An example Analysis of correlation Mapping between miRNA and Target genes in R language
This article will explain in detail the example analysis of the mapping of miRNA and target genes in R language. The editor thinks it is very practical, so I share it with you as a reference. I hope you can get something after reading this article.
Mapping the relationship between miRNA and target genes
First of all, to generate an input file for drawing, there is a script, which is used as follows:
Perl Correlation_analysis.pl-mirna Donkey_E_vs_Donkey_T.DEG.final.xls-DE_tar targets.final.xls-list bta.mir2target.list-out Correlation_analysis_input.txt
Donkey_E_vs_Donkey_T.DEG.final.xls is the file of differential miRNA expression, targets.final.xls is the file of target gene expression, and bta.mir2target.list is the corresponding file of miRNA and target gene.
Donkey_E_vs_Donkey_T.DEG.final.xls is in the same format as targets.final.xls, as follows:
# ID Donkey2_E_Count Donkey1_T_Count Donkey2_E_TPM Donkey1_T_TPM FDR log2FC regulatedbta-miR-106a 26 113 1.164865 54.990056 3.20268371822863e-08 4.02066488557677 upbta-miR-10a 166025 1771 7438.335738 861.835296 0.000238871249442218-4.55148673628311 downbta-miR-135a 563114114 252.2829 55.476693 0.00268305048158635-3.62181067749427 downbta-miR-135b 25 89 1.120063 43.310752 6.41785537824902e-07 3.73045876228641 upbta-miR-141 1201 2 53.807807 0.973275 2.17196461482771e-07-6.91926313309958 downbta-miR-182 1882 33 84.318313 16.059043 19553585103038-3. 81519649022355 downbta-miR-183 1544 40 69.175066 19.465506 0.00778797871791981-3.25606929861198 downbta-miR-187 0 18 8.759478 6.10013036195234e-07 5.24714335099614 upbta-miR-196a 2831 128.627989 15.085767 0.000103494675187443-4.51281538763237 down
The bta.mir2target.list format is as follows:
Bta-let-7a-3p EAS0021476;EAS0000070;EAS0013638;EAS0001074;EAS0004577;EAS0006787;EAS0008899;EAS0013962;EAS0017655;EAS0005413;EAS0006009bta-let-7a-5p EAS0008419;EAS0003830;EAS0009803;EAS0011296;EAS0005793;EAS0004645;EAS0014772bta-let-7b EAS0017817;EAS0013474;EAS0008780;EAS0018405;EAS0017359;EAS0017674;EAS0020797;EAS0015379;EAS0011272;EAS0010435;EAS0013631bta-let-7c EAS0006104;EAS0004549;EAS0003321;EAS0010481;EAS0021491;EAS0015994;EAS0001788;EAS0002956;EAS0007473;EAS0008880;EAS0007345;EAS0003066bta-let-7d EAS0005004;EAS0009924;EAS0020635;EAS0002650;EAS0011574
The resulting file is as follows:
MiRNA Gene log2Ratio_miR log2Ratio_Genebta-let-7c EAS0003321-1.43072567351625 1.37874741391777bta-let-7c EAS0007345-1.43072567351625 1.11756970634256bta-let-7c EAS0003066-1.43072567351625 1.67994238012443bta-let-7c EAS0017368-1.43072567351625 3.05079253633835bta-let-7c EAS0004422-1.43072567351625 1.50329494091278bta-let-7c EAS0003988 -1.43072567351625 1.18612566219656bta-let-7c EAS0005608-1.43072567351625 1.89027598062113bta-let-7c EAS0012078-1.43072567351625 3.50870147552217bta-let-7c EAS0015105-1.43072567351625 1.06781330535694bta-let-7c EAS0020084-1.43072567351625 1.10413933910007
With the input file, and then you can draw, drawing script is relatively simple, usage:
Rscript correlation_analysis.R-I Correlation_analysis_input.txt-n Correlation_analysis
Script code:
Library (ggplot2) library ('getopt'); spec = matrix (c (' help', 'hype, 0, "logical", "for help",' input', 'iTunes, 1, "character", "input file, required",' name', 'nasty, 1, "character", "photo name"), byrow=TRUE, ncol=5); opt = getopt (spec); print_usage
