Data normalization and integration
Xiaotao Shen PhD (https://www.shenxt.info/)
Created on 2022-01-18 and updated on 2022-08-07
Source:vignettes/data_normalization_integration.Rmd
data_normalization_integration.RmdWe can use masscleaner for data normalization and data
integration.
First, we need to prepare samples for masscleaner.
Data preparation
Load data from previous step.
library(masscleaner)
#> ── Attaching packages ───────────────────────────────────── masscleaner 0.9.2 ──
#> ✓ massdataset 0.99.1 ✓ tinytools 0.9.1
#> ── Conflicts ──────────────────────────────────────── masscleaner_conflicts() ──
#> x methods::body<-() masks base::body<-()
#> x massdataset::filter() masks stats::filter()
#> x methods::kronecker() masks base::kronecker()
library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
#> ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
#> ✓ tibble 3.1.6 ✓ dplyr 1.0.7
#> ✓ tidyr 1.1.4 ✓ stringr 1.4.0
#> ✓ readr 2.1.1 ✓ forcats 0.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> x tidyr::extract() masks magrittr::extract()
#> x dplyr::filter() masks massdataset::filter(), stats::filter()
#> x dplyr::lag() masks stats::lag()
#> x purrr::set_names() masks magrittr::set_names()
load("peak_tables/POS/object")
object
#> --------------------
#> massdataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 5101 x 259 data.frame]
#> 2.sample_info:[ 259 x 6 data.frame]
#> 3.variable_info:[ 5101 x 6 data.frame]
#> 4.sample_info_note:[ 6 x 2 data.frame]
#> 5.variable_info_note:[ 6 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-01-17 23:07:04
#> mutate_variable_na_freq ----------
#> Package Function.used Time
#> 1 massdataset mutate_variable_na_freq() 2022-01-17 23:07:09
#> 2 massdataset mutate_variable_na_freq() 2022-01-17 23:07:09
#> 3 massdataset mutate_variable_na_freq() 2022-01-17 23:07:09
#> filter ----------
#> Package Function.used Time
#> 1 massdataset filter() 2022-01-17 23:07:10
#> impute_mv ----------
#> Package Function.used Time
#> 1 masscleaner impute_mv() 2022-01-17 23:20:44Data quality assessment
We can use the massqc
package to assess the data quality.
library(massqc)
#> ── Attaching packages ────────────────────────────────────────── massqc 0.0.1 ──
#> ✓ ggfortify 0.4.14
#> ── Conflicts ───────────────────────────────────────────── massqc_conflicts() ──
#> x methods::body<-() masks base::body<-()
#> x tidyr::extract() masks magrittr::extract()
#> x dplyr::filter() masks massdataset::filter(), stats::filter()
#> x methods::kronecker() masks base::kronecker()
#> x dplyr::lag() masks stats::lag()
#> x purrr::set_names() masks magrittr::set_names()
object <-
object %>%
activate_mass_dataset(what = "sample_info") %>%
dplyr::mutate(batch = as.character(batch))
object %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
We can see the clear batch effect.
object %>%
activate_mass_dataset(what = "sample_info") %>%
dplyr::filter(class == "QC") %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_sample_boxplot(color_by = "group",
order_by = "injection.order") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
object %>%
activate_mass_dataset(what = "sample_info") %>%
dplyr::filter(class == "QC") %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_rsd_plot()
object %>%
activate_mass_dataset(what = "sample_info") %>%
dplyr::filter(class == "QC") %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_sample_correlation(method = "square", tl.cex = 5) +
theme(axis.text = element_text(size = 5))
Data normalization
Total/median/mean
object1 <-
normalize_data(object, method = "total")
##PCA
object1 %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
object2 <-
normalize_data(object, method = "median")
##PCA
object2 %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
object3 <-
normalize_data(object, method = "mean")
##PCA
object3 %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
Loess/SVR
object4 <-
normalize_data(object, method = "loess")
#>
[33mBatch 1 ...
#>
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[32mLOESS normalization...
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#>
[32mLOESS normalization is done.
#>
[39m
#>
[33mBatch 2 ...
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[32mLOESS normalization...
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[39m
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#>
[32mLOESS normalization is done.
#>
[39m
##PCA
object4 %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
object5 <-
normalize_data(object, method = "svr")
#>
[33mBatch 1 ...
#>
[39m
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#>
[32mSVR normalization is done.
#>
[39m
#>
[33mBatch 2 ...
#>
[39m
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#>
[32mSVR normalization is done.
#>
[39m
##PCA
object5 %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
We select the median method.
object_normalization <- object2Data integration
object_integration <-
integrate_data(object_normalization, method = "qc_median")
##PCA
object_integration %>%
`+`(1) %>%
log(2) %>%
massqc::massqc_pca(color_by = "batch", line = FALSE)
Then we can draw the compare plot for RSD:
qc_id =
object %>%
activate_mass_dataset(what = "sample_info") %>%
filter(class == "QC") %>%
pull(sample_id)
rsd_before =
object %>%
mutate_rsd(according_to_samples = qc_id) %>%
activate_mass_dataset(what = "variable_info") %>%
pull(rsd)
rsd_after =
object_integration %>%
mutate_rsd(according_to_samples = qc_id) %>%
activate_mass_dataset(what = "variable_info") %>%
pull(rsd)
data.frame(rsd_before, rsd_after) %>%
dplyr::mutate(class = dplyr::case_when(rsd_after < rsd_before ~ "Decrease",
rsd_after > rsd_before ~ "Increase",
rsd_after == rsd_before ~ "Equal")) %>%
ggplot(aes(rsd_before, rsd_after, colour = class)) +
ggsci::scale_color_jama() +
geom_abline(slope = 1, intercept = 0) +
geom_point() +
labs(x = "RSD after normalization", y = "RSD before normalization") +
theme_bw()
intensity_plot(object = object, variable_index = 1,
color_by = "group",
order_by = "injection.order", interactive = FALSE) 
intensity_plot(object = object_integration, variable_index = 1,
color_by = "group",
order_by = "injection.order", interactive = FALSE)
Save for next analysis.
save(object_integration, file = "peak_tables/POS/object_integration")Session information
sessionInfo()
#> R Under development (unstable) (2022-01-11 r81473)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur/Monterey 10.16
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> loaded via a namespace (and not attached):
#> [1] rprojroot_2.0.2 digest_0.6.29 crayon_1.4.2 R6_2.5.1
#> [5] jsonlite_1.7.2 magrittr_2.0.1 evaluate_0.14 stringi_1.7.6
#> [9] rlang_0.4.12 cachem_1.0.6 fs_1.5.2 jquerylib_0.1.4
#> [13] bslib_0.3.1 ragg_1.2.1 rmarkdown_2.11 pkgdown_2.0.1
#> [17] textshaping_0.3.6 desc_1.4.0 tools_4.2.0 stringr_1.4.0
#> [21] purrr_0.3.4 yaml_2.2.1 xfun_0.29 fastmap_1.1.0
#> [25] compiler_4.2.0 systemfonts_1.0.3 memoise_2.0.1 htmltools_0.5.2
#> [29] knitr_1.37 sass_0.4.0