.Rhistory_冲突文件_DELL_20230617123831

library(blogdown)
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library(blogdown)
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blogdown::serve_site()
blogdown::stop_server()
blogdown::build_site()
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blogdown::stop_server()
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blogdown::serve_site()
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library(celltalker)
#-------------------------P4 sagittal MALDI-TOF imaging--------------------------#
{
#################################Load library needed
library(Cardinal)
library(dplyr)
library(Seurat)   ###Seurat analysis pipeline
}
#######################################Analysis our Rapidflex MALDI-TOF/TOF data;
setwd("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/")
msi_folder <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/"
name_list <- list.files(path = "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/", pattern='.imzML')
name_1 <- substr(name_list[1], 1, nchar(name_list[1])-6)
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
name_1
name_list
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
#######################################Analysis our Rapidflex MALDI-TOF/TOF data;
setwd("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/")
msi_folder <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/"
name_list <- list.files(path = "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/", pattern='.imzML')
name_1 <- substr(name_list[1], 1, nchar(name_list[1])-6)
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
MSI_obj
name_1
MSI_obj@featureData@resolution
################Flip the image;
MSI_obj@elementMetadata@coord@listData[["x"]] <- -(MSI_obj@elementMetadata@coord@listData[["x"]])
MSI_obj@elementMetadata@coord@listData[["y"]] <- -(MSI_obj@elementMetadata@coord@listData[["y"]])
################Pre-processing;
###We calculate the mean spectrum using summarizeFeatures().
#Fun: A function or list of functions that return statistical summaries.
#Known statistics can be provided to be computed more efficiently than providing the equivalent function.
#Known statistics include "min", "max", "mean", "sum", "sd", and "var".
MSI_obj_mean <- summarizeFeatures(MSI_obj, "mean")
dir.create("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/")
plot_path <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/"
plot(MSI_obj_mean, main = "Mean_Neg_1-5DAN_human_tumor")
png(paste0(plot_path, "Mean_Neg_mode_1-5DAN_2022_0909_human_tumor.png"), width =1200, height = 400)
plot(MSI_obj_mean, main = "Mean_Neg_1-5DAN_human_tumor")
dev.off()
######################Apply plotly plot to interactively visualize TIC plot, and check individual peaks;
feature_data_mean <- as.data.frame(cbind(MSI_obj_mean@featureData@mz, MSI_obj_mean@featureData$mean))
colnames(feature_data_mean) <- c("mz", "mean")
library(plotly)
###################normalize all pixels with TIC method;
##In order to make the mass spectra comparable between different pixels, it is necessary to normalize the data. We will use TIC normalization.
##Let’s calculate the TIC to see how it currently varies across the dataset in the raw, unprocessed specra.
###summarizePixels(x, FUN = "mean", ...)   FUN = "sum";
# summarize image by TIC
lj_100um_mid_tic <- summarizePixels(MSI_obj, c(tic="sum"))
###########All m/z;
all_mz <- MSI_obj@featureData@mz
all_mz <- as.data.frame(all_mz)
#############################################setting the color bar;
#library(devtools)
#install_github("marchtaylor/sinkr")
library(sinkr) # https://github.com/marchtaylor/sinkr
ncolors <- 256
colors <- jetPal(ncolors)
setwd("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/")
#################################export all mz spectrum into one pdf and have a profile view;
##dir.create("D:/lijun_2022/lijun_All_data/2022_04_12_in_guodian_MALDI/neg_wholebrain_batch_A")
plot_path <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/"
################create a reference peaks;
###To process the dataset, we will first perform peak picking on the mean spectrum to create a set of reference peaks.
###We will then bin the peaks in the entire dataset to this reference.
lj_100um_mid_ref <- MSI_obj_mean %>%
peakPick(SNR=3) %>%        ###SNR; The minimum signal-to-noise ratio to be considered a peak.
peakAlign(ref="mean",     ###ref; A reference to which to align the peaks.
tolerance=0.5,  ###The tolerance to be used when aligning detected peaks to the reference. If this is NA, then automatically guess a tolerance from the data.
units="mz") %>%  ###The units to use for the tolerance. Either parts-per-million or the raw m/z values.
peakFilter() %>%          ###
process()
###Now we bin the rest of the dataset to the reference peaks.
lj_100um_mid_peaks <- MSI_obj %>%
normalize(method="tic") %>%
peakBin(ref=mz(lj_100um_mid_ref),
tolerance=0.26,    ###units = c("ppm", "mz")   #######0.26, manual measured
units="mz") %>%   ###The units to use for the tolerance.
