
#> [8] "Eye Diseases"
#> [9] "Immune System Diseases"
#> [10] "Musculoskeletal Diseases"
#> [11] "Neoplasms"
#> [12] "Nervous System Diseases"
#> [13] "Nutritional and Metabolic Diseases"
#> [14] "Psychiatric/Brain disorders"
#> [15] "Renal Disorder"
#> [16] "Reproductive disorders"
#> [17] "Respiratory Tract Diseases"
#> [18] "Skin and Connective Tissue Diseases"
This list of disease groups can be used as input in the function.
Pathway Analysis
Getting enriched pathways
Explain about use of enriched pathways. The pathway analysis tool gives a of enriched pathways for selected diseases. We call it using the generatePathwayAnalysis() function. It accepts , and as arguments. Database can either be "KEGG" or "Reactome".
pathways.df = PathwayAnalysis(disease_list = c("Anemia"),
dataset = "Genes",
database = "KEGG")
head(pathways.df)
#> Pathway name
#> row.1...1 Phospholipase D signaling pathway
#> row.2...2 Neuroactive ligand-receptor interaction
#> row.3...3 Adrenergic signaling in cardiomyocytes
#> row.4...4 Vascular smooth muscle contraction
#> row.5...5 Renin-angiotensin system
#> row.6...6 Renin secretion
#> Pathway genes associated with PCOS and the selected diseases
#> row.1...1 AGT, PDGFRA
#> row.2...2 AGT, PRL, PTH
#> row.3...3 AGT
#> row.4...4 AGT
#> row.5...5 AGT, REN
#> row.6...6 AGT, REN
#> Pathway genes associated with PCOS
#> row.1...1 AKT3, AGT, EGFR, AKT1, AKT2, CXCL8, INS, INSR, PDGFRA, PIK3CA, PIK3CG, PIK3R1, PLCB2, PLCB3, MAPK1, MAPK3
#> row.2...2 CGA, UCN3, ADM, CRH, ADRB2, ADRB3, DRD2, AGT, APLNR, EDN1, FSHB, FSHR, GCG, GH1, GHR, GHRH, GIP, GLP1R, GNRH1, GNRHR, NR3C1, HTR2C, IAPP, INSL3, KISS1, KNG1, LEP, LEPR, LHB, LHCGR, MTNR1A, MTNR1B, NPY, OXT, GHRL, POMC, PRL, PYY, PTH, RLN1, RLN2, SST, TACR3, C3, CALCA, CCK, APLN, UCN2
#> row.3...3 AKT3, MAPK14, ADRB2, AGT, AKT1, AKT2, GNAQ, KCNQ1, ATF4, PIK3CG, PLCB2, PLCB3, MAPK1, MAPK3
#> row.4...4 ADM, AGT, EDN1, GNAQ, MYH11, NPPB, PLCB2, PLCB3, MAPK1, MAPK3, ACTA2, ACTG2, CALCA
#> row.5...5 ACE, AGT, REN
#> row.6...6 ADRB2, ADRB3, ACE, AGT, EDN1, GNAQ, PDE3B, PLCB2, PLCB3, REN
#> Hypergeometric probability
#> row.1...1 0.159732685532648
#> row.2...2 0.23363353260246
#> row.3...3 0.371785240224444
#> row.4...4 0.364445361612939
#> row.5...5 0.00783718583187695
#> row.6...6 0.0819318218889266
This function returns a list of enriched pathways. Enriched pathways are identifed based on hypergeometric distribution with the threshold p value set as 0.05 (gene dataset) and 0.001 (miRNA dataset) based on the data size.
Visualizing enriched pathways
Output of the generatePathwayAnalysis() is used as input for the viewNetwork() function. This functions allows us to visualize the pathways as a network.
viewNetwork(pathways.df)
Each pathway is represented as a node and is connected to other pathways in the network based on common genes or miRNAs. Te thickness of the edge is proportional to the number of shared genes or miRNAs. In this example there are 151 nodes. We can also filter the data.frame to just obtain a network with enriched pathways for example.
#filter dataframe to only obtain enriched pathways
enriched_pathways.df = pathways.df[which(pathways.df$`Hypergeometric probability` <= 0.005),]
viewNetwork(enriched_pathways.df)
Gene Network Analysis
This tool allows us to generate hypothesis about disease targets based on network properties. Experimentally validated interactions from STRING v11 were used for creating gene interaction networks for enriched pathways. Critical genes in these pathways were identifed based on network topological properties such as degree, closeness centrality, and betweenness centrality calculated using igraph package in R. You can call it using the getGeneNetworkAnalysis() function.
gene_tbl.df = getGeneNetworkAnalysis(disease_list = c("Psychomotor Disorders", "Psychosexual Disorders" , "Pubertal Disorder"),
database = "Reactome")
#> Submitting with 'Add'
Check PCOSKB for details on how bottleneck genes and hub genes are chosen.
