1.2_train_expression_check_training

Author

Saideep Gona

Published

November 22, 2023

Code

library(data.table)
library(glmnet)

Loading required package: Matrix

Loaded glmnet 4.1-8

Code

library(glue)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()     masks data.table::between()
✖ tidyr::expand()      masks Matrix::expand()
✖ dplyr::filter()      masks stats::filter()
✖ dplyr::first()       masks data.table::first()
✖ lubridate::hour()    masks data.table::hour()
✖ lubridate::isoweek() masks data.table::isoweek()
✖ dplyr::lag()         masks stats::lag()
✖ dplyr::last()        masks data.table::last()
✖ lubridate::mday()    masks data.table::mday()
✖ lubridate::minute()  masks data.table::minute()
✖ lubridate::month()   masks data.table::month()
✖ tidyr::pack()        masks Matrix::pack()
✖ lubridate::quarter() masks data.table::quarter()
✖ lubridate::second()  masks data.table::second()
✖ purrr::transpose()   masks data.table::transpose()
✖ tidyr::unpack()      masks Matrix::unpack()
✖ lubridate::wday()    masks data.table::wday()
✖ lubridate::week()    masks data.table::week()
✖ lubridate::yday()    masks data.table::yday()
✖ lubridate::year()    masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Code

X <- as.matrix(read.table("/beagle3/haky/users/saideep/projects/aracena_modeling/aracena_predixcan/train_epigenome.txt", row.names = 1))
y_flu <- as.data.frame(read.table("/beagle3/haky/users/saideep/projects/aracena_modeling/aracena_predixcan/train_Flu_aracena.txt", row.names = 1))

glmnet_model_flu <- readRDS("/beagle3/haky/users/saideep/projects/aracena_modeling/elastic_net/trained_eln_RNAseq_Flu_normalized_covariates.linear.rds")
glmnet_predictions_flu <- as.numeric(predict(glmnet_model_flu, X, s = "lambda.min", type="response"))

cor(y_flu, glmnet_predictions_flu)

           [,1]
V2 0.0009384878