kstawiski

nasza = read.csv("nasza.csv", header=T, stringsAsFactors = F)



library("openxlsx")
library(dplyr)
miRtarbase = openxlsx::read.xlsx("hsa_MTI.xlsx")
refdb = filter(miRtarbase, Support.Type == "Functional MTI" | Support.Type == "Non-Functional MTI")

miR = unique(nasza$hsa.miRNA)

kstawiski

library(plyr)
library(dplyr)
library(edgeR)
library(epiDisplay)
library(rsq)
library(MASS)
library(Biocomb)
library(caret)
library(dplyr)
library(epiDisplay)

kstawiski

suppressMessages(library(rmdformats))
suppressMessages(library(dplyr))
suppressMessages(library(plyr))
suppressMessages(library(tidyverse))
suppressMessages(library(reshape2))
suppressMessages(library(xlsx))
suppressMessages(library(psych))
suppressMessages(library(knitr))
suppressMessages(library(gplots))
suppressMessages(library(car))

kstawiski

library(OmicSelector); set.seed(1);
if(!dir.exists("/OmicSelector/temp")) { dir.create("/OmicSelector/temp") }
OmicSelector_load_extension("deeplearning")
library(data.table)
#use_session_with_seed(1)
#selected_miRNAs = c("hsa.miR.192.5p",
#                    "hsa.let.7g.5p",
#                    "hsa.let.7a.5p",
#                    "hsa.let.7d.5p",
#                    "hsa.miR.194.5p",

kstawiski

library(readxl)
library(dplyr)
dane <- read_excel("dataset_uzupelniony.xlsx") %>% mutate_if(is.character,funs(factor(.)))

kstawiski

library(ggpubr)
library(ggsci)
temp = data.frame("Przyjęcie" = dane$Poczatkowe.stezenie.kreatyniny.w.trakcie.hospitalizacji., "Wypis" = dane$Stezenie.kreatyniny.przy.wypisie.)
a = ggpaired(temp, cond1 = "Przyjęcie", cond2 = "Wypis", palette = "npg", xlab = "", ylab = "Kreatynina", line.color = "grey", point.size = 0.01)
describe(temp)
wilcox.test(temp$Przyjęcie, temp$Wypis, paired = T)
t.test(temp$Przyjęcie, temp$Wypis, paired = T)

gridExtra::grid.arrange(a, ncol = 1)

kstawiski

# -*- coding: utf-8 -*-
options(reticulate.conda_binary = "/opt/conda/bin/conda") # conda path

library(survival)
library(ranger)
library(ggplot2)
library(dplyr)
library(readr)

completed = read_csv("pirads_v0_complete.csv")

kstawiski

#!/bin/bash
wget https://github.com/sebanc/brunch/releases/download/r97-stable-20220121/brunch_r97_stable_20220121.tar.gz
# Chrome OS image: https://cros-updates-serving.appspot.com/
wget https://dl.google.com/dl/edgedl/chromeos/recovery/chromeos_14324.62.0_rammus_recovery_stable-channel_mp-v2.bin.zip

sudo apt install -y pv cgpt
tar xvzf brunch_*.tar.gz
unzip chromeos_*.bin.zip

sudo bash chromeos-install.sh -src chromeos_*.bin -dst chromeos.img -s 32 # 32 GB image

kstawiski

mklink /J "C:\Projekty\" "C:\Users\Konrad\OneDrive - Uniwersytet Medyczny w Łodzi\"

kstawiski

# This code is a part of paper entitled: "Deep learning-based integration of circulating and cellular miRNAs expression of pancreatic cancer"
# Author: Konrad Stawiski, MD, PhD (konrad@konsta.com.pl; https://konsta.com.pl)
# Based on https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/1472-6947-12-8

library(dplyr)
setwd("C:/Users/konra/OneDrive/Doktorat/Analiza/Szacowanie")
set.seed(1)

# Get exemplary file
Elias2017 <- data.table::fread("Elias2017.csv")