# HG changeset patch
# User vmarcon
# Date 1516274430 18000
# Node ID 79f00bc83ecc3d0a9e3e3b6de4a946043d22aede
planemo upload commit a2411926bebc2ca3bb31215899a9f18a67e59556
diff -r 000000000000 -r 79f00bc83ecc LICENSE
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/LICENSE Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc.
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+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+ 17. Interpretation of Sections 15 and 16.
+
+ If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+ END OF TERMS AND CONDITIONS
+
+ How to Apply These Terms to Your New Programs
+
+ If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+ To do so, attach the following notices to the program. It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+ {one line to give the program's name and a brief idea of what it does.}
+ Copyright (C) {year} {name of author}
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+
+Also add information on how to contact you by electronic and paper mail.
+
+ If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+ {project} Copyright (C) {year} {fullname}
+ This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+ This is free software, and you are welcome to redistribute it
+ under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License. Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+ You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+.
+
+ The GNU General Public License does not permit incorporating your program
+into proprietary programs. If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library. If this is what you want to do, use the GNU Lesser General
+Public License instead of this License. But first, please read
+.
diff -r 000000000000 -r 79f00bc83ecc normalization.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/normalization.R Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,282 @@
+# R Script implementing different kind of normalisation
+# Input : a file containing a table with numeric values
+# except for the first column containing sample names
+# and the first line containing variable names
+# separator expected is
+#
+# Normalization method :
+# log, DESeq2, Rlog, Standard_score, Pareto, TSS, TSS+CLR, Pareto
+#
+# Ouptut : input table with values normalized according
+# to the normalization procedure chosen
+#-----------------------------------------------------------------
+# Authors : luc.jouneau(at)inra.fr
+# valentin.marcon(at)inra.fr
+# Version : 0.9
+# Date : 30/08/2017
+#-----------------------------------------------------------------
+
+normalization=function(
+##########################################################
+# Function input
+##########################################################
+#Possible values : "log", "DESeq2", "Rlog", "Standard_score", "Pareto", "TSS", "TSS_CLR"
+transformation_method="Standard_score",
+na_encoding="NA",
+#Path to file containg table of values (separator="tab")
+input_file="",
+#Path to file produced after transformation
+output_file="out/table_out.txt",
+#Path to file containing messages for user if something bad happens
+log_file="log/normalization_report.html",
+#Boolean flag (0/1) indicating if variables are in line or in columns
+variable_in_line="1") {
+
+##########################################################
+# Read and verify data
+##########################################################
+#1°) Checks valids for all modules
+if (variable_in_line=="1") {
+ column_use="individual"
+ line_use="variable"
+} else {
+ line_use="individual"
+ column_use="variable"
+}
+log_error=function(message="") {
+ cat("Normalization report\n",file=log_file,append=F,sep="")
+ cat("⚠ An error occurred while trying to read your table.\n ",file=log_file,append=T,sep="")
+ cat("Please check that:\n ",file=log_file,append=T,sep="")
+ cat("
\n",file=log_file,append=T,sep="")
+ cat("
the table you want to process contains the same number of columns for each line
\n",file=log_file,append=T,sep="")
+ cat("
the first line of your table is a header line (specifying the name of each ",column_use,")
\n",file=log_file,append=T,sep="")
+ cat("
the first column of your table specifies the name of each ",line_use,"
\n",file=log_file,append=T,sep="")
+ cat("
both individual and variable names should be unique
\n",file=log_file,append=T,sep="")
+ cat("
each value is separated from the other by a TAB character
\n",file=log_file,append=T,sep="")
+ cat("
except for first line and first column, table should contain a numeric value
\n",file=log_file,append=T,sep="")
+ cat("
this value may contain character '.' as decimal separator or '",na_encoding,"' for missing values
\n",file=log_file,append=T,sep="")
+ cat("
\n",file=log_file,append=T,sep="")
+ cat("------- \nError messages recieved : \n",conditionMessage(message),"\n",file=log_file,append=T,sep="")
+ cat("\n",file=log_file,append=T,sep="")
+ q(save="no",status=1)
+}
+
+tab_in=tryCatch(
+ {
+ tab_in=read.table(file=input_file,sep="\t",header=T,quote="\"",na.strings=na_encoding,check.names=FALSE)
+ },
+ error=function(cond) {
+ log_error(message=cond)
+ return(NA)
+ },
+ warning=function(cond) {
+ log_error(message=cond)
+ return(NA)
+ },
+ finally={
+ #Do nothing special
+ }
+)
+
+if (ncol(tab_in)<2) {
+ log_error(simpleCondition("The table you want to normalize contains less than two columns."))
