#BiocManager::install("mixOmicsTeam/mixOmics")
library(mixOmics)
## Loading required package: MASS
## Loading required package: lattice
## Loading required package: ggplot2
##
## Loaded mixOmics 6.23.2
## Thank you for using mixOmics!
## Tutorials: http://mixomics.org
## Bookdown vignette: https://mixomicsteam.github.io/Bookdown
## Questions, issues: Follow the prompts at http://mixomics.org/contact-us
## Cite us: citation('mixOmics')
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#BiocManager::install("tidyverse")
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ tibble 3.1.8 ✔ purrr 0.3.5
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ purrr::map() masks mixOmics::map()
## ✖ dplyr::select() masks MASS::select()
#BiocManager::install("edgeR")
library(edgeR)
## Loading required package: limma
#BiocManager::install("limma")
library(limma)
#load datasets
#metadata
z <- read_csv("meta_data.csv")
## Rows: 25 Columns: 2
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Samples, Treatment
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
metadata <- data.matrix(z)
head(z)
## # A tibble: 6 × 2
## Samples Treatment
## <chr> <chr>
## 1 EX_590 Exercise
## 2 EX_591 Exercise
## 3 EX_615 Exercise
## 4 EX_605 Exercise
## 5 EX_715 Exercise
## 6 EX_716 Exercise
#miRNA data frame
miRNA <- read_csv("liver_EV_miRNA_normalized_filtered_counts_EX_SED_PBS_transposed_without_col1_title.csv")
## New names:
## Rows: 25 Columns: 78
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (77): mmu-let-7a-1-3p, mmu-let-7b-5p, mmu-let-7c-5p,
## mmu-let-7d-3p, mmu-...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
miRNA
## # A tibble: 25 × 78
## ...1 mmu-l…¹ mmu-l…² mmu-l…³ mmu-l…⁴ mmu-l…⁵ mmu-l…⁶ mmu-m…⁷ mmu-m…⁸ mmu-m…⁹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EX_5… 15.0 17.3 15.0 6.63 7.90 7.82 9.04 7.30 6.34
## 2 EX_5… 14.8 16.8 14.8 6.64 7.35 6.27 8.91 6.27 6.94
## 3 EX_6… 15.0 17.2 15.0 7.33 7.97 7.00 9.06 7.27 5.68
## 4 EX_6… 14.7 16.6 14.7 5.67 7.45 6.08 9.29 6.75 7.20
## 5 EX_7… 14.8 16.7 14.8 5.90 6.75 7.58 9.00 6.38 7.03
## 6 EX_7… 14.8 16.8 14.8 6.36 7.60 6.90 8.98 6.98 5.22
## 7 EX_7… 15.5 17.5 15.5 5.29 6.40 6.61 8.43 6.61 5.03
## 8 EX_7… 15.2 17.3 15.2 5.69 7.32 6.96 8.28 6.18 6.07
## 9 EX_9… 14.6 16.7 14.6 6.70 7.25 6.76 9.14 6.44 6.70
## 10 SED_… 14.9 16.8 14.9 6.57 7.47 7.66 8.89 6.69 6.86
## # … with 15 more rows, 68 more variables: `mmu-miR-107-5p` <dbl>,
## # `mmu-miR-10a-3p` <dbl>, `mmu-miR-10a-5p` <dbl>, `mmu-miR-1198-5p` <dbl>,
## # `mmu-miR-122-5p` <dbl>, `mmu-miR-126a-3p` <dbl>, `mmu-miR-127-3p` <dbl>,
## # `mmu-miR-130a-5p` <dbl>, `mmu-miR-136-5p` <dbl>, `mmu-miR-140-5p` <dbl>,
## # `mmu-miR-141-3p` <dbl>, `mmu-miR-141-5p` <dbl>, `mmu-miR-142a-3p` <dbl>,
## # `mmu-miR-143-3p` <dbl>, `mmu-miR-146a-3p` <dbl>, `mmu-miR-146b-5p` <dbl>,
## # `mmu-miR-152-3p` <dbl>, `mmu-miR-15a-3p` <dbl>, `mmu-miR-16-1-3p` <dbl>, …
dim(miRNA)
## [1] 25 78
miRNA.matrix <- data.matrix(miRNA)
#data with Trt group
#miRNA
miRNA.trt <- read_csv("liver_EV_miRNA_normalized_filtered_counts_EX_SED_PBS_transposed_with_trt.csv")
## Rows: 25 Columns: 79
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Samples, Treatment
## dbl (77): mmu-let-7a-1-3p, mmu-let-7b-5p, mmu-let-7c-5p, mmu-let-7d-3p, mmu-...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
miRNA.trt
## # A tibble: 25 × 79
## Samples Treatment mmu-let-7…¹ mmu-l…² mmu-l…³ mmu-l…⁴ mmu-l…⁵ mmu-l…⁶ mmu-m…⁷
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EX_590 Exercise 15.0 17.3 15.0 6.63 7.90 7.82 9.04
## 2 EX_591 Exercise 14.8 16.8 14.8 6.64 7.35 6.27 8.91
## 3 EX_615 Exercise 15.0 17.2 15.0 7.33 7.97 7.00 9.06
## 4 EX_605 Exercise 14.7 16.6 14.7 5.67 7.45 6.08 9.29
## 5 EX_715 Exercise 14.8 16.7 14.8 5.90 6.75 7.58 9.00
## 6 EX_716 Exercise 14.8 16.8 14.8 6.36 7.60 6.90 8.98
## 7 EX_732 Exercise 15.5 17.5 15.5 5.29 6.40 6.61 8.43
## 8 EX_737 Exercise 15.2 17.3 15.2 5.69 7.32 6.96 8.28
## 9 EX_934 Exercise 14.6 16.7 14.6 6.70 7.25 6.76 9.14
## 10 SED_626 Sedentary 14.9 16.8 14.9 6.57 7.47 7.66 8.89
## # … with 15 more rows, 70 more variables: `mmu-miR-103-3p` <dbl>,
## # `mmu-miR-107-3p` <dbl>, `mmu-miR-107-5p` <dbl>, `mmu-miR-10a-3p` <dbl>,
## # `mmu-miR-10a-5p` <dbl>, `mmu-miR-1198-5p` <dbl>, `mmu-miR-122-5p` <dbl>,
## # `mmu-miR-126a-3p` <dbl>, `mmu-miR-127-3p` <dbl>, `mmu-miR-130a-5p` <dbl>,
## # `mmu-miR-136-5p` <dbl>, `mmu-miR-140-5p` <dbl>, `mmu-miR-141-3p` <dbl>,
## # `mmu-miR-141-5p` <dbl>, `mmu-miR-142a-3p` <dbl>, `mmu-miR-143-3p` <dbl>,
## # `mmu-miR-146a-3p` <dbl>, `mmu-miR-146b-5p` <dbl>, `mmu-miR-152-3p` <dbl>, …
dim(miRNA.trt)
## [1] 25 79
miRNA.matrix.trt <- data.matrix(miRNA.trt)
#RNAseq data frame
RNAseq <- read_csv("liver_EV_RNAseq_normalized_filtered_counts_EX_SED_PBS_transposed_without_col1_title.csv")
## New names:
## Rows: 25 Columns: 427
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (1): ...1 dbl (426): 1110065P20Rik, 1190005I06Rik, 1700123O20Rik,
