mirMrnaInt: mirMrnaInt

In TimiRGeN: Time sensitive microRNA-mRNA integration, analysis and network generation tool

Description

Create a correlation matrix of all the potential miR-mRNA interactions which could arise between the input miR data and the mRNAs found from the wikiMrna function. The time series DE data will be averaged from the dataframe created by diffExpressRes of miR data and the dataframe created by wikiMrna. This will show miR-mRNA correlations over the time course.

Usage

mirMrnaInt(MAE, miR_express, GenesofInterest, maxInt)

Arguments

MAE	MultiAssayExperiment which will store the output of mirMrnaInt. It is recommended to begin a new MAE using MultiAssayExperiment() here so the MAE objects do not get too large.
miR_express	Dataframe from using the diffExpressRes function on miR data. Rownames should be miR gene names and columns should include DE results displaying abundance e.g. log2fc or average expression. These dataframes should also have gene IDs. This should be stored as an assay within the MAE used in the diffExpressRes function.
GenesofInterest	Dataframe including mRNAs found in both the input data and the pathway of interest, as well as gene IDs. This is the output from wikiMrna. This should be found as an assay within the MAE which was used in the wikiMrna function. Make sure the same ID type is used in the inputs for miR_express and GenesofInterest.
maxInt	Integer. Should be equal to number of samples in both mRNA and miR data e.g. number of different time points. In the example it is 5 because there are 5 time points.

Value

A large correlation matrix which contains averaged miR-mRNA time series information for every possible miR-mRNA interaction between the genes of interest and all the miRs. Output will be stored as an assay in the input MAE.

Examples

G <- data.frame(row.names = c("Acaa1a", "Acadm", "Acss1", "Adh1"),
                "D1.Log2FC" = c("-1.2944593","-2.0267432","-2.1934942",
                                "-2.1095853"),
                "D2.Log2FC" = c("-1.1962396","-2.1345451","-1.7699232",
                                "-1.0961674"),
                "D3.Log2FC" = c("0.2738496","-1.9991046","-1.7637549",
                                "-1.6572653"),
                "D7.Log2FC" = c("-0.51765245","-2.20689661","-0.68479699",
                                "-2.06512466"),
                "D14.Log2FC" = c("-0.4510294","-1.1523849","-0.4297012",
                                 "-1.1017597"),
                "ID" = c("113868","11364","68738","11522"))

MIR <- data.frame(row.names = c("mmu-miR-101a-3p", "mmu-miR-101a-5p",
                                "mmu-miR-101c", "mmu-miR-106a-5p"),
                  "D1.Log2FC" = c("-0.0039141722","-0.4328659746",
                                  "-0.0038897133", "-0.4161749123"),
                  "D2.Log2FC" = c("-0.210605345","-0.600422732",
                                  "-0.210574742", "-0.530311376"),
                  "D3.Log2FC" = c("-0.315070839","-0.745367163",
                                  "-0.315012148", "-0.559274530"),
                  "D5.Log2FC" = c("-0.41087763","-0.63952382",
                                  "-0.41087876", "-1.03618015"),
                  "D14.Log2FC" = c("-0.39466968","-0.60122678",
                                   "-0.39461099", "-0.41889698"),
                  "ID" = c("387143","387143","100628572","723829"))

MAE <- MultiAssayExperiment()

MAE <- mirMrnaInt(MAE, miR_express = MIR, GenesofInterest = G,
                     maxInt = 5)

TimiRGeN documentation built on April 17, 2021, 6:03 p.m.