+}
+
+rn=as.character(tab_in[,1])
+if (length(rn)!=length(unique(rn))) {
+ duplicated_rownames=table(rn)
+ duplicated_rownames=duplicated_rownames[duplicated_rownames>1]
+ duplicated_rownames=names(duplicated_rownames)
+ if (length(duplicated_rownames)>3) {
+ duplicated_rownames=c(duplicated_rownames[1:3],"...")
+ }
+ duplicated_rownames=paste(duplicated_rownames,collapse=", ")
+ log_error(simpleCondition(
+ paste("The table you want to normalize have duplicated values in the first column (",
+ line_use," names) - duplicated ",line_use," names : ",duplicated_rownames,sep="")
+ ))
+}
+tab=tab_in[,-1]
+rownames(tab)=rn
+
+#Check all columns are numeric
+tab=as.matrix(tab)
+cell.with.na=c()
+for (i in 1:ncol(tab)) {
+ na.v1=is.na(tab[,i])
+ na.v2=is.na(as.numeric(tab[,i]))
+ if (sum(na.v1)!=sum(na.v2)) {
+ sel=which(na.v1!=na.v2)
+ sel=sel[1]
+ value=tab[sel,i]
+ log_error(simpleCondition(
+ paste("Column '",colnames(tab)[i],"' of your table contains non numeric values. Please check its content (on line #",sel," : value='",value,"').",sep="")
+ ))
+ }
+ if (length(cell.with.na)==0 & sum(na.v1)!=0) {
+ cell.with.na=c(i,which(na.v1)[1])
+ }
+}
+
+#2°) Checks only valid for normalization module
+if (transformation_method %in% c("DESeq2","Rlog")) {
+ #Check there is no missing values
+ if (length(cell.with.na)!=0) {
+ log_error(simpleCondition(
+ paste("Column '",colnames(tab)[cell.with.na[1]],"' of your table contains missing values (see line #",cell.with.na[2],").\n",
+ transformation_method," normalization does not accept missing values. ",sep="")
+ ))
+ }
+}
+if (transformation_method %in% c("DESeq2","Rlog","TSS","TSS_CLR")) {
+ #Check values are integer
+ for (i in 1:ncol(tab)) {
+ if (!is.integer(tab[,i])) {
+ sel=which(!is.integer(tab[,i]))
+ sel=sel[1]
+ value=tab[sel,i]
+ log_error(simpleCondition(
+ paste("Column '",colnames(tab)[i],"' of your table contains non integer values.\n",
+ transformation_method," normalization only accepts integer values. ",
+ "Please check its content (on line #",sel," : value=",value,").",sep="")
+ ))
+ }
+ }
+}
+
+if (transformation_method %in% c("log","TSS","TSS_CLR","DESeq2","Rlog")) {
+ #Check values are positive
+ for (i in 1:ncol(tab)) {
+ if (sum(tab[,i]>=0 | is.na(tab[,i]))!=nrow(tab)) {
+ sel=which(tab[,i]<0)
+ sel=sel[1]
+ value=tab[sel,i]
+ log_error(simpleCondition(
+ paste("Column '",colnames(tab)[i],"' of your table contains negative values.\n",
+ transformation_method," normalization only accepts positive or null values. ",
+ "Please check its content (on line #",sel," : value=",value,").",sep="")
+ ))
+ }
+ }
+}
+
+##########################################################
+# End of data checks
+##########################################################
+
+### Transpose if variable are in line ###
+if (variable_in_line=="1") {
+ #Transpose matrix
+ tab=t(tab)
+}
+
+##########################################################
+### Value transformation
+##########################################################
+
+#Avoid null values when there is a log transformation
+na.replaced=c()
+log.transformed=FALSE
+if (transformation_method %in% c("log","TSS_CLR")) {
+ log.transformed=TRUE
+ for (idx_col in 1:ncol(tab)) {
+ sel=tab[,idx_col]==0
+ na.replaced=cbind(na.replaced,sel)
+ tab[sel,idx_col]=1e-2
+ }
+}
+
+### log ###
+if (transformation_method=="log") {
+ tab=log2(tab)
+}
+
+### DESeq2 or Rlog ###
+if (transformation_method %in% c("DESeq2","Rlog")) {
+ library(DESeq2)
+ n <- ncol(tab)
+ dds <- DESeqDataSetFromMatrix(tab,
