# Introduction

Daniele Ramazzotti, Bo Wang, Luca De Sano and Serafim Batzoglou

#### May 02, 2023

#### Package

SIMLR 1.26.1

## 0.1 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..

## 0.2 Installing SIMLR

The R version of SIMLR can be installed from Github. To do so, we need to install the R packages SIMLR depends on and the devtools package.

    # install SIMLR dependencies
    if (!require("Matrix")) install.packages("Matrix")
    if (!require("Rcpp")) install.packages("Rcpp")
    if (!require("RcppAnnoy")) install.packages("RcppAnnoy")
    if (!require("RSpectra")) install.packages("RSpectra")
    if (!require("pracma")) install.packages("pracma")

    # install SIMLR library
    if (!require("devtools")) install.packages("devtools")
    library("devtools")
    install_github("BatzoglouLabSU/SIMLR", ref = "master")

    # load SIMLR library
    library("SIMLR")

## 0.3 Debug

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