## 1810024B03Rik, 22100...
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...1`
RNAseq
## # A tibble: 25 × 427
## ...1 11100…¹ 11900…² 17001…³ 18100…⁴ 22100…⁵ 23100…⁶ 23100…⁷ 46324…⁸ 49334…⁹
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EX_5… 3.78 3.01 5.21 1.84 5.49 0.779 5.30 3.77 2.22
## 2 EX_5… 3.52 3.47 4.93 2.45 5.48 1.61 5.63 3.31 2.19
## 3 EX_6… 3.60 3.30 5.04 2.37 5.48 0.967 5.64 3.36 1.87
## 4 EX_6… 3.59 3.11 4.88 2.56 5.56 1.33 5.46 3.36 2.17
## 5 EX_7… 3.39 2.92 4.78 1.94 5.72 1.63 5.26 3.08 2.16
## 6 EX_7… 3.47 2.83 4.79 1.92 5.51 0.779 5.09 3.34 2.10
## 7 EX_7… 3.67 2.93 4.93 2.34 5.61 0.467 5.45 3.67 1.83
## 8 EX_7… 3.64 3.28 5.18 2.35 5.58 0.0263 5.44 3.23 2.16
## 9 EX_9… 3.34 2.98 4.92 2.38 5.60 1.20 5.21 3.37 2.05
## 10 SED_… 3.16 2.49 4.79 1.80 5.47 3.01 5.13 3.47 2.43
## # … with 15 more rows, 417 more variables: `9230114K14Rik` <dbl>,
## # `9330159M07Rik` <dbl>, A930018P22Rik <dbl>, Aars2 <dbl>, Abhd11 <dbl>,
## # Acad12 <dbl>, Adam10 <dbl>, Adar <dbl>, Alg14 <dbl>, Alkbh8 <dbl>,
## # Alms1 <dbl>, Ankfy1 <dbl>, Ankrd54 <dbl>, Ap2s1 <dbl>, Ap5s1 <dbl>,
## # Apcs <dbl>, Arpc1a <dbl>, Asns <dbl>, Atp5g1 <dbl>, Atpaf2 <dbl>,
## # B3gat3 <dbl>, B4galnt1 <dbl>, Bbx <dbl>, BC147527 <dbl>, Blvrb <dbl>,
## # Bmp2 <dbl>, Borcs5 <dbl>, Brme1 <dbl>, Btd <dbl>, Bud23 <dbl>, …
dim(RNAseq)
## [1] 25 427
RNAseq.matrix <- data.matrix(RNAseq)
#data with Trt group
#RNAseq
RNAseq.trt <- read_csv("liver_EV_RNAseq_normalised_filtered_counts_EX_SED_PBS_transposed_with_trt.csv")
## Rows: 25 Columns: 428
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Samples, Treatment
## dbl (426): 1110065P20Rik, 1190005I06Rik, 1700123O20Rik, 1810024B03Rik, 22100...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
RNAseq.trt
## # A tibble: 25 × 428
## Samples Treatment 1110065P2…¹ 11900…² 17001…³ 18100…⁴ 22100…⁵ 23100…⁶ 23100…⁷
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EX_590 Exercise 3.78 3.01 5.21 1.84 5.49 0.779 5.30
## 2 EX_591 Exercise 3.52 3.47 4.93 2.45 5.48 1.61 5.63
## 3 EX_615 Exercise 3.60 3.30 5.04 2.37 5.48 0.967 5.64
## 4 EX_605 Exercise 3.59 3.11 4.88 2.56 5.56 1.33 5.46
## 5 EX_715 Exercise 3.39 2.92 4.78 1.94 5.72 1.63 5.26
## 6 EX_716 Exercise 3.47 2.83 4.79 1.92 5.51 0.779 5.09
## 7 EX_732 Exercise 3.67 2.93 4.93 2.34 5.61 0.467 5.45
## 8 EX_737 Exercise 3.64 3.28 5.18 2.35 5.58 0.0263 5.44
## 9 EX_934 Exercise 3.34 2.98 4.92 2.38 5.60 1.20 5.21
## 10 SED_626 Sedentary 3.16 2.49 4.79 1.80 5.47 3.01 5.13
## # … with 15 more rows, 419 more variables: `4632415L05Rik` <dbl>,
## # `4933427D14Rik` <dbl>, `9230114K14Rik` <dbl>, `9330159M07Rik` <dbl>,
## # A930018P22Rik <dbl>, Aars2 <dbl>, Abhd11 <dbl>, Acad12 <dbl>, Adam10 <dbl>,
## # Adar <dbl>, Alg14 <dbl>, Alkbh8 <dbl>, Alms1 <dbl>, Ankfy1 <dbl>,
## # Ankrd54 <dbl>, Ap2s1 <dbl>, Ap5s1 <dbl>, Apcs <dbl>, Arpc1a <dbl>,
## # Asns <dbl>, Atp5g1 <dbl>, Atpaf2 <dbl>, B3gat3 <dbl>, B4galnt1 <dbl>,
## # Bbx <dbl>, BC147527 <dbl>, Blvrb <dbl>, Bmp2 <dbl>, Borcs5 <dbl>, …
dim(RNAseq.trt)
## [1] 25 428
RNAseq.matrix.trt <- data.matrix(RNAseq.trt)
#Sub-setting
#miRNA.RNAseq <- data.frame(miRNA = c(25, 78), RNAseq = c(25, 427))
#miRNA.RNAseq$miRNA
#miRNA.RNAseq$RNAseq
#miRNA.RNAseq.matrix <- data.matrix(miRNA.RNAseq)
#PCA_miRNA.trt
pca.miRNA.trt <- pca(miRNA.trt) # 1 Run the method
plotIndiv(pca.miRNA.trt, group = miRNA.trt$Treatment, legend = TRUE,
title = "EV miRNA PCA plot") # 2 Plot the samples
pca.miRNA.trt
## Eigenvalues for the first 2 principal components, see object$sdev^2:
## PC1 PC2
## 58.288706 9.950163
##
## Proportion of explained variance for the first 2 principal components, see object$prop_expl_var:
## PC1 PC2
## 0.6322435 0.1079270
##
## Cumulative proportion of explained variance for the first 2 principal components, see object$cum.var:
## PC1 PC2
## 0.6322435 0.7401705
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## plotIndiv, plot, plotVar, selectVar, biplot
##Correlation_circle_Plot_miRNA_no_Trt_column
pca.miRNA.corr <- pca(miRNA) # 1 Run the method
plotVar(pca.miRNA.corr, cutoff = 0.8) # 3 Plot the variables
##plotIndiv(MyResult.pca.miRNA, ind.names = FALSE) #add or remove variable name
#PCA_RNAseq.trt
pca.RNAseq.trt <- pca(RNAseq.trt) # 1 Run the method
plotIndiv(pca.RNAseq.trt, group = RNAseq.trt$Treatment, legend = TRUE,
title = "EV RNA-seq PCA plot") # 2 Plot the samples
pca.RNAseq.trt
## Eigenvalues for the first 2 principal components, see object$sdev^2:
## PC1 PC2
## 56.98940 24.39713
##
## Proportion of explained variance for the first 2 principal components, see object$prop_expl_var:
## PC1 PC2
## 0.5549827 0.2375878
##
## Cumulative proportion of explained variance for the first 2 principal components, see object$cum.var:
## PC1 PC2
## 0.5549827 0.7925705
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## plotIndiv, plot, plotVar, selectVar, biplot
##Correlation_circle_Plot_RNAseq_no_Trt_column
pca.RNAseq.corr <- pca(RNAseq.trt) # 1 Run the method
plotVar(pca.RNAseq.corr, cutoff = 0.8, cex = 3) # cex adjusts font size
#call metadata
z
## # A tibble: 25 × 2
## Samples Treatment
## <chr> <chr>
## 1 EX_590 Exercise
## 2 EX_591 Exercise
## 3 EX_615 Exercise
## 4 EX_605 Exercise
## 5 EX_715 Exercise
## 6 EX_716 Exercise
## 7 EX_732 Exercise
## 8 EX_737 Exercise
## 9 EX_934 Exercise
## 10 SED_626 Sedentary
## # … with 15 more rows
#To display the results on other components, we can change the comp argument
#provided we have requested enough components to be calculated.
