Rmagic and seurat analysis
Ehsan.Rz
2022-06-16
library(Seurat)## Attaching SeuratObjectlibrary(SeuratData)## ── Installed datasets ───────────────────────────────────── SeuratData v0.2.2 ──## ✓ ifnb 3.1.0 ✓ pbmcsca 3.0.0
## ✓ panc8 3.0.2## ────────────────────────────────────── Key ─────────────────────────────────────## ✓ Dataset loaded successfully
## > Dataset built with a newer version of Seurat than installed
## ❓ Unknown version of Seurat installedlibrary(SeuratWrappers)
library(Rmagic)## Loading required package: Matrixlibrary(ggplot2)
library(viridis)## Loading required package: viridisLitelibrary(phateR)##
## Attaching package: 'phateR'## The following object is masked from 'package:Rmagic':
##
## library.size.normalizelibrary(devtools)## Loading required package: usethislibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(cowplot)The first step is converting seurat to “data.frame” data
pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/KELLY 2020/E11.5/Final_15_clusters.RDS")pbmc## An object of class Seurat
## 16968 features across 2333 samples within 1 assay
## Active assay: RNA (16968 features, 2000 variable features)
## 2 dimensional reductions calculated: pca, umapcounts.df <- pbmc@assays$RNA@counts %>% as.matrix %>% t %>% as.data.frameanalysing with Rmagic. I also selected three candiodate genes, here.
data_magic <- magic(counts.df, genes=c("Col2a1", "Hes1", "Sox9"))data_magic## MAGIC with elements
## $result : (2333, 3)
## $operator : Python MAGIC operator
## $params : list with elements (data, knn, knn.max, decay, t, npca, knn.dist.method)x <- ggplot(counts.df) + geom_point(aes(Col2a1, Hes1, color=Sox9)) + scale_color_viridis(option="C") + xlab("Gene Expression of Col2a1") + ylab("Gene Expression of Hes1")+theme(text = element_text(size = 14, angle = 0, face = "italic"))
x
z <- ggplot(data_magic) + geom_point(aes(Col2a1, Hes1, color=Sox9)) + scale_color_viridis(option="C") + xlab("Gene Expression of Col2a1") + ylab("Gene Expression of Hes1")+theme(text = element_text(size = 14, angle = 0, face = "italic"))
z
what if we change t value here?
magic1 <- magic(counts.df, genes=c("Col2a1", "Hes1","Sox9"), t=6)y <- ggplot(magic1) + geom_point(aes(Col2a1, Hes1, color=Sox9)) + scale_color_viridis(option="C") + xlab("Gene Expression of Col2a1") + ylab("Gene Expression of Hes1")+theme(text = element_text(size = 14, angle = 0, face = "italic"))
y
magic1 <- magic(counts.df, genes=c("Col2a1", "Hes1","Sox9"), t=8)r <- ggplot(magic1) + geom_point(aes(Col2a1, Hes1, color=Sox9)) + scale_color_viridis(option="C") + xlab("Gene Expression of Col2a1") + ylab("Gene Expression of Hes1")+theme(text = element_text(size = 14, angle = 0, face = "italic"))
r
Visualizing MAGIC values on PCA We can visualize the results of MAGIC on
PCA with genes=“pca_only”.
magic_PCA <- magic(counts.df, genes= "pca_only", t=6)a <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Sox9)) + scale_color_viridis(option="B") + labs(color="Sox9")
b <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Col2a1)) + scale_color_viridis(option="B") + labs(color="Col2a1")
c <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Hes1)) + scale_color_viridis(option="B") + labs(color="Hes1")
d <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Grem1)) + scale_color_viridis(option="B") + labs(color="Grem1")
e <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Nes)) + scale_color_viridis(option="B") + labs(color="Nes")
f <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Foxa1)) + scale_color_viridis(option="B") + labs(color="Foxa1")
g <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Lepr)) + scale_color_viridis(option="B") + labs(color="Lepr")
h <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Pdgfra)) + scale_color_viridis(option="B") + labs(color="Pdgfra")
i <- ggplot(magic_PCA) + geom_point(aes(x=PC1, y=PC2, color= counts.df$Pdgfrb)) + scale_color_viridis(option="B") + labs(color="Pdgfrb")
plot_grid(a, b, c, d, e, f, g, h, i)
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.2 (2020-06-22)
## os Ubuntu 20.04.4 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_AU.UTF-8
## ctype en_AU.UTF-8
## tz Australia/Melbourne
## date 2022-06-16
## pandoc 2.17.1.1 @ /usr/lib/rstudio-server/bin/quarto/bin/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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## [1] /mnt/nectar_volume/home/eraz0001/R/x86_64-pc-linux-gnu-library/4.0
## [2] /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/library
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## ─ Python configuration ───────────────────────────────────────────────────────
## python: /usr/bin/python3.8
## libpython: /usr/lib/python3.8/config-3.8-x86_64-linux-gnu/libpython3.8.so
## pythonhome: //usr://usr
## version: 3.8.10 (default, Mar 15 2022, 12:22:08) [GCC 9.4.0]
## numpy: /mnt/nectar_volume/home/eraz0001/.local/lib/python3.8/site-packages/numpy
## numpy_version: 1.22.2
## magic: /home/eraz0001/.local/lib/python3.8/site-packages/magic
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## NOTE: Python version was forced by RETICULATE_PYTHON
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