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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-",
eval = FALSE
)
```
# CrossICC
## Table of the content
- [Overview](#overview)
- [Installation](#installation)
* [Via GitHub (latest)](#via-github-latest)
- [Usage](#usage)
- [FAQ](#faq)
- [Contact](#contact)
## Overview
Unsupervised clustering of high-throughput molecular profiling data is widely adopted for discovering cancer subtypes. However, cancer subtypes derived from a single dataset are not usually applicable across multiple datasets from different platforms. We previously published an iterative clustering algorithm to address the issue (see this [paper](http://clincancerres.aacrjournals.org/content/21/4/870.long)), but its use was hampered due to lack of implementation. In this work, we presented CrossICC that was an R package implementation of this method. Moreover, many new features were added to improve the performance of the algorithm. Briefly, [CrossICC](https://github.com/bioinformatist/CrossICC) utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering. [CrossICC](https://github.com/bioinformatist/CrossICC) is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions to help users visualize the identified subtypes and evaluate the subtyping performance. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes.
There are two modes for the integration of clusters derived cross-platform datasets: `cluster` mode and `sample` mode. For `cluster` mode, samples from each platform are clustered separately and centroids of each sub cluster derived from [ConsensusClusterPlus](https://bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.htm) were further clustered to generate super cluster. This process avoided removing batch effect across platforms. The details step by step illustration of this algorithm can be found in our previous published [paper](http://clincancerres.aacrjournals.org/content/early/2014/12/09/1078-0432.ccr-14-2481) and our recent submitted paper[coming soon]. For `sample` mode, sub clusters were firstly derived from [ConsensusClusterPlus](https://bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus.htm) in each platform. We then calculated correlation coefficient between samples and centroids of clusters to get a new feature vector of each samples. Based on this new matrix, samples were divided into new clusters.
## Installation
### Via GitHub (latest)
```{r}
install.packages("devtools")
devtools::install_github("bioinformatist/CrossICC")
```
## Usage
CrossICC has the ability to automatically process **arbitrary numbers** of expression datasets, **no matter which platform they came from** (Even you can **use sequencing and microarray data together**). What you **only** need is a list of matrices in R, without any type of pre-processing (never need manipulation like filtering or normalization).
```{r}
library(CrossICC)
CrossICC.obj <- CrossICC(demo.platforms, skip.mfs = TRUE, max.iter = 100,
cross = "cluster", fdr.cutoff = 0.1,
ebayes.cutoff = 0.1, filter.cutoff = 0.1)
```
CrossICC will automatically iterate your data until it reaches convergence. By default, CrossICC will generate an `.rds` formatted object in your home path (`~/`, a.k.a `$HOME` in Linux), followed by an shiny app as shown below that is opened in your default browser, which provides you a very intuitive way to view the results.
## Shiny app
Our package also comes with a shiny app. To run it:
```r
pkg.suggested <- c('ggalluvial', 'rmarkdown', 'knitr', 'shiny', 'shinydashboard', 'shinyWidgets', "shinycssloaders", 'DT', 'ggthemes', 'ggplot2', 'pheatmap', 'RColorBrewer', 'tibble')
checkPackages <- function(pkg){
if (!requireNamespace(pkg, quietly = TRUE)) {
stop("Package pkg needed for shiny app. Please install it.", call. = FALSE)
}
}
lapply(pkg.suggested, checkPackages)
shiny::runApp(system.file("shiny", package = "CrossICC"))
```
## FAQ
- Question 1: NA values involved in our data set, how to go through them?
> A: Users may encounter unexpected errors due to NA values in raw dataset. Therefore, we strongly recommanded that users checked the NA valus in their data set before loading it into `CrossICC`. To check the completed cases in matrix, `completed.cases` can be a good option to do that. Here, we also present an example for users to impute there data in case they don't want to remove case in the dataset. The imputation method shown here are KNN method from impute package.
```{r}
# for a individual matrix, plz do imputation using the following r code
tempdata.impute=impute.knn(as.matrix(tempdata) ,k = 10, rowmax = 0.5, colmax = 0.8)
normalize.Data=as.data.frame(tempdata.impute$data)
```
## Contribution
Qi Zhao [\@likelet](https://github.com/likelet) and Yu Sun [\@bioinformatist](https://github.com/bioinformatist) implemented the packages. Zhixiang Zuo [\@zhixiang](https://scholar.google.com/citations?user=Ln_bw9AAAAAJ&hl=zh-CN) supervise the project. Zekun Liu performed the test and helped run an example of the package. For more information or questions, plz contact either of the authors above.