In kra277/rnaseqviz: A set functions that help in organizing, and visualizing RNA seq results.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

rnaseqviz

The goal of rnaseqviz is to sorting, organizing, and visualizing RNA seq results generated using \code{\link{DESeq2}}. This package is developed to be used in the Aouizerat Lab.

Installation

You can install the development version of rnaseqviz from GitHub with:

# install.packages("devtools")
devtools::install_github("kra277/rnaseqviz")

Example

Below are some basic examples of how to execute the commands:

library(rnaseqviz)
library(tidyverse)
library(corrplot)

PCA Plot

# Prep the normalized count data 
sample_pca_data <- pca_prep(sample_vst_dd, "Group")

# Give the title and a caption
ana <- "Sample Title PC1 vs PC2"
caption <- "Data Source: \n Sample Data"

# Plot the graph
rna_pca_plot(sample_pca_data)

QQ plot

# Give the title and a caption
ana <- "Sample Title PC1 vs PC2"
caption <- "Data Source: \n Sample Data"

# Given DEG P values
rna_qq_plot(sample_clean_deg$pvalue)

Volcano plot

# Give the title and a caption
ana <- "Sample Title PC1 vs PC2"
caption <- "Data Source: \n Sample Data"

# Given DEG dataframe
rna_vol_plot(df = sample_clean_deg, p = pvalue, val = 0.05)

Correlation plots

Correlation plots for batch corrected results

library(RUVSeq)

Perform RUV seq to get surrogate variables

# Perform RUV batch corrections for Hidden batch effects
set <- newSeqExpressionSet(counts(sample_deseq_data))
idx  <- rowSums(counts(set) > 10) >= 1
set  <- set[idx, ]
set <- betweenLaneNormalization(set, which="upper")
not.sig <- rownames(sample_clean_deg)[which(sample_clean_deg$pvalue > .1)]
empirical <- rownames(set)[ rownames(set) %in% not.sig ]
set <- RUVg(set, empirical, k=2)

Plot the correlation between surrogate variables and phenotype

bc_corr_plot(bc_method = "ruv", var_int = "Group", font_size = 1)

kra277/rnaseqviz documentation built on Feb. 6, 2022, 9:43 p.m.