process()
lj_100um_mid_peaks    ###(tol = 0.26 mz)
length(lj_100um_mid_peaks@featureData@mz)
image(MSI_obj, mz=lj_100um_mid_peaks@featureData@mz[100], plusminus=0.1, colorscale = colors, contrast.enhance = c("suppression"))
#################Export result MSI after peak picking;
mz_array_peaks <- lj_100um_mid_peaks@featureData@mz
pdf(paste0(plot_path, "human_tumor_after_peak_picking_378_features_1_5_DAN_neg_09_09.pdf"), width = 35.1/8, height = 23.6/8)
4.13、2
4.13/2
35.1/8
23.6/8
pdf(paste0(plot_path, "human_tumor_after_peak_picking_378_features_1_5_DAN_neg_09_09.pdf"), width = 4.13, height = 5.21)
###4.13 * 5.21
for(i in 1:length(mz_array_peaks)){
p <- image(MSI_obj, mz=mz_array_peaks[i], plusminus=0.1, colorscale = colors, contrast.enhance = c("suppression"))
print(p)
}
dev.off()
name_list <- list.files(path = "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/", pattern='.imzML')
name_1 <- substr(name_list[2], 1, nchar(name_list[1])-6)
name_1
gc()
#-------------------------P4 sagittal MALDI-TOF imaging--------------------------#
{
#################################Load library needed
library(Cardinal)
library(dplyr)
library(Seurat)   ###Seurat analysis pipeline
}
#######################################Analysis our Rapidflex MALDI-TOF/TOF data;
setwd("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/")
msi_folder <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/"
name_list <- list.files(path = "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/", pattern='.imzML')
name_1 <- substr(name_list[2], 1, nchar(name_list[1])-6)
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
MSI_obj
name_list <- list.files(path = "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/", pattern='.imzML')
name_1 <- substr(name_list[2], 1, nchar(name_list[1])-6)
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
name_1
name_1 <- substr(name_list[2], 1, nchar(name_list[1])-7)
MSI_obj <- readImzML(name_1, folder = msi_folder, attach.only = TRUE,
mass.range = NULL, resolution = NA, units = c("ppm", "mz"),
as = "MSImagingExperiment", parse.only = FALSE,
BPPARAM = getCardinalBPPARAM())
MSI_obj
MSI_obj@featureData@resolution
################Flip the image;
MSI_obj@elementMetadata@coord@listData[["x"]] <- -(MSI_obj@elementMetadata@coord@listData[["x"]])
MSI_obj@elementMetadata@coord@listData[["y"]] <- -(MSI_obj@elementMetadata@coord@listData[["y"]])
################Pre-processing;
###We calculate the mean spectrum using summarizeFeatures().
#Fun: A function or list of functions that return statistical summaries.
#Known statistics can be provided to be computed more efficiently than providing the equivalent function.
#Known statistics include "min", "max", "mean", "sum", "sd", and "var".
MSI_obj_mean <- summarizeFeatures(MSI_obj, "mean")
###dir.create("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/")
plot_path <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/"
png(paste0(plot_path, "Mean_Neg_mode_1-5DAN_20um_2022_0909_human_tumor.png"), width =1200, height = 400)
plot(MSI_obj_mean, main = "Mean_Neg_1-5DAN_20um_human_tumor")
dev.off()
######################Apply plotly plot to interactively visualize TIC plot, and check individual peaks;
feature_data_mean <- as.data.frame(cbind(MSI_obj_mean@featureData@mz, MSI_obj_mean@featureData$mean))
colnames(feature_data_mean) <- c("mz", "mean")
library(plotly)
fig_mid_mean <- plot_ly(data = feature_data_mean, x = ~mz, y =~mean, mode = 'lines')
###################normalize all pixels with TIC method;
##In order to make the mass spectra comparable between different pixels, it is necessary to normalize the data. We will use TIC normalization.
##Let’s calculate the TIC to see how it currently varies across the dataset in the raw, unprocessed specra.
###summarizePixels(x, FUN = "mean", ...)   FUN = "sum";
# summarize image by TIC
lj_100um_mid_tic <- summarizePixels(MSI_obj, c(tic="sum"))
###########All m/z;
all_mz <- MSI_obj@featureData@mz
all_mz <- as.data.frame(all_mz)
#############################################setting the color bar;
#library(devtools)
#install_github("marchtaylor/sinkr")
library(sinkr) # https://github.com/marchtaylor/sinkr
ncolors <- 256
colors <- jetPal(ncolors)
setwd("E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/")
#################################export all mz spectrum into one pdf and have a profile view;
##dir.create("D:/lijun_2022/lijun_All_data/2022_04_12_in_guodian_MALDI/neg_wholebrain_batch_A")
plot_path <- "E:/LC_MS_raw_data/20220909_gelab_human_tumor/NIN-tumor/export_plot/"
################create a reference peaks;
###To process the dataset, we will first perform peak picking on the mean spectrum to create a set of reference peaks.
