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1_2020-02-05_Clustering_of_EGFP.Rmd
---
title: "1 Clustering of EGFP (2 samples combined--using harmony). T-SNE and UMAP"
author: "Yifang Liu"
date: "`r Sys.Date()`"
output:
rmdformats::html_clean:
code_folding: hide
fig_width: 10
fig_height: 10
highlight: kate
thumbnails: false
lightbox: true
gallery: true
---
```{r knitr_init, echo=FALSE, cache=FALSE}
library(knitr)
library(rmdformats)
options(max.print = 200)
opts_chunk$set(echo = TRUE,
cache = FALSE,
prompt = FALSE,
tidy = TRUE,
comment = NA,
message = FALSE,
warning = FALSE,
dev = c('png', 'pdf'),
fig.width = 10,
fig.height = 10,
fig.align = "center",
fig.path = '1_PDF_2020-02-05_Clustering_of_EGFP/',
dpi = 72)
opts_knit$set(width = 75)
```
```{r setup}
set.seed(123)
npc <- 20
# theta1 <- 2
# theta2 <- 5
# theta <- c(theta1, theta2)
resolution <- 0.1
pt_size <- 1
# alpha <- 0.8
# Suppress loading messages
suppressPackageStartupMessages({
library(Matrix)
library(dplyr)
library(tidyverse)
library(Seurat)
library(cowplot)
library(Rcpp)
library(harmony)
library(SoupX)
})
```
```{r UMAP}
EGFP <- readRDS("Data/2020-02-05_EGFP_seurat_obj.Rds")
object <- EGFP
dims <- c(1, 2)
reduction <- "umap"
cells <- colnames(x = object)
data <- Embeddings(object = object[[reduction]])[cells, dims]
data <- as.data.frame(x = data)
dims <- paste0(Key(object = object[[reduction]]), dims)
object[['ident']] <- Idents(object = object)
group_by <- "ident"
data[, group_by] <- object[[group_by]][cells, , drop = FALSE]
data[, "LibraryID"] <- object[["LibraryID"]][cells, , drop = FALSE]
data_G0 <- subset(data, LibraryID == "G0")
data_G1 <- subset(data, LibraryID == "G1")
# group_color <- c("#0000EE","#9d009d","#ff7f0e","#ff0078","#05e259","#35bbf8","#c4af00","#686864","#9467bd","#006c00","#1b8c8b","#8d532e","#9f5084","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22")
```
# UMAP plots of combined and separated G0 and G1 {.tabset}
## UMAP combined with legend
```{r UMAP_combined_with_legend}
# range(data$UMAP_1)
# range(data$UMAP_2)
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-7, 16), ylim = c(-9, 12)) +
theme_cowplot()
```
## UMAP combined
```{r UMAP_combined}
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-7, 16), ylim = c(-9, 12)) +
theme_cowplot() +
theme(legend.position = "none")
```
## G0
```{r UMAP_G0}
data <- data_G0
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-7, 16), ylim = c(-9, 12)) +
theme_cowplot() +
theme(legend.position = "none")
```
## G1
```{r UMAP_G1}
data <- data_G1
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-7, 16), ylim = c(-9, 12)) +
theme_cowplot() +
theme(legend.position = "none")
```
```{r tSNE}
EGFP <- readRDS("Data/2020-02-05_EGFP_seurat_obj.Rds")
object <- EGFP
dims <- c(1, 2)
reduction <- "tsne"
cells <- colnames(x = object)
data <- Embeddings(object = object[[reduction]])[cells, dims]
data <- as.data.frame(x = data)
dims <- paste0(Key(object = object[[reduction]]), dims)
object[['ident']] <- Idents(object = object)
group_by <- "ident"
data[, group_by] <- object[[group_by]][cells, , drop = FALSE]
data[, "LibraryID"] <- object[["LibraryID"]][cells, , drop = FALSE]
data_G0 <- subset(data, LibraryID == "G0")
data_G1 <- subset(data, LibraryID == "G1")
# group_color <- c("#0000EE","#9d009d","#ff7f0e","#ff0078","#05e259","#35bbf8","#c4af00","#686864","#9467bd","#006c00","#1b8c8b","#8d532e","#9f5084","#f7b6d2","#7f7f7f","#c7c7c7","#bcbd22")
```
# tSNE plots of combined and separated G0 and G1 {.tabset}
## tSNE combined with legend
```{r tSNE_combined_with_legend}
# range(data$tSNE_1)
# range(data$tSNE_2)
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-43, 44), ylim = c(-50, 48)) +
theme_cowplot()
```
## tSNE combined
```{r tSNE_combined}
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-43, 44), ylim = c(-50, 48)) +
theme_cowplot() +
theme(legend.position = "none")
```
## G0
```{r tSNE_G0}
data <- data_G0
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-43, 44), ylim = c(-50, 48)) +
theme_cowplot() +
theme(legend.position = "none")
```
## G1
```{r tSNE_G1}
data <- data_G1
ggplot(data = data) +
geom_point(
mapping = aes_string(
x = dims[1],
y = dims[2],
color = "ident"
),
shape = 16,
size = pt_size
) +
# scale_color_manual(values = alpha(group_color, alpha)) +
coord_cartesian(xlim = c(-43, 44), ylim = c(-50, 48)) +
theme_cowplot() +
theme(legend.position = "none")
```
# Notes
2020-02-05:
* Clustering of EGFP (2 samples combined--using harmony). T-SNE and UMAP.
Sun Dec 1, 2019:
* UMAP plots (after SoupX) of combined and separated EGFP and TSC.
Tue Oct 29, 2019:
* use SoupX fixed 0.45 to remove ambient RNA.
Mon Oct 7, 2019:
* Add more sequence depth.
Mon, Sep 30, 2019:
* remove genes: EGFP, Tsc1, gig. Then perform integrate analysis of EGFP, TSC1.
Fri, Sep 20, 2019:
* First version for integrate analysis of EGFP, TSC1.
# Session Info
```{r sessioninfo, message=TRUE}
sessionInfo()
```