tp1

src/.Rhistory

+# Les données analysées nécessitant beaucoup de RAM, nous allons sélectionner aléatoirement 250000 SNPs et réecrire des fichiers bed, bim, fam
+penncath_bed_path = "results/data/penncath.bed"
+penncath_bim_path = "results/data/penncath.bim"
+penncath_fam_path = "results/data/penncath.fam"
+geno <- snpStats::read.plink(penncath_bed_path, penncath_bim_path, penncath_fam_path, select.snps=sample(1:861473, 25000, replace = FALSE ), na.strings = ("-9"))
+# Les données analysées nécessitant beaucoup de RAM, nous allons sélectionner aléatoirement 250000 SNPs et réecrire des fichiers bed, bim, fam
+penncath_bed_path = "~/Bureau/project_reprod/m2reprod/results/data/penncath.bed"
+penncath_bim_path = "~/Bureau/project_reprod/m2reprod/results/data/penncath.bim"
+penncath_fam_path = "~/Bureau/project_reprod/m2reprod/results/data/penncath.fam"
+geno <- snpStats::read.plink(penncath_bed_path, penncath_bim_path, penncath_fam_path, select.snps=sample(1:861473, 25000, replace = FALSE ), na.strings = ("-9"))

workflows/Makefile

+# Makefile 
+all : results/tp3/TP3_asbvg.RData
+
+results/tp3/TP3_asbvg.RData: results/tp1/plink_base.bed results/tp1/plink_base.bim results/tp1/plink_base.fam results/tp1/TP1_asbvg.RData 
+    Rscript src/tp1.R
+    
+results/tp1/plink_base.bed results/tp1/plink_base.bim results/tp1/plink_base.fam results/tp1/TP1_asbvg.RData: results/data/penncath.bed results/data/penncath.bim results/data/penncath.fam
+	Rscript src/tp1.R
+	
+results/data/penncath.bed results/data/penncath.bim results/data/penncath.fam:
+	Rscript src/download_data.R
+
+clean:
+	rm -rf results/data/penncath.bed results/data/penncath.bim results/data/penncath.fam results/data/penncath.csv results/penncath.tar.gz results/data
+
+