+ colData = data.frame(condition = c("a", rep("b", n - 1))),
+ design = formula(~ condition))
+ colnames(dds) <- colnames(tab)
+ dds <- estimateSizeFactors(dds)
+ tab <- switch(transformation_method,
+ DESeq2 = counts(dds, normalized = TRUE),
+ Rlog = assay(rlogTransformation(dds))
+ )
+}
+
+### Standard_score ###
+if (transformation_method=="Standard_score") {
+ tab=scale(tab)
+}
+
+### Pareto ###
+if (transformation_method=="Pareto") {
+ tab.centered <- apply(tab, 2, function(x) x - mean(x,na.rm=TRUE))
+ tab.sc <- apply(tab.centered, 2, function(x) x/sqrt(sd(x,na.rm=TRUE)))
+ tab=tab.sc
+}
+
+### TSS ###
+if (transformation_method=="TSS") {
+ tab= t(apply(tab, 1, function(x) x/sum(x,na.rm=TRUE)))
+}
+
+### TSS + CLR avec function de mixOmics ###
+if (transformation_method=="TSS_CLR") {
+ #From http://stackoverflow.com/questions/2602583/geometric-mean-is-there-a-built-in
+ geometric.mean = function(x, na.rm=TRUE){
+ exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
+ }
+ tab = t(apply(tab+1e-2,1,function(x) log(x/geometric.mean(x,na.rm=TRUE))))
+}
+
+
+#If there is a log transformation put 0 where there was NA
+if (log.transformed) {
+ for (idx_col in 1:ncol(tab)) {
+ tab[na.replaced[,idx_col],idx_col]=0
+ }
+}
+
+#If there are missing values, replace it with NA_enconding
+for (idx_col in 1:ncol(tab)) {
+ sel=is.na(tab[,idx_col])
+ tab[sel,idx_col]=na_encoding
+}
+
+##########################################################
+# Prepare and write output table
+##########################################################
+if (variable_in_line=="1") {
+ #Transpose matrix again
+ tab=t(tab)
+}
+
+tab_out=cbind(rownames(tab),tab)
+colnames(tab_out)[1]=colnames(tab_in)[1]
+
+write.table(file=output_file,tab_out,sep="\t",row.names=F,quote=F)
+
+##########################################################
+# Treatment successfull
+##########################################################
+cat("Normalization report\n",file=log_file,append=F,sep="")
+cat(paste("➔ You choose to apply the transformation method :",transformation_method," "),file=log_file,append=F,sep="")
+cat("✓ Your normalization process is successfull ! ",file=log_file,append=T,sep="")
+cat("\n",file=log_file,append=T,sep="")
+
+q(save="no",status=0)
+
+} # end of function
+
+##########################################################
+# Test
+##########################################################
+#Used for debug : LJO 6/3/2017
+#normalization()
+#setwd("H:/INRA/cati/groupe stats/Galaxy/normalisation")
+#normalization(transformation_method="Standard_score",na_encoding="NA",input_file="datasets/valid - decathlon.txt",output_file="out/table_out.txt",log_file="log/normalization.html",variable_in_line="0")
+#normalization(transformation_method="Pareto",na_encoding="NA",input_file="datasets/valid - decathlon.txt",output_file="out/table_out.txt",log_file="log/normalization.html",variable_in_line="1")
+
diff -r 000000000000 -r 79f00bc83ecc normalization.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/normalization.xml Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,229 @@
+
+
+
+ Normalize your data with some well known methods
+
+ R
+ bioconductor-deseq2
+ r-batch
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ _
+ | input file normalized according to the choosen method
+ |
+
+Normalization_log
+ |
+
+-------
+Advices
+-------
+
+Nature of data may change
+ | Depending on the subjects of the experimentation and/or the technology used to measure a signal on these subjects.