#miRNA
pca.miRNA.2 <- pca(miRNA, ncomp = 3)
plotIndiv(pca.miRNA.2, comp = c(1,3),
title = 'miRNA, PCA comp 1-3')
#The amount of variance explained can be extracted with a screeplot
#miRNA
#scree plot
plot(pca.miRNA.2, legen = "miRNA Scree Plot components")
# the actual numerical proportions of explained variance.
pca.miRNA.2
## Eigenvalues for the first 3 principal components, see object$sdev^2:
## PC1 PC2 PC3
## 57.675551 9.924455 8.891406
##
## Proportion of explained variance for the first 3 principal components, see object$prop_expl_var:
## PC1 PC2 PC3
## 0.63042501 0.10847967 0.09718789
##
## Cumulative proportion of explained variance for the first 3 principal components, see object$cum.var:
## PC1 PC2 PC3
## 0.6304250 0.7389047 0.8360926
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## plotIndiv, plot, plotVar, selectVar, biplot
#RNAseq
pca.RNAseq.2 <- pca(RNAseq, ncomp = 3)
plotIndiv(pca.RNAseq.2, comp = c(1,3),
title = 'RNAseq, PCA comp 1 - 3')
#RNAseq
#scree plot
plot(pca.RNAseq.2, legen = "RNAseq Scree Plot components")
# the actual numerical proportions of explained variance.
pca.RNAseq.2
## Eigenvalues for the first 3 principal components, see object$sdev^2:
## PC1 PC2 PC3
## 56.357201 24.396936 2.786207
##
## Proportion of explained variance for the first 3 principal components, see object$prop_expl_var:
## PC1 PC2 PC3
## 0.55262921 0.23923224 0.02732107
##
## Cumulative proportion of explained variance for the first 3 principal components, see object$cum.var:
## PC1 PC2 PC3
## 0.5526292 0.7918614 0.8191825
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## plotIndiv, plot, plotVar, selectVar, biplot
## To look at the variable coefficients in each component with the loading vectors.
#miRNA
#plotLoadings(pca.miRNA, ndisplay = 15,
# size.name = 0.7, coly = c(2,40))
## To look at the variable coefficients in each component with the loading vectors.
#RNAseq
#plotLoadings(RNAseq$pca.RNAseq, ndisplay = 15,
# size.name = 0.7)
#Plots can also be interactively displayed in 3 dimensions using the option style="3d".
#We use the rgl package for this (the interative figure is only interatice in the html vignette).
#BiocManager::install("rgl")
library(rgl)
#3D_pca_miRAN
plotIndiv(pca.miRNA.2,
group = miRNA.trt$Treatment, style="3d",
legend = TRUE, title = 'miRNA, PCA comp 1 - 2 - 3')
#3D_pca_RANseq
plotIndiv(pca.RNAseq.2,
group = RNAseq.trt$Treatment, style="3d",
legend = TRUE, title = 'RNAseq, PCA comp 1 - 2 - 3')
## Variable selection with sparse PCA (sPCA)
# Biological question: Apply PCA but also identify the key variables that
# contribute to the explanation of most variance in the data set.
# Variable selection can be performed using spca (Shen and Huang 2008).
# The user needs to provide the number of variables to select on each PC.
# miRNA
dim(miRNA)
## [1] 25 78
miRNA.spca.2comp <- spca(miRNA.matrix.trt, ncomp = 2, keepX = c(45, 10)) # 1 Run the method
plotIndiv(miRNA.spca.2comp, group = miRNA.trt$Treatment, # 2 Plot the samples
pch = as.factor(miRNA.trt$Treatment),
legend = TRUE, title = 'miRNA, sPCA comp 1 - 2',
legend.title = 'Treatment')
miRNA.spca.2comp
## sparse PCA with 2 principal components.
## Input data X of dimensions: 25 79
## Number of selected variables on each prinicipal components:
## PC1 PC2
## 45 10
## Proportion of adjusted explained variance for the first 2 principal components, see object$prop_expl_var:
## PC1 PC2
## 0.2086193 0.1509311
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## tune.spca, plotIndiv, plot, plotVar, selectVar, biplot
#Plot the variables - Correlation circle
#miRNA
plotVar(miRNA.spca.2comp)
#RNAseq
dim(RNAseq)
## [1] 25 427
RNAseq.spca.2comp <- spca(RNAseq.matrix.trt, ncomp = 2, keepX = c(234,107)) # 1 Run the method
plotIndiv(RNAseq.spca.2comp, group = RNAseq.trt$Treatment, # 2 Plot the samples
pch = as.factor(RNAseq.trt$Treatment),
legend = TRUE, title = 'RNAseq, sPCA comp 1 - 2',
legend.title = 'Treatment')
RNAseq.spca.2comp
## sparse PCA with 2 principal components.
## Input data X of dimensions: 25 428
## Number of selected variables on each prinicipal components:
## PC1 PC2
## 234 107
## Proportion of adjusted explained variance for the first 2 principal components, see object$prop_expl_var:
## PC1 PC2
## 0.51750143 0.08410191
##
## Other available components:
## --------------------
## loading vectors: see object$rotation
## Other functions:
## --------------------
## tune.spca, plotIndiv, plot, plotVar, selectVar, biplot
#Plot the variables - Correlation circle
#RNAseq
plotVar(RNAseq.spca.2comp, cex = 3) # cex is used to change the size of the labels on the plot
#Selected variables can be identified on each component with the selectVar function.
