-
Notifications
You must be signed in to change notification settings - Fork 9
/
Copy pathREADME.Rmd
80 lines (69 loc) · 2.84 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# BatchQC
## Introduction
Sequencing and microarray samples are often collected or processed in multiple
batches or at different times. This often produces technical biases that can
lead to incorrect results in the downstream analysis. BatchQC is a software tool
that streamlines batch preprocessing and evaluation by providing interactive
diagnostics, visualizations, and statistical analyses to explore the extent to
which batch variation impacts the data. BatchQC diagnostics help determine
whether batch adjustment needs to be done, and how correction should be applied
before proceeding with a downstream analysis. Moreover, BatchQC interactively
applies multiple common batch effect approaches to the data, and the user can
quickly see the benefits of each method. BatchQC is developed as a Shiny App.
The output is organized into multiple tabs, and each tab features an important
part of the batch effect analysis and visualization of the data. The BatchQC
interface has the following analysis groups: Upload Data, Experimental Design,
Variation Analysis, Heatmaps, Dendrograms, PCA Analysis, Differential Expression
Analysis, and Data Download.
The package includes:
1. Upload Data; apply desired Normalization and Batch Effect Correction (incl.
ComBat and ComBat-Seq)
2. Experimental Design to view summary of data
3. Variation Analysis
4. Heatmap plot of gene expressions
5. Median Correlation Plot
6. Circular Dendrogram clustered and colored by batch, condition, and covariates
7. Principal Component Analysis and plots
8. Differential Expression Plots and Analysis using DESeq2
9. Data Download to export any corrections or normalizations as an SE object
`BatchQC()` is the function that launches the Shiny App in interactive mode.
## Installation
### Bioconductor Version
When pushed to Bioconductor (aka not yet possible, this will download the old
version of BatchQC):
To begin, install [Bioconductor](http://www.bioconductor.org/) and then install
BatchQC:
```{R, eval = FALSE}
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("BatchQC")
```
### Github Version
To install the most up-to-date version of BatchQC, please install directly from
github. You will need the devtools package. You can install both of these with
the following commands:
```{R, eval = FALSE}
if (!require("devtools", quietly = TRUE)) {
install.packages("devtools")
}
library(devtools)
install_github("wejlab/BatchQC")
```
### Load BatchQC and Launch Shiny App
You should now be able to load BatchQC and launch the shiny app.
```{R, echo = FALSE}
library(BatchQC)
#BatchQC()
```