+ | By instance, when dealing with RNA-Seq data, expression intensity values are expressed as counts, while with microarray technology, it is expressed as fluorescence intensity.
+ |
+
+Before to conduct any analysis on a table of data, it is important to:
+ | Identify the nature of data you are dealing with
+ | Check if this nature of data is adapted to the type of analysis you want to do
+
+If your nature of data is not adapted to the analysis you plan to do, you should first transform your data in a scale of values which fits better requirement of your analysis.
+This transformation process is named “normalization”.
+
+
+---------------------
+Normalization Methods
+---------------------
+
+In this Galaxy module, we propose several normalization methods, and we provide some guidelines to help user choose the accurate normalization method:
+
+Log normalization
+ | -Objective: Binary logarithm provide homogeneity of variance even if the range of values is pretty large
+ | -Accepted: values Any positive or null real numbers
+ | (null values, will stay null after transformation)
+ | -Range of values: Input: [0;100.000] / Output: [0;17]
+ | -Adapted for: PCA, HC, SS*
+ |
+
+DESeq2 normalization
+ | -Objective: Obtain comparable counts between samples, whatever the difference of their libraries sequencing depth
+ | -Accepted values: NGS counts (positive integers ; no missing values)
+ | (null values, will stay null after transformation)
+ | -Range of values: Input: [0;100.000] / Output: [0; 100.000]
+ | -Adapted for: Differential analysis
+ |
+
+RLog normalization
+ | -Objective: Similar to a combination of {DESeq2 + Log} transformation
+ | -Accepted values: NGS counts (positive integers ; no missing values)
+ | -Range of values: Input: [0;100.000] / Output: [0; 20]
+ | -Adapted for: PCA, HC, SS
+ |
+
+Standard score normalization
+ | -Objective: Transform values such as {mean=0 and standard deviation=1} for all variables.
+ | -Accepted values: No specific constraint
+ | -Range of values: No specific constraint
+ | -Adapted for: PCA, HC, SS
+ |
+
+Pareto normalization
+ | -Objective: Transform values such as
+ | {mean=0 and variance equal to its standard deviation instead of unit variance} for all variables.
+ | -Accepted values: No specific constraint
+ | -Range of values: No specific constraint
+ | -Adapted for: metabolite intensity values before PCA, HC, SS
+ |
+
+Total sum scaling normalization (TSS)
+ | -Objective: Normalizes count data by dividing variable read count by the total number of read counts in each individual sample
+ | -Accepted values: 16S rRNA amplicon sequencing
+ | -Range of values: Input: no specific constraint / Output: [0;1[
+ | -Adapted for: PCA, HC, SS
+ |
+
+Total sum scaling+Log ratio normalization (TSS+CLR)
+ | -Objective: Transform values such as {mean=0 and standard deviation=1} for all variables.
+ | -Accepted values: 16S rRNA amplicon sequencing
+ | -Range of values: Input: no specific constraint / Output: [0;1[
+ | -Adapted for: PCA, HC, SS
+
+(*)PCA: Principal Component Analysis / HC: Hierarchical Clustering / SS: Summary Statistics
+
+------
+
+**Authors**: Luc Jouneau (luc.jouneau@inra.fr), Sarah Maman (sarah.maman@inra.fr) and Valentin Marcon (valentin.marcon@inra.fr)
+
+Contact : support.sigenae@inra.fr
+
+E-learning available : Not yet.
