diff --git a/README.md b/README.md
index 9e2189b2a4d4cecfac3328788220d6442a816640..2866268558f5efb3a5084c4ad5fe6dc610df5585 100644
--- a/README.md
+++ b/README.md
@@ -1,40 +1,31 @@
VarExp
======
-The R package **VarExp** provides functions for the estimating of the
-percentage of phenotypic variance explained by genetic effects,
-interaction effects or jointly by both effects. This suite of functions
-are useful for meta-analysis designs where pooling individual genotype
-data is challenging. A pre-print article related to this work is
-available [here](bioRkiv%20link)
+The R package **VarExp** provides functions for the estimating of the percentage of phenotypic variance explained by genetic effects, interaction effects or jointly by both effects. This suite of functions are useful for meta-analysis designs where pooling individual genotype data is challenging. A pre-print article related to this work is available [here](bioRkiv%20link)
Prerequisite
------------
-Library
-[**Rcurl**](https://cran.r-project.org/web/packages/RCurl/index.html) is
-required to run **VarExp**
+Library [**Rcurl**](https://cran.r-project.org/web/packages/RCurl/index.html) is required to run **VarExp**
Installation
------------
-For now, **VarExp** can be installed only using package source. In R,
-after setting your working directory to *VarExp\_0.1.0.tar.gz* location,
-type:
+For now, **VarExp** can be installed only using package source. In R, after setting your working directory to *VarExp\_0.1.0.tar.gz* location, type:
- install.packages("VarExp_0.1.0.tar.gz", repos = NULL, type = "source")
+``` r
+install.packages("VarExp_0.1.0.tar.gz", repos = NULL, type = "source")
+```
Input format
------------
Two input files are required.
-- A file providing the meta-analysis results with the following
- mandatory columns:
+- A file providing the meta-analysis results with the following mandatory columns:
- the rs identifier of the variant
- the chromosome number on which the variant is
- - the physical position of the variant (currently in NCBI Build
- B37)
+ - the physical position of the variant (currently in NCBI Build B37)
- the tested allele of the variant
- the frequency of the allele A0
- the regression coefficient of the main genetic effect
@@ -54,9 +45,7 @@ Two input files are required.
## rs7538305 1 824398 C 0.15379 0.054950590 -0.04494799
## rs28613513 1 1112810 T 0.05358 -0.001334013 0.10294423
-- A file providing the summary statistics for the outcome and the
- exposure in each individual cohort included in the meta-analysis.
- Mandatory columns of this file are:
+- A file providing the summary statistics for the outcome and the exposure in each individual cohort included in the meta-analysis. Mandatory columns of this file are:
- the identifier of the cohort
- the sample size of the cohort
- the phenotype mean in the cohort
@@ -73,44 +62,44 @@ Two input files are required.
## 4 10000 1.342020 3.151429 1.999943 1.256718
## 5 10000 1.385564 3.153274 2.002401 1.235129
-Note that in the case of a binary exposure, the two latter columns can
-be replaced by a single column providing the count of exposed
-individuals in each cohort.
+Note that in the case of a binary exposure, the two latter columns can be replaced by a single column providing the count of exposed individuals in each cohort.
Short tutorial
--------------
Data used in this tutorial are included in the ***VarExp*** package.
- # Load the package
- library(VarExp)
+``` r
+# Load the package
+library(VarExp)
- # Load the meta-analysis summary statistics file
- data(GWAS)
+# Load the meta-analysis summary statistics file
+data(GWAS)
- # Load the cohort description file
- data(COHORT)
+# Load the cohort description file
+data(COHORT)
- # Compute the genotype correlation matrix from the reference panel
- C <- getGenoCorMatrix(GWAS$RSID, GWAS$CHR, GWAS$POS, GWAS$A0, "EUR", pruning = FALSE)
+# Compute the genotype correlation matrix from the reference panel
+C <- getGenoCorMatrix(GWAS$RSID, GWAS$CHR, GWAS$POS, GWAS$A0, "EUR", pruning = FALSE)
- # Make sure SNPs in the GWAS data and in the correlation matrix match
- # Necessary if pruning = TRUE, otherwise should have no effect
- GWAS <- checkInput(GWAS, colnames(C))
+# Make sure SNPs in the GWAS data and in the correlation matrix match
+# Necessary if pruning = TRUE, otherwise should have no effect
+GWAS <- checkInput(GWAS, colnames(C))
- # Retrieve mean and variance of the exposure and the phenotype
- # from individual cohort summary statistics
- parsY <- calculateParamsFromIndParams(COHORT$PHENO_N, COHORT$PHENO_Mean, COHORT$PHENO_SD)
- parsE <- calculateParamsFromIndParams(COHORT$PHENO_N, COHORT$EXPO_Mean, COHORT$EXPO_SD)
+# Retrieve mean and variance of the exposure and the phenotype
+# from individual cohort summary statistics
+parsY <- calculateParamsFromIndParams(COHORT$PHENO_N, COHORT$PHENO_Mean, COHORT$PHENO_SD)
+parsE <- calculateParamsFromIndParams(COHORT$PHENO_N, COHORT$EXPO_Mean, COHORT$EXPO_SD)
- # Re-scale effect sizes as if estimated in a standardized model
- std_betaG <- standardizeBeta(GWAS$MAIN_EFFECT, GWAS$INT_EFFECT, GWAS$FREQ_A0, parsE[1], parsE[2], type = "G")
- std_betaI <- standardizeBeta(GWAS$MAIN_EFFECT, GWAS$INT_EFFECT, GWAS$FREQ_A0, parsE[1], parsE[2], type = "I")
+# Re-scale effect sizes as if estimated in a standardized model
+std_betaG <- standardizeBeta(GWAS$MAIN_EFFECT, GWAS$INT_EFFECT, GWAS$FREQ_A0, parsE[1], parsE[2], type = "G")
+std_betaI <- standardizeBeta(GWAS$MAIN_EFFECT, GWAS$INT_EFFECT, GWAS$FREQ_A0, parsE[1], parsE[2], type = "I")
- # Estimation of the fraction of variance explained
- fracG <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "G")
- fracI <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "I")
- fracJ <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "J")
+# Estimation of the fraction of variance explained
+fracG <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "G")
+fracI <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "I")
+fracJ <- calculateVarFrac(std_betaG, std_betaI, C, parsY[2], sum(COHORT$PHENO_N), "J")
+```
Bug report / Help
-----------------
@@ -120,13 +109,9 @@ Please open an issue if you find a bug.
Code of conduct
---------------
-Please note that this project is released with a [Contributor Code of
-Conduct](https://gitlab.pasteur.fr/statistical-genetics/VarExp/blob/master/code-of-conduct.md).
-By participating in this project you agree to abide by its terms.
+Please note that this project is released with a [Contributor Code of Conduct](https://gitlab.pasteur.fr/statistical-genetics/VarExp/blob/master/code-of-conduct.md). By participating in this project you agree to abide by its terms.
License
-------
-This project is licensed under the MIT License - see the
-[LICENSE.md](https://gitlab.pasteur.fr/statistical-genetics/VarExp/blob/master/LICENSE)
-file for details
+This project is licensed under the MIT License - see the [LICENSE.md](https://gitlab.pasteur.fr/statistical-genetics/VarExp/blob/master/LICENSE) file for details