chienyuehlee / anamir-2.0

An integrated analysis R package of miRNA and mRNA profiiling

anamiR-2.0

An integrated analysis R package of miRNA and mRNA profiiling

anamiR

General Workflow

1. Import data

library(anamiR)
data(mrna)
data(mirna)
data(pheno.mirna)
data(pheno.mrna)

2. SummarizedExperimaent object

mirna_se <- SummarizedExperiment(assays = SimpleList(counts=mirna), colData = pheno.mirna)
mrna_se <- SummarizedExperiment(assays = SimpleList(counts=mrna), colData = pheno.mrna)

3. Differential Expression analysis

mirna_d <- differExp_discrete(se = mirna_se, class = "ER", method = "limma", log2 = FALSE, p_value.cutoff = 0.05, logratio = 0.5, p_adjust.method = "BH")
mrna_d <- differExp_discrete(se = mrna_se, class = "ER", method = "limma", log2 = FALSE, p_value.cutoff = 0.05, logratio = 0.5, p_adjust.method = "BH")

4. miRNA ID conversion

mirna_21 <- miR_converter(data = mirna_d, remove_old = TRUE, original_version = 17, latest_version = 21)

5. Correlation abalysis

cor <- negative_cor(mrna_data = mrna_d, mirna_data = mirna_21, cut.off = -0.5, method = "pearson")

6. Database Intersection

sup <- database_support(cor_data = cor, Sum.cutoff = 2, org = "hsa")

7. Heatmap visualization

heat_vis(cor, mrna_d, mirna_21)

8. Functional analysis

enr <- enrichment(data_support = sup, per_time = 5000)

GSEA Workflow

1. Load data

aa <- system.file("extdata", "GSE19536_mrna.csv", package = "anamiR")
mrna <- data.table::fread(aa, fill = T, header = T)
bb <- system.file("extdata", "GSE19536_mirna.csv", package = "anamiR")
mirna <- data.table::fread(bb, fill = T, header = T)
cc <- system.file("extdata", "pheno_data.csv", package = "anamiR")
pheno.data <- data.table::fread(cc, fill = T, header = T)

2. transform data format

mirna_name <- mirna[["miRNA"]]
mrna_name <- mrna[["Gene"]]
mirna <- mirna[, -1]
mrna <- mrna[, -1]
mirna <- data.matrix(mirna)
mrna <- data.matrix(mrna)
row.names(mirna) <- mirna_name
row.names(mrna) <- mrna_name
pheno_name <- pheno.data[["Sample"]]
pheno.data <- pheno.data[, -1]
pheno.data <- as.matrix(pheno.data)
row.names(pheno.data) <- pheno_name

Extra steps

mirna <- mirna[, 50:70]
mrna <- mrna[, 50:70]
pheno.data <- as.matrix(pheno.data[50:70, ])
colnames(pheno.data) <- "ER"

3. SummarizedExperiment object

mirna_21 <- miR_converter(mirna, original_version = 17)
mirna_se <- SummarizedExperiment(assays = SimpleList(counts=mirna_21), colData = pheno.data)
mrna_se <- SummarizedExperiment(assays = SimpleList(counts=mrna), colData = pheno.data)

4. GSEA_ana

table <- GSEA_ana(mrna_se = mrna_se, mirna_se = mirna_se, class = "ER", pathway_num = 2)
names(table)
miRNA_path1 <- table[[1]]
Gene_path1 <- table[[2]]

5. GSEA_res

result <- GSEA_res(table, pheno.data = pheno.data, class = "ER", DE_method = "limma", cor_cut = -0.3)
names(result)
result_path1 <- result[[1]]