Publications by Juan Steibel
Transfer learning example
Transfer and federated learning using GBLUP-GWA Author Juan Steibel Basic assumptions Genotype and phenotype data are available from several populations. In this examples we will download the data to perform computations locally and then transfer results. But in practice, each node can perform their own analyses and transfer only the quantities...
1574 sym Python (17617 sym/57 pcs) 2 img
Power computation for PFOA analyses
Power computation PFOA experiments Keating Lab Author Juan P. Steibel Overall description of the methods For all the power computations we assumed that the response variable reasonably fitted a Gaussian distribution. According to the cited literature, this is a reasonable assumption and in some cases (see Kang et al 2020), the normality assumpt...
3675 sym 3 img
Example of R markdown for reporting
Sample code in R Author JPS Reading a file in R Let’s use an example from class to read a file. The code below read a phenotype file library(lme4) Loading required package: Matrix library(lmerTest) Attaching package: 'lmerTest' The following object is masked from 'package:lme4': lmer The following object is masked from 'package:s...
279 sym Python (5918 sym/18 pcs) 2 img
Summary of meat quality populations
Summary of pork quality populations Phenotype files Meat quality phenotypes and relevant covariates were obtained from three populations described in Bernal Rubio et al. 2015. rm(list = ls()) load("phenotypes.Rdata") ls() [1] "y_comm" "y_marc" "y_msu" eapply(dim,env=.GlobalEnv) $y_comm [1] 1920 14 $y_marc [1] 1237 13 $y_msu [1] ...
1314 sym Python (8320 sym/71 pcs) 3 img
growth data
#Summary of phenodata ##setup and file input Let’s start by loading packages and data: library(tidyverse) ## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ── ## ✔ ggplot2 3.4.0 ✔ purrr 1.0.0 ## ✔ tibble 3.1.8 ...
524 sym R (5288 sym/30 pcs) 4 img
Intro to fixed effects models
Intro to Linear Models Juan Steibel Basic Linear model \[ \color{blue}{y_i} = \sum_{j=1}^{p} {\color{red}{x_{ij}} \color{darkgreen}{\beta_j}}+\color{orange}{e_i} \] \[ E(\color{orange}{e_i}) = 0 \qquad(1)\]\(\color{blue}{y_i}\) observation of response variable, \(\color{red}{x_i}\) : observation of predictor variable, \(\color{darkgreen}{...
8712 sym 3 img
read file benchmark
##Why benchmark read functions reading large flat text files is one of the most common tasks in data science and in quantitative genomic applications. There are always multiple ways of performing a task in any programming language and reading files in R is no exception. Two aspects are worth considering when benchmarking file reading functions in...
934 sym R (792 sym/19 pcs)
reading and formatting genotypes
How to read genotypes with Hypore format This is based on code provided by Austin Putz with a minor mod. library(tidyverse) #it includes readr setwd("D:/Breed composition hypore") data.geno<-read_delim("genotypes_Hypor_JP_9010_2012-09-29.dat",delim = " ",col_names = FALSE) dim(data.geno) ## [1] 9010 3 colnames(data.geno)<-c("Animal_ID","Br...
241 sym R (931 sym/7 pcs)
Analysis of extra variables
This is an analysis of significance for extra measurements in affected dogs The characteristics of this dataset are: 1) small number of dogs (n=4) and small number of repeated measures: (N=4-7) at different timepoints. The main goal is to perform a mixed model analysis to test the hypothesis of interaction of time and treatment while accounting f...
1020 sym R (36485 sym/219 pcs)
Example meta analysis
The data For this example I will use a dataset that I will cut in two to pretent they are two different populations. This is just for illustration purposes. setwd("C:/Users/marti/OneDrive/Documents/GWA") load("data_meta.RData") ls() ## [1] "G" "map" "pheno" "Z" partition<-sample(1:2,nrow(G),replace = T) pheno_1<-pheno[partition==1,] ph...
334 sym R (2907 sym/18 pcs) 5 img