#Here the coefficient values are extracted, but there are other outputs as well, see ?selectVar:
#miRNA
selectVar(miRNA.spca.2comp, comp = 1)$value
## value.var
## mmu-miR-200c-5p 0.267338725
## mmu-miR-141-5p 0.264046607
## mmu-miR-199a-3p 0.262530652
## mmu-miR-199b-3p 0.252078053
## mmu-miR-200b-3p 0.247033222
## mmu-miR-194-5p -0.240355635
## mmu-miR-141-3p 0.240350917
## mmu-miR-200a-3p 0.236416431
## mmu-miR-195a-5p 0.231801102
## mmu-miR-3065-5p -0.228759616
## mmu-miR-181d-3p 0.221659430
## mmu-miR-181b-5p 0.213350705
## mmu-miR-223-5p 0.210387282
## mmu-miR-181c-5p 0.193346767
## mmu-miR-31-3p 0.193110355
## mmu-miR-203b-5p -0.157859440
## mmu-miR-30e-3p -0.149520691
## mmu-miR-142a-3p 0.149125104
## mmu-miR-335-5p 0.132178239
## mmu-miR-146b-5p 0.108775312
## mmu-miR-146a-3p 0.107770645
## mmu-miR-122-5p -0.105880710
## mmu-miR-191-5p 0.098211351
## mmu-miR-501-5p 0.082196920
## mmu-miR-107-3p -0.077595920
## mmu-miR-93-3p 0.068650814
## mmu-miR-16-1-3p -0.066640729
## mmu-miR-30a-5p -0.064231481
## mmu-miR-186-5p -0.061940994
## mmu-miR-674-5p 0.061015891
## mmu-miR-708-5p 0.045454937
## mmu-miR-10a-3p 0.040855280
## mmu-miR-324-5p 0.033542067
## mmu-miR-26a-5p -0.030086328
## mmu-miR-152-3p -0.028380707
## mmu-miR-10a-5p 0.026411147
## mmu-let-7c-5p -0.024588643
## Treatment 0.024420746
## mmu-miR-345-3p 0.018062235
## mmu-let-7a-1-3p -0.017421924
## mmu-miR-20a-5p 0.013083201
## mmu-miR-185-3p 0.006565135
## mmu-miR-98-5p -0.003036589
## Samples 0.002075186
## mmu-miR-130a-5p -0.001623831
selectVar(miRNA.spca.2comp, comp = 2)$value
## value.var
## mmu-let-7a-1-3p 0.47371648
## mmu-let-7c-5p 0.47294337
## mmu-miR-21a-3p -0.46701530
## mmu-miR-5099 -0.31849704
## mmu-let-7b-5p 0.30409741
## mmu-miR-30d-3p 0.27525299
## mmu-miR-16-1-3p 0.16209164
## mmu-miR-101b-3p -0.15903168
## mmu-miR-26a-5p -0.08708547
## mmu-miR-677-5p -0.07060817
#selectVar(miRNA.spca.3comp, comp = 1)$value # Coefficient values of variables in comp 1
#selectVar(miRNA.spca.3comp, comp = 2)$value # Coefficient values of variables in comp 2
#selectVar(miRNA.spca.3comp, comp = 3)$value # Coefficient values of variables in comp 3
# RNAseq
selectVar(RNAseq.spca.2comp, comp = 1)$values
## NULL
selectVar(RNAseq.spca.2comp, comp = 2)$value
## value.var
## Tnks -0.2329228274
## Pcgf3 -0.1954947633
## Nbeal1 -0.1954818174
## Alkbh8 -0.1832442139
## Kctd20 -0.1824390554
## Selenow 0.1803077436
## Rsbn1 -0.1736621520
## Cbx5 -0.1660445441
## Ap2s1 0.1617612851
## D5Ertd579e -0.1592336246
## Nup153 -0.1570236034
## Trim56 -0.1543989124
## Gba 0.1506085377
## Dpy19l4 -0.1464539568
## Prpf4b -0.1450915091
## Tstd2 -0.1410508599
## Mrps15 0.1382885517
## Elob 0.1352278084
## Ankrd54 0.1344095002
## Proser1 -0.1339533316
## Lnpep -0.1329709933
## Prpf39 -0.1323396129
## Zfp862-ps -0.1308050332
## Hdac4 -0.1306807993
## Rbm42 0.1300684079
## Slc2a4rg-ps -0.1253078584
## Exosc6 0.1251868627
## Ctsz 0.1237938498
## Mef2a -0.1215504254
## Sprtn -0.1210187860
## Itsn2 -0.1197256306
## Tpgs1 0.1186051460
## 4632415L05Rik -0.1173497624
## Pfas -0.1165336611
## Cdk2ap2 0.1152510629
## Rpl29 0.1125759985
## Zfp664 -0.1052593435
## Synj1 -0.1004985562
## Coa3 0.0952918296
## Ube2m 0.0949694424
## Napa 0.0933781751
## Tasor2 -0.0933059775
## Clcn5 -0.0923510083
## Ddx5 -0.0921339454
## Sbf2 -0.0895947666
## 2210016F16Rik 0.0873301416
## Taf10 0.0840220507
## Nrgn 0.0838983984
## Cib1 0.0828844086
## Trir 0.0828607765
## Snrpa 0.0816026457
## Tnfrsf14 -0.0801961470
## Smim11 0.0800585907
## Fam185a -0.0737729044
## Dctn3 0.0704085507
## Cfdp1 0.0692526432
## Gemin5 -0.0654196669
## Sigirr 0.0643780284
## Nudt22 0.0633960916
## Mydgf 0.0606409702
## Snx3 0.0551419337
## Hars 0.0513505637
## Tmem238 0.0511158013
## Wdr55 0.0495776905
## Siah2 0.0470650968
## Glb1 0.0462132251
## Mif 0.0451338044
## Rpl21 0.0443243421
## H13 0.0442097145
## Tarbp1 -0.0441722811
## Pms2 -0.0416818514
## Lbp 0.0368448387
## Tor1aip1 -0.0364047111
## Tmem37 0.0345730875
## Crb3 0.0341157142
## Usp22 -0.0340134031
## Bbx -0.0315979900
## Fam20a 0.0310461617
## Gm10051 0.0309298199
## E130201H02Rik 0.0308185559
## Zc3h7b -0.0301725727
## Cramp1 -0.0294149091
## Gm4755 0.0277274110
## Gbp7 -0.0261349479
## Dnajb13 0.0229154595
## Trappc5 0.0196808255
## D8Ertd738e 0.0189420420
## Trim25 -0.0185640994
## Commd9 0.0176448095
## Gm15772 0.0164978816
## Eloc 0.0157792432
## Znrd2 0.0146486734
## Gm12191 0.0139373847
## Fnbp4 -0.0132803210
## Tm6sf2 0.0095737727
## Rufy2 -0.0095659561
## Sebox 0.0079366594
## Nipal3 -0.0077824649
## Serf1 0.0072402921
## C230062I16Rik 0.0064497519
## Cda 0.0062605091
## Zc3hav1 -0.0031012548
## Gadd45gip1 0.0029978817
## Pold3 0.0028325635
## Znhit1 0.0017249452
## Slc25a23 -0.0008007010
## Fcf1 0.0005663117
#selectVar(RNAseq.spca.2comp, comp = 1)$value
#selectVar(RNAseq.spca.2comp, comp = 2)$value
#selectVar(RNAseq.spca.3comp, comp = 3)$value
# We can complement this output with plotLoadings.
# We can see here that all coefficients are negative.
# miRNA
plotLoadings(miRNA.spca.2comp, comp=1)
plotLoadings(miRNA.spca.2comp, comp=2)
#plotLoadings(miRNA.spca.3comp, comp=3)
# RNAseq
plotLoadings(RNAseq.spca.2comp, comp=1)
plotLoadings(RNAseq.spca.2comp, comp=2)
#plotLoadings(RNAseq.spca.3comp, comp=3)
#Tuning Parameters
tune.pca(miRNA.trt)
tune.pca(RNAseq.trt)
### PLS(-DA): integrate two datasets measured on the same sample by extracting correlated info
# Sub-setting
miRNA.RNAseq <- data.frame(miRNA = c(45, 10), RNAseq = c(234,107))
miRNA.RNAseq
## miRNA RNAseq
## 1 45 234
## 2 10 107
#sub-setting with trt
miRNA.RNAseq.trt <- data.frame(miRNA.trt = c(45, 10), RNAseq.trt = c(234,107))
miRNA.RNAseq.trt
## miRNA.trt RNAseq.trt
## 1 45 234
## 2 10 107
X <- miRNA.RNAseq.trt$miRNA.trt
X
## [1] 45 10
Y <- miRNA.RNAseq.trt$RNAseq.trt
Y
## [1] 234 107
miRNA.RNAseq.matrix <- data.matrix(miRNA.RNAseq)
miRNA.RNAseq.matrix.trt <- data.matrix(miRNA.RNAseq.trt)
#cex
#BiocManager::install("cex")
#Spls
miRNA.RNAseq.spls <- spls(miRNA.matrix,RNAseq.matrix, keepX = c(45, 10), keepY = c(234,107))
miRNA.RNAseq.spls.trt <- spls(miRNA.matrix.trt,RNAseq.matrix.trt, keepX = c(45, 10), keepY = c(234,107))
## sample plot
plotIndiv(miRNA.RNAseq.spls) # X.label = "miRNA", Y.label = "RNAseq"
plotIndiv(miRNA.RNAseq.spls.trt)
## variable plot
plotVar(miRNA.RNAseq.spls) # cex = (miRAN.RNAseq = 3)?