Visualizing interactions
We can also visualize all gene-gene interactions using viewInteractions() function. It has one argument and the value is a gene Symbol.
viewInteractions("INS")
Venn Analysis
This tool can be used to illustrate the unique and/or common genes, pathways and ontologies for 2 or more (up to 6) diseases. The function getVennAnalysis() is used for that purpose. Function arguments a vector of disease of up to 6 diseases. There is alos the options argument that can be either "Genes", "Pathways" or
getVennAnalysis(disease_list = c("PCOS", "Bipolar Disorder", "Mental Depression", "Schizophrenia", "Autistic Disorder", "Alzheimer Disease" ), option = "Genes")
#> Warning: Ignoring unknown aesthetics: text
Object will render the graph locally in your web browser or in the RStudio viewer.
This outputs an interactive web-based Venn-Diagram diagram created using ggVennDiagram and rendered using plotly.
omtarful/pcoskbR documentation built on March 20, 2022, 8:22 a.m.
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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.
pcoskbR
pcoskbR provides an R interface to access datasets related to PCOS in PCOSKBR2. As well as, data mining tools for comorbidity prediction and identification of enriched pathways and hub genes.
Installation
You can install the developer version from github.
devtools::install_github("omtarful/pcoskbR")
Intoduction
PCOSKB is a manually curated knowledgebase on PCOS. PCOSKBR allows us. Information on associated genes, SNPs, diseases, gene ontologies and pathways along with supporting reference literature is collated and integrated in PCOSKB. Various tools are embedded in the database such as Comorbidity analysis for estimating the risk of diseases to co-occur with PCOS; Network analysis for identifying enriched pathways and hub genes and Venn analysis for finding common and unique genes, pathways and ontologies. The r "pcoskbR" package, provides an R interface to PCOSKB. This vignette details pcoskbR functionalities and provides a number of example usecases that can be used as the basis for specifying your own queries.
Selecting page and filters in pcoskbR
The R tool allows you to find information on genes, SNPs, diseases, gene ontologies and pathways associated with PCOS. The function listPages()will display information you can browse.
library(pcoskbR)
pages = listPages()
The function displays what kind of information associated to PCOS we can query in the database. The function listFilters() lists the filters you can apply to a given page.
gene_filters = listFilters("Genes")
gene_filters
#> [1] "all" "Manually curated" "Expression study" "Other sources"
Now we can retrieve information on PCOS associated genes using the browse() function, using above filters.
gene_dataset = browse(page = "Genes", filter = "Manually curated")
head(gene_dataset)
#> Gene Symbol Entrez ID Aliases
#> row ABCA1 19 ABC-1, ABC1, CERP, HDLCQTL13, HDLDT1, HPALP1, TGD
#> row.1 ACACA 31 ACAC, ACACAD, ACC, ACC1, ACCA
#> row.2 ACE 1636 ACE1, CD143, DCP, DCP1
#> row.3 ACTA2 59 ACTSA
#> row.4 ACTB 60 BRWS1, PS1TP5BP1
#> row.5 ACTG1 71 ACT, ACTG, DFNA20, DFNA26, HEL-176
#> Gene Name Chromosomal Location
#> row ATP binding cassette subfamily A member 1 9q31.1
#> row.1 Acetyl-CoA carboxylase alpha 17q12
#> row.2 Angiotensin I converting enzyme 17q23.3
#> row.3 Actin alpha 2, smooth muscle 10q23.31
#> row.4 Actin beta 7p22.1
#> row.5 Actin gamma 1 17q25.3
#> Record type
#> row Manually curated
#> row.1 Manually curated
#> row.2 Manually curated
#> row.3 Manually curated
#> row.4 Manually curated
#> row.5 Manually curated
Tools available for analysis
Comorbidity Analysis
The comorbidity Analysis tool allows us to check the comorbidity for PCOS and one or more diseases. We can call it using the function generateComorbidityAnalysis(). This function accepts a containing one or more diseases and the algorithm to perform the analysis. We can check a list of the available algorithms using the listCAlgorithms() function. For more details on how are scores are calculated refer to .
listCAlgorithms()
#> [1] "Shared genes" "Uniqueness of shared genes"
#> [3] "Shared ontologies" "Network-based seperation"
In this example we will use the algorithm based on shared genes.
generateComorbidityAnalysis(disease_list = c("Anemia", "Kawasaki disease"), algorithm = "Shared genes")
#> Warning: Removed 1 rows containing missing values (geom_text).
