miRTarget helps to find miRNA predicted and validated targets. Two databases are used miRecords and miRTarBase
- Install the latest version from GitHub as follow:
# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/miRTarget")- Loading
library(miRTarget)miRNAs <- c("hsa-miR-28-5p", "hsa-miR-768-5p")miRecords.v4 database is used. For each miRNA, we want to identify the predicted target genes by at least 5/11 databases.
The 11 databases are : "diana", "microinspector", "miranda", "mirtarget2", "mitarget", "nbmirtar", "pictar", "pita", "rna22", "rnahybrid", "targetscan".
Internet connexion is required.
p_targets <- get_mirecords_pt(miRNAs, n = 5, species = "Homo sapiens",
result_file = "mirna_mirecords_predicted_targets.txt",
show_progress = FALSE)
#> Results have been saved in the following file: mirna_mirecords_predicted_targets.txt
head(p_targets, 10)
#> mirna_name target.gene_name status
#> 1 hsa-miR-28-5p EN2 predicted
#> 2 hsa-miR-28-5p KIAA0355 predicted
#> 3 hsa-miR-28-5p MYST3 predicted
#> 4 hsa-miR-28-5p HOXA1 predicted
#> 5 hsa-miR-28-5p YPEL3 predicted
#> 6 hsa-miR-28-5p RHO predicted
#> 7 hsa-miR-28-5p LSM12 predicted
#> 9 hsa-miR-28-5p IKZF4 predicted
#> 10 hsa-miR-28-5p CSNK1G1 predicted
#> 11 hsa-miR-28-5p CABP7 predictedmiRecords and miRtarbase are used. Internet connexion is required.
- miRecords
mirecords_vt <- get_mirecords_vt(miRNAs,
species = "Homo sapiens", version = 4)
#> Downloading miRecords miRNA validated targets...
head(mirecords_vt, 10)
#> mirna_name target.gene_name status
#> 1 hsa-miR-28-5p gag-pol validated
#> 2 hsa-miR-768-5p <NA> validated- miRtarbase
mirtarbase_vt <- get_mirtarbase_vt(miRNAs)
#> Downloading miRTarbase miRNA validated targets...
head(mirtarbase_vt, 10)
#> mirna_name target.gene_name status
#> 1 hsa-miR-28-5p LINC00346 validated
#> 2 hsa-miR-28-5p SEMA4C validated
#> 3 hsa-miR-28-5p ZNF106 validated
#> 4 hsa-miR-28-5p NKIRAS2 validated
#> 5 hsa-miR-28-5p IMPDH1 validated
#> 6 hsa-miR-28-5p AAAS validated
#> 7 hsa-miR-28-5p CDKN1A validated
#> 8 hsa-miR-28-5p CENPV validated
#> 9 hsa-miR-28-5p SMYD1 validated
#> 10 hsa-miR-28-5p PAQR5 validated- Combine validated targets
v_targets <- combine_mir_vt(mirecords_vt, mirtarbase_vt)
# head(v_targets) targets <- combine_mir_pvt(p_targets, v_targets)
head(targets)
#> mirna_name target.gene_name status
#> 1 hsa-miR-28-5p gag-pol V
#> 2 hsa-miR-28-5p LINC00346 V
#> 3 hsa-miR-28-5p SEMA4C V
#> 4 hsa-miR-28-5p ZNF106 V
#> 5 hsa-miR-28-5p NKIRAS2 V
#> 6 hsa-miR-28-5p IMPDH1 V# Create some data with 4 samples (S) and two miRNAs
mirna_exprs <- data.frame(S1 = c(99, 8690), S2 = c(31, 612),
S3 = c(33, 1747), S4 = c(29, 2540))
rownames(mirna_exprs) <- c("hsa-miR-28-5p", "hsa-miR-768-5p")