#Customize sample plots
#plotIndiv(miRNA.RNAseq.spls, group = miRNA.RNAseq$miRNA,
# rep.space = "XY-variate", legend = TRUE,
# legend.title = 'EV',
# title = 'miRNA X RNAseq: sPLS')
#PLS-DA without selection - miRNA:
miRNA.plsda.noselect <- plsda(miRNA.matrix.trt, miRNA.trt$Treatment)
plotIndiv(miRNA.plsda.noselect, legend = TRUE,
ellipse = TRUE, star = TRUE, title = "miRNA PLS-DA")
plotVar(miRNA.plsda.noselect, cutoff = 0.7, cex = 3)
#sPLS-DA without selection - miRNA:
miRNA.splsda.noselect <- splsda(miRNA.matrix.trt, miRNA.trt$Treatment)
plotIndiv(miRNA.splsda.noselect, legend = TRUE,
ellipse = TRUE, star = TRUE, title = "miRNA sPLS-DA")
plotVar(miRNA.splsda.noselect, cutoff = 0.7, cex = 3)
##PLS-DA without selection - RNAseq:
#sPLS-DA without selection - RNAseq:
##SPLSDA miRNA
miRNA.splsda <- splsda(miRNA.matrix.trt, miRNA.trt$Treatment, keepX = c(45,10)) # 1 Run the method
plotIndiv(miRNA.splsda, legend = TRUE, title = 'miRNA, sPLSDA comp 1 - 2',
legend.title = 'Treatment', ellipse = TRUE, star = TRUE) # 2 Plot the samples (coloured by classes automatically)
plotVar(miRNA.splsda, cex = 3)
# Selected variables on components
selectVar(miRNA.splsda, comp = 1)
## $name
## [1] "Treatment" "Samples" "mmu-miR-122-5p" "mmu-miR-10a-3p"
## [5] "mmu-miR-20a-5p" "mmu-miR-101b-3p" "mmu-miR-324-3p" "mmu-miR-677-5p"
## [9] "mmu-miR-1981-3p" "mmu-miR-195a-5p" "mmu-miR-103-3p" "mmu-miR-143-3p"
## [13] "mmu-miR-5099" "mmu-miR-671-5p" "mmu-miR-30d-3p" "mmu-miR-7a-1-3p"
## [17] "mmu-miR-503-5p" "mmu-miR-1198-5p" "mmu-miR-127-3p" "mmu-let-7c-5p"
## [21] "mmu-let-7a-1-3p" "mmu-miR-27b-3p" "mmu-miR-324-5p" "mmu-miR-186-5p"
## [25] "mmu-miR-21a-3p" "mmu-miR-28a-3p" "mmu-miR-200c-5p" "mmu-miR-152-3p"
## [29] "mmu-miR-26a-5p" "mmu-miR-199b-3p" "mmu-miR-200a-3p" "mmu-miR-141-5p"
## [33] "mmu-let-7f-1-3p" "mmu-miR-185-3p" "mmu-miR-181c-5p" "mmu-miR-31-3p"
## [37] "mmu-miR-98-5p" "mmu-miR-93-3p" "mmu-miR-191-5p" "mmu-let-7b-5p"
## [41] "mmu-miR-141-3p" "mmu-miR-5112" "mmu-miR-331-5p" "mmu-miR-15a-3p"
## [45] "mmu-miR-199a-3p"
##
## $value
## value.var
## Treatment -0.429095323
## Samples -0.404597919
## mmu-miR-122-5p 0.236527565
## mmu-miR-10a-3p -0.216743623
## mmu-miR-20a-5p 0.214106465
## mmu-miR-101b-3p 0.210796600
## mmu-miR-324-3p 0.192964591
## mmu-miR-677-5p 0.185508315
## mmu-miR-1981-3p 0.173732565
## mmu-miR-195a-5p -0.172657511
## mmu-miR-103-3p 0.162394246
## mmu-miR-143-3p 0.157704508
## mmu-miR-5099 0.154179185
## mmu-miR-671-5p 0.142408356
## mmu-miR-30d-3p -0.140646383
## mmu-miR-7a-1-3p 0.129946849
## mmu-miR-503-5p 0.128966045
## mmu-miR-1198-5p 0.118436935
## mmu-miR-127-3p 0.117802159
## mmu-let-7c-5p -0.116395833
## mmu-let-7a-1-3p -0.115376894
## mmu-miR-27b-3p 0.112325617
## mmu-miR-324-5p -0.109223593
## mmu-miR-186-5p 0.104191774
## mmu-miR-21a-3p 0.100805058
## mmu-miR-28a-3p 0.100420472
## mmu-miR-200c-5p -0.098175681
## mmu-miR-152-3p 0.086891789
## mmu-miR-26a-5p 0.085936121
## mmu-miR-199b-3p -0.082688499
## mmu-miR-200a-3p -0.081772479
## mmu-miR-141-5p -0.078915038
## mmu-let-7f-1-3p 0.066621207
## mmu-miR-185-3p 0.065957171
## mmu-miR-181c-5p -0.064099594
## mmu-miR-31-3p -0.061735748
## mmu-miR-98-5p 0.054749366
## mmu-miR-93-3p 0.041048018
## mmu-miR-191-5p -0.038856724
## mmu-let-7b-5p -0.023616680
## mmu-miR-141-3p -0.020256165
## mmu-miR-5112 -0.010480702
## mmu-miR-331-5p -0.004304701
## mmu-miR-15a-3p -0.002098283
## mmu-miR-199a-3p -0.001149732
##
## $comp
## [1] 1
selectVar(miRNA.splsda, comp = 1)$name
## [1] "Treatment" "Samples" "mmu-miR-122-5p" "mmu-miR-10a-3p"
## [5] "mmu-miR-20a-5p" "mmu-miR-101b-3p" "mmu-miR-324-3p" "mmu-miR-677-5p"
## [9] "mmu-miR-1981-3p" "mmu-miR-195a-5p" "mmu-miR-103-3p" "mmu-miR-143-3p"
## [13] "mmu-miR-5099" "mmu-miR-671-5p" "mmu-miR-30d-3p" "mmu-miR-7a-1-3p"
## [17] "mmu-miR-503-5p" "mmu-miR-1198-5p" "mmu-miR-127-3p" "mmu-let-7c-5p"
## [21] "mmu-let-7a-1-3p" "mmu-miR-27b-3p" "mmu-miR-324-5p" "mmu-miR-186-5p"
## [25] "mmu-miR-21a-3p" "mmu-miR-28a-3p" "mmu-miR-200c-5p" "mmu-miR-152-3p"
## [29] "mmu-miR-26a-5p" "mmu-miR-199b-3p" "mmu-miR-200a-3p" "mmu-miR-141-5p"
## [33] "mmu-let-7f-1-3p" "mmu-miR-185-3p" "mmu-miR-181c-5p" "mmu-miR-31-3p"
## [37] "mmu-miR-98-5p" "mmu-miR-93-3p" "mmu-miR-191-5p" "mmu-let-7b-5p"
## [41] "mmu-miR-141-3p" "mmu-miR-5112" "mmu-miR-331-5p" "mmu-miR-15a-3p"
## [45] "mmu-miR-199a-3p"
selectVar(miRNA.splsda, comp = 2)$name
