diff --git a/SpicyBytesStats.R b/SpicyBytesStats.R
index 9ee43acbf3275863fc0fc2b256a30f4d1387f1b9..adb6e2d4ef050ec5966905bfe675fc05047a3c92 100644
--- a/SpicyBytesStats.R
+++ b/SpicyBytesStats.R
@@ -3,7 +3,7 @@ args <- commandArgs(trailingOnly = TRUE)
 treatment = args[1]
 mock = args[2]
 # the results will be saved in a csv with the treatment in the name
-outfile <- paste0("../Stat_results/significant_genes_",treatment,".csv")
+outfile <- paste0("./Stat_results/significant_genes_",treatment,".csv")
 #for later use treatment and mock must be 'condition[name]'
 treatment <- paste0("condition", treatment)
 mock <- paste0("condition", mock)
@@ -26,7 +26,7 @@ kal_dirs = sapply(sample_ids, function(id) file.path(kallisto_dir, id))
 
 
 ## design1.txt has to be created, should contain 2 columns sample	and condition
-s2c = read.table(file.path(base_dir,"/design1.txt"), header=TRUE, stringsAsFactors=FALSE)
+s2c = read.table(file.path(base_dir,"/design_kallisto_result_boot.txt"), header=TRUE, stringsAsFactors=FALSE)
 # condition should be all the conditions, first per sample, then all the names
 #condition = factor(c("ST","ST","ST","HT","HT","HT"),c("ST","HT"))
 condition <- factor(s2c$condition, levels = unique(s2c$condition))
@@ -38,7 +38,7 @@ so = sleuth_prep(s2c, ~condition, extra_bootstrap_summary=TRUE, transformation_f
 
 # compares two different models
 so = sleuth_fit(so, ~condition, 'full')
-so = sleuth_fit(so, ~treatment, 'reduced')
+so = sleuth_fit(so, ~1, 'reduced')
 #so = sleuth_lrt(so, 'reduced', 'full')
 so = sleuth_wt(so, which_beta= mock, which_model='full')
 sleuth_table = sleuth_results(so, mock, 'wt', show_all=FALSE)