###We will then bin the peaks in the entire dataset to this reference.
lj_100um_mid_ref <- MSI_obj_mean %>%
peakPick(SNR=3) %>%        ###SNR; The minimum signal-to-noise ratio to be considered a peak.
peakAlign(ref="mean",     ###ref; A reference to which to align the peaks.
tolerance=0.5,  ###The tolerance to be used when aligning detected peaks to the reference. If this is NA, then automatically guess a tolerance from the data.
units="mz") %>%  ###The units to use for the tolerance. Either parts-per-million or the raw m/z values.
peakFilter() %>%          ###
process()
###Now we bin the rest of the dataset to the reference peaks.
lj_100um_mid_peaks <- MSI_obj %>%
normalize(method="tic") %>%
peakBin(ref=mz(lj_100um_mid_ref),
tolerance=0.26,    ###units = c("ppm", "mz")   #######0.26, manual measured
units="mz") %>%   ###The units to use for the tolerance.
process()
lj_100um_mid_peaks    ###(tol = 0.26 mz)
length(lj_100um_mid_peaks@featureData@mz)
image(MSI_obj, mz=lj_100um_mid_peaks@featureData@mz[100], plusminus=0.1, colorscale = colors, contrast.enhance = c("suppression"))
#################Export result MSI after peak picking;
mz_array_peaks <- lj_100um_mid_peaks@featureData@mz
pdf(paste0(plot_path, "human_tumor_20um_after_peak_picking_250_features_1_5_DAN_neg_09_09.pdf"), width = 3.01, height = 2.53)
###width = 3.01, height = 2.53
for(i in 1:length(mz_array_peaks)){
p <- image(MSI_obj, mz=mz_array_peaks[i], plusminus=0.1, colorscale = colors, contrast.enhance = c("suppression"))
print(p)
}
dev.off()
gc()
setwd("D:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
setwd("D:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
library(blogdown)
blogdown::serve_site()
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
#library(shinyjs)
library(shiny)
library(DT)
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
#library(shinyjs)
library(shiny)
library(DT)
library(Cardinal)   ####???
install.packages("Cardinal")
install.packages("BiocManager")
BiocManager::install("Cardinal")
install.packages("locfit")
library(Cardinal)   ####???
library(plotly)
library(ggplot2)
library(Seurat)
#library(fontawesome)
library(bs4Dash)
library(tidyverse)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("Cardinal")
library(Cardinal)   ####???
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("EBImage")
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("EBImage", force = TRUE)
library(Seurat)
#library(fontawesome)
library(bs4Dash)
library(tidyverse)
library(shinydashboard)
library(DT)
library(blogdown)
library(blogdown)
blogdown::serve_site()
blogdown::stop_server()
blogdown::build_site()
blogdown::serve_site()
blogdown::serve_site()
blogdown::stop_server()
library(blogdown)
setwd("D:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
blogdown::serve_site()
install.packages("leaflet")
library(blogdown)
blogdown::serve_site()
blogdown::stop_server()
blogdown::build_site()
library(blogdown)
blogdown::serve_site()
592-450
143*3
20^4
20^5
20^6
20^7
4574-3955
library(blogdown)
setwd("D:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
blogdown::serve_site()
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
## biocLite("BiocUpgrade") ## you may need this
biocLite("ggtree")
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ggtree")
library(ggtree)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ggtree")
install.packages("dplyr")
install.packages("dplyr")
library(ggtree)
library(blogdown)
setwd("F:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
blogdown::serve_site()
blogdown::stop_server()
blogdown::serve_site()
install.packages("edfReader")
library(blogdown)
setwd("F:/BaiduSyncdisk/lijun_All_codes/leo1234j_website/leo1234jv4/")
blogdown::serve_site()
blogdown::stop_server()
blogdown::build_site()
library(ggpubr)
# Create a simple bar plot
ggbarplot(
ToothGrowth, x = "dose", y = "len",
add = c("mean_se", "jitter"),
fill = "#BF504D"
)
