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Component Name | Food | glyoxylate | glycolate | pyruvate | lactate | 2-oxobutanoate | acetoacetate | glycerate | uracil | fumarate | Maleic acid | 2-keto-isovalerate | Guanidoacetic acid | succinate | Methylmalonic acid | 3-S-methylthiopropionate | nicotinate | taurine | Pyroglutamic acid | Citraconic acid | 2-ketohaxanoic acid | N-Acetyl-L-alanine | oxaloacetate | Hydroxyisocaproic acid | malate | hypoxanthine | anthranilate | p-aminobenzoate | p-hydroxybenzoate | acetylphosphate | Carbamoyl phosphate | a-ketoglutarate | Phenylpropiolic acid | 2-oxo-4-methylthiobutanoate | 2-Hydroxy-2-methylbutanedioic acid | 3-methylphenylacetic acid | xanthine | Hydroxyphenylacetic acid | 2,3-dihydroxybenzoic acid | orotate | dihydroorotate | allantoin | Aminoadipic acid | Indole-3-carboxylic acid | phenylpyruvate | Atrolactic acid | Phenyllactic acid | quinolinate | phosphoenolpyruvate | Uric acid | dihydroxy-acetone-phosphate | D-glyceraldehdye-3-phosphate | sn-glycerol-3-phosphate | shikimate | aconitate | allantoate | Ascorbic acid | 2-Isopropylmalic acid | N-carbamoyl-L-aspartate_neg | Pyrophosphate | glucono-D-lactone | myo-inositol | hy |
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library(readxl) | |
library(tidyr) | |
library(dplyr) | |
df_XX <- read_excel(path = "hoge.xlsx", | |
sheet = 1, | |
na = "N/A") | |
Metabo <- df_XX %>% spread("Component Name", Area) |
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# tensorflow backend | |
# original: https://github.com/jjallaire/deep-learning-with-r-notebooks/blob/master/notebooks/5.4-visualizing-what-convnets-learn.Rmd | |
library(keras) | |
model <- application_vgg16( | |
weights = "imagenet", | |
include_top = FALSE | |
) | |
layer_name <- "block3_conv1" |
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library(tidyverse) | |
ms_dial_filter <- function(df, adduct, annotatelevel, cv) { | |
df %>% dplyr::select(`Metabolite name`, `Adduct type`, `Annotation tag (VS1.0)`, `Total score`, | |
`RT similarity`, `Average Rt(min)`, INCHIKEY , 33:ncol(df)) %>% | |
filter(`Annotation tag (VS1.0)` %in% annotatelevel) %>% | |
filter(`Adduct type` %in% adduct) %>% | |
mutate(qc_mean = rowMeans(dplyr::select(., contains("QC")))) %>% | |
filter(qc_mean != 0) %>% | |
mutate(qc_cv = apply(dplyr::select(., contains("QC")), 1, sd)/ qc_mean * 100) %>% |
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# Reference: https://github.com/jjallaire/deep-learning-with-r-notebooks | |
library(keras) | |
use_backend(backend = "plaidml") | |
# 6-1 original ver | |
# get_layer(model, index = 2) %>% | |
# set_weights(list(embedding_matrix)) %>% | |
# freeze_weights() |