## [1] "Treatment" "Samples" "mmu-miR-677-5p" "mmu-miR-30a-5p"
## [5] "mmu-miR-30d-3p" "mmu-miR-5112" "mmu-miR-30e-3p" "mmu-miR-27a-3p"
## [9] "mmu-miR-26a-5p" "mmu-miR-3065-5p"
##SPLSDA RNAseq
RNAseq.splsda <- splsda(RNAseq.matrix.trt, RNAseq.trt$Treatment, keepX = c(234,107)) # 1 Run the method
plotIndiv(RNAseq.splsda, legend = TRUE, title = 'RNAseq, sPLSDA comp 1 - 2',
legend.title = 'Treatment', ellipse = TRUE, star = TRUE)
plotVar(RNAseq.splsda, cex = 3)
# Selected variables on components
selectVar(RNAseq.splsda, comp = 1)
## $name
## [1] "Gba" "Foxred2" "Tor1aip1" "Trim56"
## [5] "Odf2" "Taf10" "Trappc5" "Fxn"
## [9] "Dxo" "Heatr6" "Sigirr" "Commd9"
## [13] "Cenpj" "Napa" "Ube2m" "Clptm1"
## [17] "Nipal3" "Gm10051" "Hars" "Smim11"
## [21] "Tmem37" "Ccdc77" "Eefsec" "Mrps15"
## [25] "Mif" "Slc12a6" "Mbd3" "Llph"
## [29] "Gm53175" "Sprtn" "Pms2" "Gm4755"
## [33] "D330050I16Rik" "Cep63" "Gpr107" "Cdk2ap2"
## [37] "Cramp1" "Alms1" "Qpctl" "Trappc1"
## [41] "Znhit1" "Med7" "Tmem238" "Serf1"
## [45] "Coa3" "Usp1" "Emc3" "Gemin5"
## [49] "Rsbn1" "Foxa2" "4632415L05Rik" "Ap2s1"
## [53] "Rpl21" "Fam20a" "Lztr1" "Zfp862-ps"
## [57] "Pcgf3" "Dctn3" "Pals2" "Zfp664"
## [61] "Slc2a4rg-ps" "Zup1" "Arpc1a" "H13"
## [65] "Mydgf" "Asns" "Inhbc" "Saa3"
## [69] "2210016F16Rik" "Foxq1" "Rpn1" "Alg14"
## [73] "Pfas" "Ifih1" "Gadd45gip1" "Zc3hav1"
## [77] "Gbp7" "Tstd2" "Ddx5" "Adar"
## [81] "Rfng" "Mogs" "Rbm42" "Tnks"
## [85] "Gorasp2" "Gm3219" "Snrpa" "Ppp2r5c"
## [89] "Sap30l" "Itsn2" "Mea1" "Slc20a1"
## [93] "Ift88" "Lypla2" "Sec11c" "Vegfb"
## [97] "Glb1" "Hapln4" "C230062I16Rik" "Tfg"
## [101] "Synj1" "Emc10" "Fam162a" "Elob"
## [105] "Tarbp1" "Map1lc3a" "Nup153" "Ggh"
## [109] "Synj2" "Myl6b" "Kdelr1" "Clcn5"
## [113] "Ddx60" "Hras" "Rpl29" "Nr2f6"
## [117] "Dnajc12" "Fgfr3" "Memo1" "Keap1"
## [121] "Parp12" "Brme1" "Ndufv3" "Znrd2"
## [125] "Cbx5" "Fcf1" "Snora65" "Cntf"
## [129] "Eif3i" "Nsun4" "Ubqln4" "Trir"
## [133] "Saysd1" "Cib1" "Slc4a11" "Myg1"
## [137] "Fnbp4" "Tsen34" "Hgf" "Mrps11"
## [141] "Frat1" "9230114K14Rik" "Spg20" "Pcdhb16"
## [145] "Ankrd54" "9330159M07Rik" "Rnasek" "Tpgs1"
## [149] "Orai1" "Exosc6" "Snx3" "Lrrc9"
## [153] "Trim25" "Gltpd2" "Wrnip1" "Dhrs3"
## [157] "Dhps" "Tmem160" "Rasa3" "Dgkd"
## [161] "Tasor2" "Macroh2a1" "Sbf2" "Timm17b"
## [165] "Lbp" "Wdr55" "Tm6sf2" "Slc17a1"
## [169] "Hadhb" "Nfyc" "Ocel1" "D5Ertd579e"
## [173] "Dpy19l4" "2310034O05Rik" "D8Ertd738e" "Proser1"
## [177] "Cltb" "Rnaseh2c" "Tchh" "1110065P20Rik"
## [181] "Slc22a26" "Ripor3" "Eloc" "Abhd11"
## [185] "Usp22" "Cops8" "Blvrb" "Nudt22"
## [189] "Lrrfip1" "Gbp3" "Patj" "Thnsl1"
## [193] "Polr1a" "Wdr12" "H2bc4" "Gal3st4"
## [197] "Rgs18" "Tnfrsf14" "Rnf7" "H2-T10"
## [201] "Zfp182" "Vmo1" "Nop16" "Epn1"
## [205] "Nsmaf" "Alkbh8" "1810024B03Rik" "Adam10"
## [209] "Ddb2" "Gaa" "Kctd20" "Clca3a2"
## [213] "Ergic3" "Uchl5" "Cwc15" "Siah2"
## [217] "Slfn9" "Slc25a2" "Tuba4a" "Atp5g1"
## [221] "Rin3" "Rps6ka3" "Guk1" "Cdk10"
## [225] "Nsmce3" "Ldha" "Selenow" "Ralb"
## [229] "Klra3" "Smpd1" "Ppp5c" "Plpp6"
## [233] "Lnpep" "Kif3b"
##
## $value
## value.var
## Gba -0.1862611107
## Foxred2 0.1612198661
## Tor1aip1 0.1563358525
## Trim56 0.1477039971
## Odf2 0.1476512720
## Taf10 -0.1379223503
## Trappc5 -0.1357998896
## Fxn -0.1351971727
## Dxo -0.1350427765
## Heatr6 0.1337182625
## Sigirr -0.1329662533
## Commd9 -0.1327398837
## Cenpj 0.1295053386
## Napa -0.1264161290
## Ube2m -0.1252182580
## Clptm1 -0.1245909548
## Nipal3 0.1191402522
## Gm10051 -0.1164963236
## Hars -0.1146442218
## Smim11 -0.1145083082
## Tmem37 -0.1135324415
## Ccdc77 0.1088234231
## Eefsec -0.1086050621
## Mrps15 -0.1078886066
## Mif -0.1062356380
## Slc12a6 0.1054247415
## Mbd3 -0.1046836992
## Llph -0.1040511217
## Gm53175 0.1039917565
## Sprtn 0.1039402630
## Pms2 0.1031790551
## Gm4755 -0.1028703149
## D330050I16Rik -0.1028674038
## Cep63 -0.1007460004
## Gpr107 0.1006381123
## Cdk2ap2 -0.0991862432
## Cramp1 0.0986403920
## Alms1 0.0958736573
## Qpctl -0.0936896171
## Trappc1 -0.0929475699
## Znhit1 -0.0915410637
## Med7 -0.0906999823
## Tmem238 -0.0906419265
## Serf1 -0.0902188988
## Coa3 -0.0897361574
## Usp1 0.0884459684
## Emc3 -0.0884299959
## Gemin5 0.0877580425
## Rsbn1 0.0867047375
## Foxa2 -0.0864586604
## 4632415L05Rik 0.0834510812
## Ap2s1 -0.0818466758
## Rpl21 -0.0817563404
## Fam20a -0.0799365643
## Lztr1 0.0794123973
## Zfp862-ps 