---
title: "Introduction"
author: 
  - Daniele Ramazzotti
  - Bo Wang
  - Luca De Sano
  - Serafim Batzoglou
date: "`r format(Sys.time(), '%B %d, %Y')`"
graphics: yes
package: SIMLR
output:
  BiocStyle::html_document:
    toc_float: true
vignette: >
  %\VignetteIndexEntry{Introduction}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\VignetteDepends{SIMLR,BiocStyle}
---

## Overview

Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of 
heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, 
visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression 
similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, *SIMLR* (Single-cell 
Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, 
clustering and visualization.. 

```{r include=FALSE}
library(knitr)
opts_chunk$set(
concordance = TRUE,
background = "#f3f3ff"
)
```

## Installing SIMLR

The SIMLR package can be installed from Bioconductor as follow.

```{r, eval=FALSE}
if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SIMLR")
```

# load SIMLR library
library("SIMLR")
```

## Debug

Please feel free to contact us if you have problems running our tool at daniele.ramazzotti1@gmail.com or wangbo.yunze@gmail.com.