+
+.. class:: infomark
+
+-------------
+Please cite :
+-------------
+
+- (Depending on the help provided you can cite us in acknowledgements, references or both.)
+
+Acknowledgements
+ | We wish to thank SIGENAE group and the statistical CATI BIOS4Biol group : Luc Jouneau, Sarah Maman
+ | Re-packaging was provided by Valentin Marcon (INRA, Migale platform http://migale.jouy.inra.fr), as part of the IFB project 'Galaxy For Life Science' (http://www.france-bioinformatique.fr/fr)
+ |
+
+References
+ | SIGENAE [http://www.sigenae.org/]
+ |
+
+ ]]>
+
diff -r 000000000000 -r 79f00bc83ecc normalization_galaxy.R
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/normalization_galaxy.R Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,45 @@
+#!/usr/local/bioinfo/bin/Rscript --vanilla --slave --no-site-file
+
+# R Script making the bridge between Galaxy and the call of the normalization method
+#-----------------------------------------------------------------
+# Authors : luc.jouneau(at)inra.fr
+# valentin.marcon(at)inra.fr
+# Version : 0.9
+# Date : 30/08/2017
+#---------------------------------------------------------------
+
+##------------------------------
+## Options
+##------------------------------
+strAsFacL <- options()$stringsAsFactors
+options(stringsAsFactors = FALSE)
+
+##------------------------------
+## Libraries laoding
+##------------------------------
+# For parseCommandArgs function
+library(batch)
+
+# R script call
+source_local <- function(fname)
+{
+ argv <- commandArgs(trailingOnly = FALSE)
+ base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
+ source(paste(base_dir, fname, sep="/"))
+}
+
+#Import the different functions used for Normalization
+source_local("normalization.R")
+
+##------------------------------
+## Lecture parametres
+##------------------------------
+argLs <- parseCommandArgs(evaluate=FALSE)
+
+normalization(input_file=argLs[["input_file"]],
+ transformation_method=argLs[["transformation_method"]],
+ na_encoding=argLs[["na_encoding"]],
+ output_file=argLs[["output_file"]],
+ log_file=argLs[["log_file"]],
+ variable_in_line=argLs[["variable_in_line"]])
+
diff -r 000000000000 -r 79f00bc83ecc test-data/decathlon.tsv
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/decathlon.tsv Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,42 @@
+"name" "100m" "Long.jump" "Shot.put" "High.jump" "400m" "110m.hurdle" "Discus" "Pole.vault" "Javeline" "1500m"
+"SEBRLE" 11.04 7.58 14.83 2.07 49.81 14.69 43.75 5.02 63.19 291.7
+"CLAY" 10.76 7.4 14.26 1.86 49.37 14.05 50.72 4.92 60.15 301.5
+"KARPOV" 11.02 7.3 14.77 2.04 48.37 14.09 48.95 4.92 50.31 300.2
+"BERNARD" 11.02 7.23 14.25 1.92 48.93 14.99 40.87 5.32 62.77 280.1