0.0776571479
## Pcgf3 0.0776454869
## Dctn3 -0.0758286470
## Pals2 -0.0754423768
## Zfp664 0.0745328377
## Slc2a4rg-ps 0.0742583629
## Zup1 0.0736145105
## Arpc1a -0.0735162393
## H13 -0.0730522259
## Mydgf -0.0729551168
## Asns -0.0724083737
## Inhbc -0.0722127968
## Saa3 -0.0721008754
## 2210016F16Rik -0.0719038757
## Foxq1 -0.0718447600
## Rpn1 -0.0698301176
## Alg14 -0.0686682571
## Pfas 0.0680990796
## Ifih1 0.0668914068
## Gadd45gip1 -0.0667849863
## Zc3hav1 0.0657665082
## Gbp7 0.0657605545
## Tstd2 0.0653173000
## Ddx5 0.0644131534
## Adar 0.0633450209
## Rfng -0.0619331603
## Mogs -0.0618881790
## Rbm42 -0.0618567943
## Tnks 0.0609911982
## Gorasp2 -0.0608491401
## Gm3219 -0.0606795473
## Snrpa -0.0605481485
## Ppp2r5c -0.0595267894
## Sap30l -0.0577536213
## Itsn2 0.0575330266
## Mea1 -0.0571878774
## Slc20a1 0.0558138170
## Ift88 0.0557507107
## Lypla2 -0.0557385174
## Sec11c -0.0552270871
## Vegfb -0.0551558872
## Glb1 -0.0550146488
## Hapln4 -0.0543251357
## C230062I16Rik -0.0539673840
## Tfg -0.0528254200
## Synj1 0.0521008080
## Emc10 -0.0517375784
## Fam162a -0.0512626443
## Elob -0.0507468391
## Tarbp1 0.0502655250
## Map1lc3a -0.0500445295
## Nup153 0.0492963856
## Ggh -0.0487725881
## Synj2 0.0487379914
## Myl6b -0.0485738311
## Kdelr1 -0.0477508821
## Clcn5 0.0471127893
## Ddx60 0.0470866413
## Hras -0.0470037954
## Rpl29 -0.0465932372
## Nr2f6 -0.0454704773
## Dnajc12 -0.0451611817
## Fgfr3 0.0451532858
## Memo1 -0.0450056100
## Keap1 -0.0445148017
## Parp12 0.0432668655
## Brme1 0.0432309119
## Ndufv3 -0.0428136002
## Znrd2 -0.0425311592
## Cbx5 0.0423768972
## Fcf1 -0.0422067853
## Snora65 0.0415499613
## Cntf -0.0407284109
## Eif3i -0.0387528020
## Nsun4 -0.0384363970
## Ubqln4 -0.0383572061
## Trir -0.0377651493
## Saysd1 -0.0375205083
## Cib1 -0.0374331513
## Slc4a11 0.0366834655
## Myg1 -0.0366061356
## Fnbp4 0.0361232864
## Tsen34 -0.0359580335
## Hgf 0.0342111534
## Mrps11 -0.0329194578
## Frat1 -0.0316641186
## 9230114K14Rik 0.0307008199
## Spg20 -0.0305901316
## Pcdhb16 0.0300197483
## Ankrd54 -0.0298668348
## 9330159M07Rik 0.0297921855
## Rnasek -0.0291430678
## Tpgs1 -0.0289410063
## Orai1 -0.0285380838
## Exosc6 -0.0282872159
## Snx3 -0.0281238856
## Lrrc9 0.0276386065
## Trim25 0.0274596001
## Gltpd2 -0.0274500516
## Wrnip1 -0.0272862649
## Dhrs3 -0.0272258121
## Dhps -0.0269607005
## Tmem160 -0.0260300934
## Rasa3 0.0256191294
## Dgkd 0.0253367626
## Tasor2 0.0249398384
## Macroh2a1 -0.0248995071
## Sbf2 0.0247812907
## Timm17b -0.0247399560
## Lbp -0.0240102311
## Wdr55 -0.0239205515
## Tm6sf2 -0.0236476642
## Slc17a1 -0.0233936203
## Hadhb -0.0232620011
## Nfyc -0.0232289310
## Ocel1 -0.0222444566
## D5Ertd579e 0.0217330010
## Dpy19l4 0.0214021052
## 2310034O05Rik 0.0212903781
## D8Ertd738e -0.0208807204
## Proser1 0.0199407292
## Cltb -0.0193328778
## Rnaseh2c -0.0189006025
## Tchh 0.0174210244
## 1110065P20Rik -0.0173753408
## Slc22a26 -0.0170427901
## Ripor3 0.0168772997
## Eloc -0.0167957667
## Abhd11 -0.0166498095
## Usp22 0.0166112166
## Cops8 -0.0164488305
## Blvrb -0.0149071576
## Nudt22 -0.0145224301
## Lrrfip1 0.0144318376
## Gbp3 0.0142776523
## Patj 0.0136853102
## Thnsl1 0.0135013446
## Polr1a 0.0129985749
## Wdr12 -0.0127830259
## H2bc4 -0.0127528375
## Gal3st4 0.0127071789
## Rgs18 0.0126818967
## Tnfrsf14 0.0122875975
## Rnf7 -0.0120523773
## H2-T10 0.0110382630
## Zfp182 0.0108547531
## Vmo1 -0.0107782498
## Nop16 -0.0100588076
## Epn1 -0.0095123307
## Nsmaf 0.0090161567
## Alkbh8 0.0083170855
## 1810024B03Rik -0.0078418371
## Adam10 0.0075588785
## Ddb2 0.0070027579
## Gaa -0.0069689014
## Kctd20 0.0069278876
## Clca3a2 0.0069220888
## Ergic3 -0.0068550774
## Uchl5 -0.0066669459
## Cwc15 -0.0063512270
## Siah2 -0.0060655698
## Slfn9 0.0056338741
## Slc25a2 -0.0052168635
## Tuba4a -0.0050606643
## Atp5g1 -0.0049790001
## Rin3 -0.0048487064
## Rps6ka3 0.0044395312
## Guk1 -0.0042616555
## Cdk10 -0.0041387830
## Nsmce3 -0.0039807623
## Ldha -0.0039767001
## Selenow -0.0035423925
## Ralb 0.0022496849
## Klra3 0.0010291570
## Smpd1 -0.0010268018
## Ppp5c -0.0010110232
## Plpp6 -0.0004858471
## Lnpep 0.0003617761
## Kif3b 0.0002468642
##
## $comp
## [1] 1
selectVar(RNAseq.splsda, comp = 1)$name
