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rCASC | ||
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Since the end of the 90’s omics high-throughput technologies have | ||
generated an enormous amount of data, reaching today an exponential | ||
growth phase. Analysis of omics big data is a revolutionary means of | ||
understanding the molecular basis of disease regulation and | ||
susceptibility, and this resource is accessible to the | ||
biological/medical community via bioinformatics frameworks. However, | ||
because of the fast evolution of computation tools and omics methods, | ||
the /reproducibility crisis/ | ||
<https://en.wikipedia.org/wiki/Replication_crisis> is becoming a very | ||
important issue [/Nature, 6 July 2018/ | ||
<https://www.nature.com/collections/prbfkwmwvz>] and there is a | ||
mandatory need to to guarantee robust and reliable results to the | ||
research community [/Global Engage Blog/ | ||
<http://www.global-engage.com/life-science/reproducibility-computational-biology/>]. | ||
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Our group is deeply involved in developing workflows that guarantee both | ||
*functional* (i.e. the information about data and the utilized tools are | ||
saved in terms of meta-data) and *computation* reproducibility (i.e. the | ||
real image of the computation environment used to generate the data is | ||
stored). For this reason we have founded a bioinformatics community | ||
called /reproducible-bioinformatics.org/ | ||
<http://www.reproducible-bioinformatics.org/> /Kulkarni et al. BMC | ||
Bioinformatics/ <https://rdcu.be/9gMq> designed to provide to the | ||
biological community a reproducible bioinformatics ecosystem [/Beccuti | ||
et al. Bioinformatics 2018/ | ||
<https://academic.oup.com/bioinformatics/article/34/5/871/4562334>]. | ||
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rCASC, Cluster Analysis of Single Cells, is part of the | ||
/reproducible-bioinformatics.org/ | ||
<http://www.reproducible-bioinformatics.org/> project and provides | ||
single cell analysis functionalities within the reproducible rules | ||
described by Sandve et al. [/PLoS Comp Biol. 2013/ | ||
<http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003285>]. | ||
rCASC is designed to provide a complete workflow (Figure 1) for | ||
cell-subpopulation discovery. | ||
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Fig. 1:rCASC workflow | ||
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Fig. 1:rCASC workflow | ||
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Installation | ||
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|install.packages("devtools") | ||
library(devtools) | ||
install_github("kendomaniac/rCASC", ref="master")| | ||
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Requirements | ||
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You need to have docker installed on your linux machine, for more info | ||
see this document: https://docs.docker.com/engine/installation/. | ||
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The functions in CASC package require that user is sudo or part of a | ||
docker group. See the following document for more info: | ||
https://docs.docker.com/engine/installation/linux/ubuntulinux/#/manage-docker-as-a-non-root-user | ||
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IMPORTANT The first time casc is installed the downloadContainers needs | ||
to be executed to download to the local repository the containers that | ||
are needed for the use of docker4seq | ||
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|library(rCASC) | ||
downloadContainers()| | ||
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More info: CASC vignette <http://rpubs.com/rcaloger/285423> | ||
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