# Introduction

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

#### October 24, 2023

#### Package

SIMLR 1.28.0

## 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 SIMLR package can be installed from Bioconductor as follow.

    if (!require("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("SIMLR")

# 1 load SIMLR library

library(“SIMLR”) ```

## 1.1 Debug

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