#> Anemia Kawasaki disease
#> PCOS 5.328597 0.8880995
#> Anemia NA 3.3333333
The function returns a matrix with scores in each cell indicating the risk of comorbidity between two diseases and generates a heatmap with the risks.
Network Analysis
Disease-Disease Network
This tools allows us to illustrate a disease-disease network through shared genes. It can be called using the function getNetworkAnalysis(). It returns a dataframe with all input diseases and associated genes and displays a network of diseases where size of the node shows the size of a node is proportional to the number of genes associated with the disease and the width of each edge is proportional to the number the shared genes.
getNetworkAnalysis(disease_list = c("Cardiovascular Abnormalities","Xerotic keratitis", "Anemia",
"Breast Cancer"), dataset = "Genes")
If this your first time using , you might wonder how to find diseases associated with PCOS. The function listDiseases() can be used to see all diseases associated with PCOS in the database.
listDiseases(dataset = "Genes", disease_group = "Renal Disorder")
#> [1] "Albuminuria" "Allanson Pantzar McLeod syndrome"
#> [3] "Alport Syndrome" "Ascites"
#> [5] "Atypical Hemolytic Uremic Syndrome" "Azotemia"
#> [7] "Balkan Nephropathy" "Bladder Neck Obstruction"
#> [9] "CAKUT" "Diabetic Kidney Disease"
#> [11] "Diabetic Nephropathy" "Glomerular Hyalinosis"
#> [13] "Glomerular Hypertrophy" "Glomerulonephritis"
#> [15] "Glomerulopathy" "Glomerulosclerosis"
#> [17] "Hematuria" "Hemolytic-Uremic Syndrome"
#> [19] "Hydronephrosis" "Hyperuricemic Nephropathy"
#> [21] "Hypospadias" "Interstitial Cystitis"
#> [23] "Kidney Calculi" "Kidney Failure"
#> [25] "Kidney Insufficiency" "Kidney Tubular Necrosis"
#> [27] "Nephritis" "Nephritis, Interstitial"
#> [29] "Nephrolithiasis" "Nephropathy"
#> [31] "Nephrosis" "Nephrotic Syndrome"
#> [33] "Oliguria" "Overactive Bladder"
#> [35] "Oxalosis" "Painful Bladder Syndrome"
#> [37] "Polycystic Kidney Diseases" "Renal Fibrosis"
#> [39] "Renal Osteodystrophy" "Uremia"
#> [41] "Ureteral Calculi" "Urinary Bladder Diseases"
The argument can either have "Genes" or "miRNAs" as value. To see the list of disease groups that can be used as input, you can use tbe 'listDiseaseGroup()' function. Default values are "Genes" and "all" for the and arguments.
listDiseaseGroup(dataset = "Genes")
#> [1] "all"
#> [2] "Blood Disorders"
#> [3] "Cardiovascular Diseases"
#> [4] "Congenital, Hereditary, and Neonatal Diseases and Abnormalities"
#> [5] "Digestive System Diseases"
#> [6] "Ear Or Mastoid Diseases"
#> [7] "Endocrine System Diseases"
#> [8] "Eye Diseases"
#> [9] "Immune System Diseases"
#> [10] "Musculoskeletal Diseases"
#> [11] "Neoplasms"
#> [12] "Nervous System Diseases"
#> [13] "Nutritional and Metabolic Diseases"
#> [14] "Psychiatric/Brain disorders"
#> [15] "Renal Disorder"
#> [16] "Reproductive disorders"
#> [17] "Respiratory Tract Diseases"
#> [18] "Skin and Connective Tissue Diseases"
This list of disease groups can be used as input in the function.