ToothGrowth
# Grouped bar plots
# Colored by groups
ggbarplot(
ToothGrowth, x = "dose", y = "len",
add = c("mean_sd", "jitter"),
color = "supp", palette = c("#807F7F", "#BF504D"),
position = position_dodge(0.8)
)
# Create a simple bar plot
ggbarplot(
ToothGrowth, x = "dose", y = "len",
add = c("mean_se", "jitter"),
fill = "#BF504D"
)
# Change bars fill color by groups
# You need to change also point shapes by groups
ggbarplot(
ToothGrowth, x = "dose", y = "len",
add = c("mean_sd", "jitter"),
add.params = list(shape = "supp"),
fill= "supp", palette = c("#807F7F", "#BF504D"),
position = position_dodge(0.8)
)
# Create a simple bar plot
ggbarplot(
ToothGrowth, x = "dose", y = "len",
add = c("mean_se", "jitter"),
fill = "#BF504D"
)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("msa")
BiocManager::install("msa", force = TRUE)
library(msa)
## ----LoadPackageToDetermineVersion,echo=FALSE,message=FALSE,results='hide'----
options(width=65)
set.seed(0)
library(msa)
library(seqinr)
msaVersion <- packageDescription("msa")$Version
msaDateRaw <- packageDescription("msa")$Date
msaDateYear <- as.numeric(substr(msaDateRaw, 1, 4))
msaDateMonth <- as.numeric(substr(msaDateRaw, 6, 7))
msaDateDay <- as.numeric(substr(msaDateRaw, 9, 10))
msaDate <- paste(month.name[msaDateMonth], " ",
msaDateDay, ", ",
msaDateYear, sep="")
## ----SimpleExFileNames-----------------------------------------
mySequenceFile <- system.file("examples", "exampleAA.fasta", package="msa")
mySequences <- readAAStringSet(mySequenceFile)
mySequences
## ----doAlignment-----------------------------------------------
myFirstAlignment <- msa(mySequences)
myFirstAlignment
## ----showWholeWidth--------------------------------------------
print(myFirstAlignment, show="complete")
## ----VisualizePDF,results='asis'-------------------------------
msaPrettyPrint(myFirstAlignment, y=c(164, 213), output="asis",
showNames="none", showLogo="none", askForOverwrite=FALSE)
## ----OtherAlgorithms-------------------------------------------
myClustalWAlignment <- msa(mySequences, "ClustalW")
myClustalWAlignment
## ----maskExample-----------------------------------------------
myMaskedAlignment <- myFirstAlignment
colM <- IRanges(start=1, end=100)
colmask(myMaskedAlignment) <- colM
myMaskedAlignment
## ----Hemoglobin2-----------------------------------------------
library(seqinr)
d <- dist.alignment(hemoAln2, "identity")
as.matrix(d)[2:5, "HBA1_Homo_sapiens", drop=FALSE]
## ----HemoglobinTree,output.width='0.8\\textwidth',output.height='0.5\\textwidth',message=FALSE,results='hide'----
library(ape)
hemoTree <- nj(d)
## ----ShowConsensusBottom,results="asis"------------------------
msaPrettyPrint(myFirstAlignment, output="asis", y=c(164, 213),
subset=c(1:6), showNames="none", showLogo="none",
consensusColor="ColdHot", showLegend=FALSE,
askForOverwrite=FALSE)
## ----conservationExample1--------------------------------------
data(BLOSUM62)
msaConservationScore(myFirstAlignment, BLOSUM62)
## ----conservationExample2--------------------------------------
msaConservationScore(myFirstAlignment, BLOSUM62, gapVsGap=0,
type="upperlower", thresh=c(40, 20))
## ----conservationExample3--------------------------------------
msaConservationScore(myMaskedAlignment, BLOSUM62, gapVsGap=0,
type="upperlower", thresh=c(40, 20))
## ----Hemoglobin1-----------------------------------------------
hemoSeq <- readAAStringSet(system.file("examples/HemoglobinAA.fasta",
package="msa"))
hemoAln <- msa(hemoSeq)
hemoAln
hemoAln2 <- msaConvert(hemoAln, type="seqinr::alignment")
## ----Hemoglobin2-----------------------------------------------
library(seqinr)
d <- dist.alignment(hemoAln2, "identity")
as.matrix(d)[2:5, "HBA1_Homo_sapiens", drop=FALSE]
## ----HemoglobinTree,output.width='0.8\\textwidth',output.height='0.5\\textwidth',message=FALSE,results='hide'----
library(ape)
hemoTree <- nj(d)
plot(hemoTree, main="Phylogenetic Tree of Hemoglobin Alpha Sequences")