## [1] "Gba" "Foxred2" "Tor1aip1" "Trim56"
## [5] "Odf2" "Taf10" "Trappc5" "Fxn"
## [9] "Dxo" "Heatr6" "Sigirr" "Commd9"
## [13] "Cenpj" "Napa" "Ube2m" "Clptm1"
## [17] "Nipal3" "Gm10051" "Hars" "Smim11"
## [21] "Tmem37" "Ccdc77" "Eefsec" "Mrps15"
## [25] "Mif" "Slc12a6" "Mbd3" "Llph"
## [29] "Gm53175" "Sprtn" "Pms2" "Gm4755"
## [33] "D330050I16Rik" "Cep63" "Gpr107" "Cdk2ap2"
## [37] "Cramp1" "Alms1" "Qpctl" "Trappc1"
## [41] "Znhit1" "Med7" "Tmem238" "Serf1"
## [45] "Coa3" "Usp1" "Emc3" "Gemin5"
## [49] "Rsbn1" "Foxa2" "4632415L05Rik" "Ap2s1"
## [53] "Rpl21" "Fam20a" "Lztr1" "Zfp862-ps"
## [57] "Pcgf3" "Dctn3" "Pals2" "Zfp664"
## [61] "Slc2a4rg-ps" "Zup1" "Arpc1a" "H13"
## [65] "Mydgf" "Asns" "Inhbc" "Saa3"
## [69] "2210016F16Rik" "Foxq1" "Rpn1" "Alg14"
## [73] "Pfas" "Ifih1" "Gadd45gip1" "Zc3hav1"
## [77] "Gbp7" "Tstd2" "Ddx5" "Adar"
## [81] "Rfng" "Mogs" "Rbm42" "Tnks"
## [85] "Gorasp2" "Gm3219" "Snrpa" "Ppp2r5c"
## [89] "Sap30l" "Itsn2" "Mea1" "Slc20a1"
## [93] "Ift88" "Lypla2" "Sec11c" "Vegfb"
## [97] "Glb1" "Hapln4" "C230062I16Rik" "Tfg"
## [101] "Synj1" "Emc10" "Fam162a" "Elob"
## [105] "Tarbp1" "Map1lc3a" "Nup153" "Ggh"
## [109] "Synj2" "Myl6b" "Kdelr1" "Clcn5"
## [113] "Ddx60" "Hras" "Rpl29" "Nr2f6"
## [117] "Dnajc12" "Fgfr3" "Memo1" "Keap1"
## [121] "Parp12" "Brme1" "Ndufv3" "Znrd2"
## [125] "Cbx5" "Fcf1" "Snora65" "Cntf"
## [129] "Eif3i" "Nsun4" "Ubqln4" "Trir"
## [133] "Saysd1" "Cib1" "Slc4a11" "Myg1"
## [137] "Fnbp4" "Tsen34" "Hgf" "Mrps11"
## [141] "Frat1" "9230114K14Rik" "Spg20" "Pcdhb16"
## [145] "Ankrd54" "9330159M07Rik" "Rnasek" "Tpgs1"
## [149] "Orai1" "Exosc6" "Snx3" "Lrrc9"
## [153] "Trim25" "Gltpd2" "Wrnip1" "Dhrs3"
## [157] "Dhps" "Tmem160" "Rasa3" "Dgkd"
## [161] "Tasor2" "Macroh2a1" "Sbf2" "Timm17b"
## [165] "Lbp" "Wdr55" "Tm6sf2" "Slc17a1"
## [169] "Hadhb" "Nfyc" "Ocel1" "D5Ertd579e"
## [173] "Dpy19l4" "2310034O05Rik" "D8Ertd738e" "Proser1"
## [177] "Cltb" "Rnaseh2c" "Tchh" "1110065P20Rik"
## [181] "Slc22a26" "Ripor3" "Eloc" "Abhd11"
## [185] "Usp22" "Cops8" "Blvrb" "Nudt22"
## [189] "Lrrfip1" "Gbp3" "Patj" "Thnsl1"
## [193] "Polr1a" "Wdr12" "H2bc4" "Gal3st4"
## [197] "Rgs18" "Tnfrsf14" "Rnf7" "H2-T10"
## [201] "Zfp182" "Vmo1" "Nop16" "Epn1"
## [205] "Nsmaf" "Alkbh8" "1810024B03Rik" "Adam10"
## [209] "Ddb2" "Gaa" "Kctd20" "Clca3a2"
## [213] "Ergic3" "Uchl5" "Cwc15" "Siah2"
## [217] "Slfn9" "Slc25a2" "Tuba4a" "Atp5g1"
## [221] "Rin3" "Rps6ka3" "Guk1" "Cdk10"
## [225] "Nsmce3" "Ldha" "Selenow" "Ralb"
## [229] "Klra3" "Smpd1" "Ppp5c" "Plpp6"
## [233] "Lnpep" "Kif3b"
selectVar(RNAseq.splsda, comp = 2)$name
## [1] "Treatment" "Samples" "Zfp28" "Gper1"
## [5] "Hdac4" "Nyap1" "Sike1" "Prpf38a"
## [9] "Ndufb4c" "Pax6" "Plpp6" "Fam47e"
## [13] "E130201H02Rik" "Prss8" "Yars" "Dnajb13"
## [17] "Ezh1" "Ybx1" "Vegfb" "Thnsl1"
## [21] "Serf1" "Btd" "Kctd20" "Mir3061"
## [25] "Llph" "Ralb" "Ccl27a" "Dgkd"
## [29] "Tnfrsf14" "Gal3st4" "Mbtps2" "Rufy2"
## [33] "Gps1" "Cops8" "Arpc1a" "Foxa1"
## [37] "Pold3" "Tmem72" "Gm2614" "Rpgrip1l"
## [41] "Dpm2" "Zdhhc12" "Sntb2" "Cpn1"
## [45] "Veph1" "Gm4755" "Glg1" "Zfp712"
## [49] "Macroh2a1" "Mrps11" "Tmem267" "Tmem238"
## [53] "Scn3a" "Pacs2" "2310061I04Rik" "1810024B03Rik"
## [57] "Tomm6" "1700123O20Rik" "H13" "Rfwd3"
## [61] "Fahd1" "Nprl2" "F2r" "Caprin2"
## [65] "Dxo" "B3gat3" "Katnb1" "Ildr1"
## [69] "Dusp28" "Mcu" "Lactb" "Pdlim2"
## [73] "Crb3" "Slc25a23" "Dhrs3" "Cda"
## [77] "Tarbp1" "Ccne1" "Pik3ap1" "Nipal3"
## [81] "Plekhg2" "Ctsz" "Ptpn14" "Tmem259"
## [85] "Tubgcp2" "Cdk5rap2" "Wrnip1" "Klhdc8b"
## [89] "Ccdc77" "Zbtb46" "Taf1b" "Ndufs7"
## [93] "Bmp2" "Mospd3" "Pak2" "Rbm43"
## [97] "Tab1" "Cyp4b1" "Zpr1" "Gsto1"
## [101] "Nrgn" "Nsmce3" "Ccdc146" "Bbx"
## [105] "Timm17b" "Gbp7" "Gaa"
#SPLSDA miRNA&RNAseq
#miRNA.RNAseq.splsda <- splsda(miRNA.RNAseq.matrix.trt, miRNA.trt$Treatment, RNAseq.trt$Treatment,
# keepX = c(45, 10), keepY = c(234,107)) # 1 Run the method
#plotIndiv(miRNA.splsda, legend = TRUE, title = 'RNAseq, sPLSDA comp 1 - 2',
# legend.title = 'Treatment')
#Background miRNA
background.miRNA <- background.predict(miRNA.splsda, comp.predicted=2,
dist = "max.dist")
plotIndiv(miRNA.splsda, comp = 1:2, group = miRNA.trt$Treatment,
ind.names = FALSE, title = "miRNA Maximum distance",
legend = TRUE, background = background.miRNA)
#Background RNAseq
background.RNAseq <- background.predict(RNAseq.splsda, comp.predicted=2,
dist = "max.dist")
plotIndiv(miRNA.splsda, comp = 1:2, group = RNAseq.trt$Treatment,
ind.names = FALSE, title = "RNAseq Maximum distance",
legend = TRUE, background = background.RNAseq)
#