Pathway Analysis
Getting enriched pathways
Explain about use of enriched pathways. The pathway analysis tool gives a of enriched pathways for selected diseases. We call it using the generatePathwayAnalysis() function. It accepts , and as arguments. Database can either be "KEGG" or "Reactome".
pathways.df = PathwayAnalysis(disease_list = c("Anemia"),
dataset = "Genes",
database = "KEGG")
head(pathways.df)
#> Pathway name
#> row.1...1 Phospholipase D signaling pathway
#> row.2...2 Neuroactive ligand-receptor interaction
#> row.3...3 Adrenergic signaling in cardiomyocytes
#> row.4...4 Vascular smooth muscle contraction
#> row.5...5 Renin-angiotensin system
#> row.6...6 Renin secretion
#> Pathway genes associated with PCOS and the selected diseases
#> row.1...1 AGT, PDGFRA
#> row.2...2 AGT, PRL, PTH
#> row.3...3 AGT
#> row.4...4 AGT
#> row.5...5 AGT, REN
#> row.6...6 AGT, REN
#> Pathway genes associated with PCOS
#> row.1...1 AKT3, AGT, EGFR, AKT1, AKT2, CXCL8, INS, INSR, PDGFRA, PIK3CA, PIK3CG, PIK3R1, PLCB2, PLCB3, MAPK1, MAPK3
#> row.2...2 CGA, UCN3, ADM, CRH, ADRB2, ADRB3, DRD2, AGT, APLNR, EDN1, FSHB, FSHR, GCG, GH1, GHR, GHRH, GIP, GLP1R, GNRH1, GNRHR, NR3C1, HTR2C, IAPP, INSL3, KISS1, KNG1, LEP, LEPR, LHB, LHCGR, MTNR1A, MTNR1B, NPY, OXT, GHRL, POMC, PRL, PYY, PTH, RLN1, RLN2, SST, TACR3, C3, CALCA, CCK, APLN, UCN2
#> row.3...3 AKT3, MAPK14, ADRB2, AGT, AKT1, AKT2, GNAQ, KCNQ1, ATF4, PIK3CG, PLCB2, PLCB3, MAPK1, MAPK3
#> row.4...4 ADM, AGT, EDN1, GNAQ, MYH11, NPPB, PLCB2, PLCB3, MAPK1, MAPK3, ACTA2, ACTG2, CALCA
#> row.5...5 ACE, AGT, REN
#> row.6...6 ADRB2, ADRB3, ACE, AGT, EDN1, GNAQ, PDE3B, PLCB2, PLCB3, REN
#> Hypergeometric probability
#> row.1...1 0.159732685532648
#> row.2...2 0.23363353260246
#> row.3...3 0.371785240224444
#> row.4...4 0.364445361612939
#> row.5...5 0.00783718583187695
#> row.6...6 0.0819318218889266
This function returns a list of enriched pathways. Enriched pathways are identifed based on hypergeometric distribution with the threshold p value set as 0.05 (gene dataset) and 0.001 (miRNA dataset) based on the data size.
Visualizing enriched pathways
Output of the generatePathwayAnalysis() is used as input for the viewNetwork() function. This functions allows us to visualize the pathways as a network.
viewNetwork(pathways.df)
Each pathway is represented as a node and is connected to other pathways in the network based on common genes or miRNAs. Te thickness of the edge is proportional to the number of shared genes or miRNAs. In this example there are 151 nodes. We can also filter the data.frame to just obtain a network with enriched pathways for example.
#filter dataframe to only obtain enriched pathways
enriched_pathways.df = pathways.df[which(pathways.df$`Hypergeometric probability` <= 0.005),]
viewNetwork(enriched_pathways.df)
Gene Network Analysis
This tool allows us to generate hypothesis about disease targets based on network properties. Experimentally validated interactions from STRING v11 were used for creating gene interaction networks for enriched pathways. Critical genes in these pathways were identifed based on network topological properties such as degree, closeness centrality, and betweenness centrality calculated using igraph package in R. You can call it using the getGeneNetworkAnalysis() function.
gene_tbl.df = getGeneNetworkAnalysis(disease_list = c("Psychomotor Disorders", "Psychosexual Disorders" , "Pubertal Disorder"),
database = "Reactome")
#> Submitting with 'Add'
Check PCOSKB for details on how bottleneck genes and hub genes are chosen.
Visualizing interactions
We can also visualize all gene-gene interactions using viewInteractions() function. It has one argument and the value is a gene Symbol.
viewInteractions("INS")
Venn Analysis
This tool can be used to illustrate the unique and/or common genes, pathways and ontologies for 2 or more (up to 6) diseases. The function getVennAnalysis() is used for that purpose. Function arguments a vector of disease of up to 6 diseases. There is alos the options argument that can be either "Genes", "Pathways" or
getVennAnalysis(disease_list = c("PCOS", "Bipolar Disorder", "Mental Depression", "Schizophrenia", "Autistic Disorder", "Alzheimer Disease" ), option = "Genes")
#> Warning: Ignoring unknown aesthetics: text
Object will render the graph locally in your web browser or in the RStudio viewer.
This outputs an interactive web-based Venn-Diagram diagram created using ggVennDiagram and rendered using plotly.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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