sponge_sample_specific: Identifying sample-specific miRNA sponge interactions

In zhangjunpeng411/miRspongeR: Identification and analysis of miRNA sponge regulation

Identifying sample-specific miRNA sponge interactions

Description

A sample control variable strategy is used to identify sample-specific miRNA sponge interactions. In the strategy, seven popular methods (pc, sppc, ppc, hermes, muTaME, cernia, and SPONGE) to identify miRNA sponge interactions.

Usage

sponge_sample_specific(miRTarget, ExpData = NULL, mres = NULL, 
consider.miR.expr = "TRUE", minSharedmiR = 3, poscorcutoff = 0, 
num_perm = 100, padjustvaluecutoff = 0.01, padjustmethod = "BH", 
senscorcutoff = 0.3, scorecutoff = 0.5, null_model, 
method = c("pc", "pc_parallel", "sppc", "sppc_parallel", 
"ppc", "ppc_parallel", "hermes", "hermes_parallel", "cernia", 
"cernia_parallel", "sponge_parallel"), num.cores = 2)

Arguments

miRTarget	Putative miRNA-target interactions. Required option for method "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes", "hermes_parallel", "muTaME", "muTaME_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
ExpData	An input expression data frame, the columns are genes and the rows are samples. Required option for method "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes" "hermes_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
mres	Putative MiRNA Response Elements (mres) data frame, each row contains five elements: Mirna, Target, energy, gap_l, gap_r. Required option for method "muTaME", "muTaME_parallel", "cernia", and "cernia_parallel".
consider.miR.expr	Logical value, TRUE for considering miRNA expression data and FALSE for ignoring miRNA expression data
minSharedmiR	The minimum number of shared miRNAs between targets. Required option for method "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes", "hermes_parallel", "muTaME", "muTaME_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
poscorcutoff	A cutoff value of positive correlation. Required option for method "pc", "pc_parallel", "sppc", "sppc_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
num_perm	The number of permutations. Required option for method "ppc", "ppc_parallel", "hermes", "hermes_parallel".
padjustvaluecutoff	A cutoff value of adjusted p-values. Required option for method "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes", "hermes_parallel", "muTaME", "muTaME_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
padjustmethod	Adjusted method of p-values, can select one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none". Required option for method "miRHomology", "miRHomology_parallel", "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes", "hermes_parallel", "muTaME", "muTaME_parallel", "cernia", "cernia_parallel", and "sponge_parallel".
senscorcutoff	A cutoff value of sensitivity partial pearson correlation. Required option for method "sppc", "sppc_parallel", and "sponge_parallel".
scorecutoff	A cutoff value of normalized score (range from 0 to 1). Required option for method "muTaME", "muTaME_parallel", "cernia", and "cernia_parallel".
null_model	Optional, pre-computed null model. Users can also build null model using "sponge_build_null_model" function in SPONGE R package. Required option for method "sponge_parallel".
method	Select a method for identifying miRNA sponge interactions, can select one of "pc", "pc_parallel", "sppc", "sppc_parallel", "ppc", "ppc_parallel", "hermes", "hermes_parallel", "muTaME", "muTaME_parallel", "cernia", "cernia_parallel", "sponge_parallel". The seven methods ("miRHomology_parallel", "pc_parallel", "sppc_parallel", "ppc_parallel", "hermes_parallel", "muTaME_parallel", "cernia_parallel") are the parallel versions of the seven original methods ("miRHomology", "pc", "sppc", "ppc", "hermes", "muTaME", "cernia").
num.cores	The number of CPU cores to be selected. Required option for method "pc_parallel", "sppc_parallel", "ppc_parallel", "hermes_parallel", "muTaME_parallel", "cernia_parallel", and "sponge_parallel".

Value

A list of sample-specific miRNA sponge interactions.

Author(s)

Junpeng Zhang (https://www.researchgate.net/profile/Junpeng_Zhang3)

References

1. Le TD, Zhang J, Liu L, et al. Computational methods for identifying miRNA sponge interactions. Brief Bioinform., 2017, 18(4):577-590.

2. Li JH, Liu S, Zhou H, et al. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res., 2014, 42(Database issue):D92-7.

3. Sarver AL, Subramanian S. Competing endogenous RNA database. Bioinformation, 2012, 8(15):731-3.

4. Zhou X, Liu J, Wang W, Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. IET Syst Biol., 2014, 8(3):96-103.

5. Xu J, Li Y, Lu J, et al. The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types. Nucleic Acids Res., 2015, 43(17):8169-82.

6. Paci P, Colombo T, Farina L, Computational analysis identifies a sponge interaction network between long non-coding RNAs and messenger RNAs in human breast cancer. BMC Syst Biol., 2014, 8:83.

7. Sumazin P, Yang X, Chiu HS, et al. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell, 2011, 147(2):370-81.

8. Tay Y, Kats L, Salmena L, et al. Coding-independent regulation of the tumor suppressor PTEN by competing endogenous mRNAs. Cell, 2011, 147(2):344-57.

9. Sardina DS, Alaimo S, Ferro A, Pulvirenti A, Giugno R. A novel computational method for inferring competing endogenous interactions. Brief Bioinform. 2017;18(6):1071-1081.

10. List M, Dehghani Amirabad A, Kostka D, Schulz MH. Large-scale inference of competing endogenous RNA networks with sparse partial correlation. Bioinformatics. 2019;35(14):i596-i604.

Examples

# Obtain expression data file "ExpData.csv" in csv format
ExpDatacsv <- system.file("extdata","ExpData.csv",package="miRspongeR")
ExpData <- read.csv(ExpDatacsv, header=TRUE, sep=",")

# Obtain miRNA-target interaction data file "miR2Target.csv" in csv format
miR2Target <- system.file("extdata", "miR2Target.csv", package="miRspongeR")
miRTarget <- read.csv(miR2Target, header=TRUE, sep=",")

# Identifying sample-specific miRNA sponge interactions, 
# the sppc method is used to identify miRNA sponge interactions
sponge_sample_specific_net <- sponge_sample_specific(miRTarget, ExpData, senscorcutoff = 0.1, method = "sppc")

zhangjunpeng411/miRspongeR documentation built on Aug. 26, 2024, 6:48 a.m.