Using rcellminer
29 October 2024
Contents
1 Overview
The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60, and to provide a powerful suite of tools for exploration of NCI-60 data. While CellMiner is an unmatched resource for online exploration of the NCI-60 data, consideration of more specialized scientific questions often requires custom programming. The rcellminer R package complements the functionality of CellMiner, providing programmatic data access, together with functions for data visualization and analysis. These functions are approachable for even beginning R users, as illustrated by the initial examples below. The subsequent case studies, inspired by CellMiner-related publications, show how modest amounts of code can script specialized analyses, integrating multiple types of data to yield new scientific insights. rcellminer functions also provide robust building blocks for more extensive tools, as exemplifed by the package’s interactive Shiny applications.
2 Basics
2.1 Installation
if (!requireNamespace("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("rcellminer")
BiocManager::install("rcellminerData")2.2 Getting Started
Load rcellminer and rcellminerData packages:
library(rcellminer)
library(rcellminerData)A list of all accessible vignettes and methods is available with the following command.
help.search("rcellminer")2.3 Searching for Compounds
The NSC number is a numeric identifier for substances submitted to the National Cancer Institute (NCI) for testing and evaluation. It is a registration number for the Developmental Therapeutics Program (DTP) repository, and it is used as the unique identifier for compounds in the CellMiner database. NSC stands for National Service Center.
rcellminer allows users to quickly search for NSC IDs by compound name or partial name. For example, many kinase inhibitors end with the suffix “nib”. Users can quickly search NSCs for compound names with this suffix; queries are case insensitive and are treated as regular expressions.
searchForNscs("nib$") ## Fostamatinib Semaxanib Gefitinib Erlotinib Lapatinib
## 365798 696819 715055 718781 727989
## Dasatinib Pazopanib Selumetinib Imatinib Lapatinib
## 732517 737754 741078 743414 745750
## Nilotinib Sunitinib Afatinib Pazopanib Amuvatinib
## 747599 750690 750691 752782 754349
## Bosutinib Masitinib Cediranib Foretinib Lenvatinib
## 755389 755400 755606 755775 755980
## Crizotinib Quizartinib Linsitinib Intedanib Cabozantinib
## 756645 756647 756652 756659 757436
## Neratinib Axitinib Intedanib Sapitinib Tivozanib
## 757439 757441 757442 758005 758007
## Tivantinib tepotinib Trametinib Ponatinib Saracatinib
## 758242 758244 758246 758487 758872
## Dovitinib Gefitinib Dasatinib Tipifarnib Vandetanib
## 759661 759856 759877 760444 760766
## Tandutinib Motesanib Cabozantinib brigatinib Vemurafenib
## 760841 760843 761068 761191 761431
## Ibrutinib Ruxolitinib Crenolanib Alectinib Dabrafenib
## 761910 763371 763526 764040 764134
## Brivanib Gandotinib Alectinib Varlitinib Bosutinib
## 764481 764820 764821 764823 765694
## Refametinib Dacomitinib Momelotinib Fedratinib Lestaurtinib
## 765866 765888 767598 767600 772196
## Vatalanib Apatinib Fostamatinib Bafetinib Rebastinib
## 772499 772886 772992 773263 774831
## Telatinib Encorafenib Defactinib Osimertinib spebrutinib
## 776017 778304 778364 779217 780020
## Volitinib Defactinib Poziotinib Altiratinib brigatinib
## 782121 782549 783296 784590 787457
## gilteritinib Bafetinib sitravatinib Acalabrutinib olmutinib
## 787846 788186 788203 791164 792848
## ensartinib ulixertinib Sulfatinib zanubrutinib Afatinib
## 793150 797771 797937 799318 799327
## Alectinib Axitinib Bafetinib Binimetinib Quizartinib
## 799328 799341 799354 799361 799659
## Cabozantinib Ceritinib Cobimetinib Crenolanib Crizotinib
## 800066 800072 800075 800079 800080
## Dacomitinib Dasatinib Defactinib Dovitinib Entrectinib
## 800084 800087 800089 800092 800095
## Fedratinib Foretinib Fostamatinib Gefitinib Gilteritinib
## 800099 800101 800102 800105 800106
## Golvatinib Ibrutinib Lapatinib Lestaurtinib Linsitinib
## 800107 800769 800780 800782 800784
## Masitinib Momelotinib Neratinib Nilotinib Osimertinib
## 800789 800800 800803 800804 800812
## Pacritinib Pazopanib Pelitinib Pexmetinib Ponatinib
## 800814 800839 800841 800844 800855
## Poziotinib Quizartinib Rebastinib Refametinib Rociletinib
## 800856 800857 800863 800864 800872
## Sapitinib Saracatinib Selumetinib Sunitinib Tesevatinib
## 800876 800878 800882 800937 800946
## Tivantinib Tivozanib Trametinib Ulixertinib Vandetanib
## 800951 800952 800956 800959 800961
## Varlitinib Vemurafenib Canertinib futibutinib Belvarafenib
## 800962 800964 801011 813488 817040
## Zanubrutinib Mobocertinib Cerdulatinib Fruquintinib Anlotinib
## 823807 825519 825827 829498 8325232.4 Profile Visualization
Often, it is useful for researchers to plot multiple data profiles next to each other in order to visually identify patterns. Below are examples for the visualization of various profiles: single drugs and multiple drugs, as well as molecular profiles and combinations of drug and molecular profiles.
# Get Cellminer data
drugAct <- exprs(getAct(rcellminerData::drugData))
molData <- getMolDataMatrices()
# One drug
nsc <- "94600"
plots <- c("drug")
plotCellMiner(drugAct, molData, plots, nsc, NULL)
# One expression
gene <- "TP53"
plots <- c("exp")
plotCellMiner(drugAct, molData, plots, NULL, gene)