From 26deaa5ed343074ac19bfaf5f3254f670647351c Mon Sep 17 00:00:00 2001 From: mkerin Date: Sun, 25 Jul 2021 18:22:10 +0100 Subject: [PATCH] Bump to version 1.0.3 --- CMakeLists.txt | 2 +- docs/gplemma.md | 4 ++-- docs/lemma.md | 16 ++++++++-------- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f2cc4e11..f986312e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,5 +1,5 @@ cmake_minimum_required(VERSION 3.7.2) -project(LEMMA VERSION 1.0.2) +project(LEMMA VERSION 1.0.3) set(CMAKE_CXX_STANDARD 11) diff --git a/docs/gplemma.md b/docs/gplemma.md index f74bc70f..4a845dd7 100644 --- a/docs/gplemma.md +++ b/docs/gplemma.md @@ -18,7 +18,7 @@ The phenotype has been simulated to have: ## Getting started To run the GPLEMMA method on the dataset given above, run the following commands ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --gplemma --random-seed 1 \ --pheno example/pheno.txt.gz \ --streamBgen example/n5k_p20k_example.bgen \ @@ -29,7 +29,7 @@ This should return heritability estimates of h2-G = 0.229 (0.028) and h2-GxE = 0 To run the MEMMA method on the same dataset, run the following commands ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --RHEreg --random-seed 1 \ --pheno example/pheno.txt.gz \ --streamBgen example/n5k_p20k_example.bgen \ diff --git a/docs/lemma.md b/docs/lemma.md index 4a0a1275..e05216b8 100644 --- a/docs/lemma.md +++ b/docs/lemma.md @@ -29,7 +29,7 @@ for cc in `seq 1 22`; do echo "example/n5k_p20k_example_chr${cc}.bgen" >> example/bgen_filenames.txt; done -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --pheno example/pheno.txt.gz \ --environment example/env.txt.gz \ --VB \ @@ -63,7 +63,7 @@ The LEMMA algorithm is modular, and so each step can be performed separately as ### Running the LEMMA variational inference algorithm ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --VB \ --pheno example/pheno.txt.gz \ --environment example/env.txt.gz \ @@ -74,7 +74,7 @@ In this case the algorithm should converge in 59 iterations. ### Association testing with imputed SNPs ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --singleSnpStats --maf 0.01 \ --pheno example/pheno.txt.gz \ --resid-pheno example/inference_converged_yhat.out.gz \ @@ -88,7 +88,7 @@ For analyses of large genomic datasets it may be useful to parallelize associati ### Heritability estimation ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --RHEreg --random-seed 1 \ --pheno example/pheno.txt.gz \ --mStreamBgen example/bgen_filenames.txt \ @@ -122,7 +122,7 @@ zcat example/dxteex_chr*.out.gz > example/dxteex.out.gz ``` Then provide the file `example/dxteex.out.gz` to LEMMA with the commandline flag `--dxteex`. ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --VB \ --pheno example/pheno.txt.gz \ --environment example/env.txt.gz \ @@ -141,7 +141,7 @@ To convert into the file format expected by LEMMA we have provided a brief Rscri Then run the heritability analysis as follows ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --RHEreg --n-RHEreg-samples 20 --n-RHEreg-jacknife 100 --random-seed 1 \ --pheno example/pheno.txt.gz \ --bgen example/n5k_p20k_example.bgen \ @@ -153,7 +153,7 @@ mpirun -n 1 build/lemma_1_0_1 \ ### Resuming from a previous parameter state In case of runtime crashes, LEMMA can save the parameter state at periodic intervals by providing the commandline flag `--resume-from-state`. LEMMA can then subsequently resume inference from this saved state. For example ``` -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --VB \ --pheno example/pheno.txt.gz \ --environment example/env.txt.gz \ @@ -161,7 +161,7 @@ mpirun -n 1 build/lemma_1_0_1 \ --param_dump_interval 10 \ --out example/inference.out.gz -mpirun -n 1 build/lemma_1_0_1 \ +mpirun -n 1 build/lemma_1_0_3 \ --VB \ --pheno example/pheno.txt.gz \ --environment example/env.txt.gz \