+"YURKOV" 11.34 7.09 15.19 2.1 50.42 15.31 46.26 4.72 63.44 276.4
+"WARNERS" 11.11 7.6 14.31 1.98 48.68 14.23 41.1 4.92 51.77 278.1
+"ZSIVOCZKY" 11.13 7.3 13.48 2.01 48.62 14.17 45.67 4.42 55.37 268
+"McMULLEN" 10.83 7.31 13.76 2.13 49.91 14.38 44.41 4.42 56.37 285.1
+"MARTINEAU" 11.64 6.81 14.57 1.95 50.14 14.93 47.6 4.92 52.33 262.1
+"HERNU" 11.37 7.56 14.41 1.86 51.1 15.06 44.99 4.82 57.19 285.1
+"BARRAS" 11.33 6.97 14.09 1.95 49.48 14.48 42.1 4.72 55.4 282
+"NOOL" 11.33 7.27 12.68 1.98 49.2 15.29 37.92 4.62 57.44 266.6
+"BOURGUIGNON" 11.36 6.8 13.46 1.86 51.16 15.67 40.49 5.02 54.68 291.7
+"Sebrle" 10.85 7.84 16.36 2.12 48.36 14.05 48.72 5 70.52 280.01
+"Clay" 10.44 7.96 15.23 2.06 49.19 14.13 50.11 4.9 69.71 282
+"Karpov" 10.5 7.81 15.93 2.09 46.81 13.97 51.65 4.6 55.54 278.11
+"Macey" 10.89 7.47 15.73 2.15 48.97 14.56 48.34 4.4 58.46 265.42
+"Warners" 10.62 7.74 14.48 1.97 47.97 14.01 43.73 4.9 55.39 278.05
+"Zsivoczky" 10.91 7.14 15.31 2.12 49.4 14.95 45.62 4.7 63.45 269.54
+"Hernu" 10.97 7.19 14.65 2.03 48.73 14.25 44.72 4.8 57.76 264.35
+"Nool" 10.8 7.53 14.26 1.88 48.81 14.8 42.05 5.4 61.33 276.33
+"Bernard" 10.69 7.48 14.8 2.12 49.13 14.17 44.75 4.4 55.27 276.31
+"Schwarzl" 10.98 7.49 14.01 1.94 49.76 14.25 42.43 5.1 56.32 273.56
+"Pogorelov" 10.95 7.31 15.1 2.06 50.79 14.21 44.6 5 53.45 287.63
+"Schoenbeck" 10.9 7.3 14.77 1.88 50.3 14.34 44.41 5 60.89 278.82
+"Barras" 11.14 6.99 14.91 1.94 49.41 14.37 44.83 4.6 64.55 267.09
+"Smith" 10.85 6.81 15.24 1.91 49.27 14.01 49.02 4.2 61.52 272.74
+"Averyanov" 10.55 7.34 14.44 1.94 49.72 14.39 39.88 4.8 54.51 271.02
+"Ojaniemi" 10.68 7.5 14.97 1.94 49.12 15.01 40.35 4.6 59.26 275.71
+"Smirnov" 10.89 7.07 13.88 1.94 49.11 14.77 42.47 4.7 60.88 263.31
+"Qi" 11.06 7.34 13.55 1.97 49.65 14.78 45.13 4.5 60.79 272.63
+"Drews" 10.87 7.38 13.07 1.88 48.51 14.01 40.11 5 51.53 274.21
+"Parkhomenko" 11.14 6.61 15.69 2.03 51.04 14.88 41.9 4.8 65.82 277.94
+"Terek" 10.92 6.94 15.15 1.94 49.56 15.12 45.62 5.3 50.62 290.36
+"Gomez" 11.08 7.26 14.57 1.85 48.61 14.41 40.95 4.4 60.71 269.7
+"Turi" 11.08 6.91 13.62 2.03 51.67 14.26 39.83 4.8 59.34 290.01
+"Lorenzo" 11.1 7.03 13.22 1.85 49.34 15.38 40.22 4.5 58.36 263.08
+"Karlivans" 11.33 7.26 13.3 1.97 50.54 14.98 43.34 4.5 52.92 278.67
+"Korkizoglou" 10.86 7.07 14.81 1.94 51.16 14.96 46.07 4.7 53.05 317
+"Uldal" 11.23 6.99 13.53 1.85 50.95 15.09 43.01 4.5 60 281.7
+"Casarsa" 11.36 6.68 14.92 1.94 53.2 15.39 48.66 4.4 58.62 296.12
diff -r 000000000000 -r 79f00bc83ecc test-data/log_file
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/log_file Thu Jan 18 06:20:30 2018 -0500
@@ -0,0 +1,1 @@
+➔ You choose to apply the transformation method : log ✓ Your normalization process is successfull !