siero5335

## ms_dial_filter function
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)  %>%

## PlaidML_Keras_R_layer_output_test_6_1_6_2.R
# 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()

## PlaidML_Keras_R_layer_output_test_5_4.R
# 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"

## MS_spread (For AB Sciex)
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)

## metabolo_test.csv

          
            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
	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) %>%
	# 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()
	# 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"
	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)