print(mirna_exprs)
#> S1 S2 S3 S4
#> hsa-miR-28-5p 99 31 33 29
#> hsa-miR-768-5p 8690 612 1747 2540data("gene_exprs")
head(gene_exprs, 10)
#> S1 S2 S3 S4
#> AFF3 2254 14 15 15
#> IL7 250 104 22 14
#> SIGLEC10 1258 221 106 110
#> TUBB2A 1678 484 71 48
#> UNG 130 1883 212 125
#> TPM4 424 3835 744 199
#> E2F6 66 389 456 622
#> HSPA6 1021 91 75 109
#> UMPS 165 1792 754 688
#> E2F3 361 2455 1388 1361# Keep only target with gene expression data
targets <- subset(targets, targets$target.gene_name %in% rownames(gene_exprs) )
# Order data by miRNA name
targets <- targets[order(targets$mirna_name), ]
# Change rownames
rownames(targets) <- paste0(targets$mirna_name, "::", targets$target.gene_name)
head(targets)
#> mirna_name target.gene_name status
#> hsa-miR-28-5p::CENPV hsa-miR-28-5p CENPV V
#> hsa-miR-28-5p::PRDM2 hsa-miR-28-5p PRDM2 V
#> hsa-miR-28-5p::ARHGAP42 hsa-miR-28-5p ARHGAP42 V
#> hsa-miR-28-5p::PRSS16 hsa-miR-28-5p PRSS16 V
#> hsa-miR-28-5p::TUBB2A hsa-miR-28-5p TUBB2A V
#> hsa-miR-28-5p::PCYOX1 hsa-miR-28-5p PCYOX1 VThe name and the number of samples in the two data set should be identical.
mir_mrna_exprs <- combine_mir_mrna_exprs(mirna_exprs, gene_exprs)If a gene is targeted by a miRNA we expect that the expression profile of the gene and the miRNA are anti-correlated.
res.cor <- corr_mir_mrna( mir_targets = MirTarget(targets),
mir_mrna_exprs = mir_mrna_exprs,
show_progress = FALSE, stand = TRUE)- Number of mRNAs per miRNAs with an absolute correlation of 0.6
viz_mrna_per_mir(res.cor, coeff = 0.6)
- If you want to visualize the fraction of negative correlation use this
viz_neg_cor_fraction(res.cor)
r < -0.6
# Negative correlation
neg.cor <- subset(res.cor, abs(cor.coeff) > 0.6 & cor.coeff < 0)
head(neg.cor, 10)
#> mirna_name target.gene_name cor.coeff p.value fdr status
#> 1 hsa-miR-28-5p IER3 -0.951 0.04901988 0.1416130 V
#> 2 hsa-miR-28-5p ZNF569 -0.939 0.06081484 0.1506698 P
#> 3 hsa-miR-28-5p TMEM167A -0.935 0.06511116 0.1538991 V
#> 4 hsa-miR-28-5p E2F6 -0.920 0.07967078 0.1726200 V
#> 5 hsa-miR-28-5p SNX1 -0.866 0.13408097 0.2582300 V
#> 6 hsa-miR-28-5p YIPF6 -0.860 0.13971692 0.2594743 V
#> 7 hsa-miR-28-5p TEX261 -0.834 0.16580415 0.2973040 V
#> 8 hsa-miR-28-5p CSNK1G1 -0.827 0.17309904 0.3000383 P
#> 9 hsa-miR-28-5p PCYOX1 -0.806 0.19422393 0.3257950 V
#> 10 hsa-miR-28-5p HNRNPC -0.759 0.24051212 0.3908322 V
nmir <- length(unique(neg.cor$mirna_name))
ngn <- length(unique(neg.cor$target.gene_name))
valid <- subset(neg.cor, status %in% c("PV", "V"))- Number of negative interactions: 33
- Number of unique gene : 33
- Number of unique miRNA : 2
- Validated target-interaction: 12
- Unique genes : 12
- Unique miRNAs : 1
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