# 1 How to make it work: Quickstart

## Installing the Uniquorn

Start an R session e.g. using RStudio

if (!requireNamespace("devtools", quietly=TRUE))
install.packages("devtools")

devtools::install_github("RaikOtto/Uniquorn")

if (!requireNamespace("tidyverse", quietly=TRUE))
install.packages("tidyverse")

## Test run

Here the NCI-60 exome sequenced HT29 Cancer Cell line, reference genome GRCh37/ HG19

library("Uniquorn")
library("tidyverse")

HT29_vcf_file = system.file("extdata/HT29.vcf", package="Uniquorn")

ident_result = as_tibble(HT29_vcf_file %>% identify_vcf_file( ref_gen = "GRCH37"))

ident_result %>% dplyr::select(-Library) %>% head()
will show a table with potential identification candidate, how many mutations overall and weighted of the training set have been found and if any training samples have surpassed the identification threshold.

Let us take a look at the amount of matches
match_statistic = ident_result %>% arrange(desc(Matches)) %>% select(CCL,Matches,Library)
match_statistic %>% head() %>% ggplot(aes(CCL, Matches,fill=Library)) + geom_col()

As we can see, multiple matches are observed by chance which is why a p-value on the likelihood of observing matches is required.

Now we will take a look at the mean amount of matching variants per library match_statistic %>% group_by(Library) %>% summarize(mean_match = mean(Matches))

### Demonstration of the impact of data heterogeneity and incompleteness

Let us identify the same CCL again, this time with CCLE and CLP libraries with the incorrect reference genome GRCH38

ident_result = as_tibble(HT29_vcf_file %>% identify_vcf_file( ref_gen = "GRCH38")) ident_result %>% arrange(desc(Matches)) %>% select(CCL,Matches,Library)

We see that even for the incorrect reference random hits are observed. ### Explanation test data

The HT29 cancer cell line vcf file which contains the somatic mutations of the HT-29 cancer cell line is taken from http://watson.nci.nih.gov/projects/nci60/wes/VCF The Watson repository contains the same cancer cell line samples as Uniquorn’s default training dataset, the original CellMiner panel, available at http://discover.nci.nih.gov/cellminer/. However, the cancer cell line vcf sample was differently filtered and different algorithms have been used to predict mutations and variations. Therefore, it was used as an example set to show the difficulties associated with an identification of cancer cell line sample: Some mutations are found only in the query version (here HT-29 from Watson) and some only in the training dataset (here HT-29 from CellMiner original panel). Moreover, some mutations of the query cancer cell line map to different cancer cell lines in the CellMiner training database what can lead to an incorrect identification of a cancer cell line.

Therefore, a robust yet sensitive cancer cell line identification algorithm is required.

You will find a file with the ending ’_uniquorn_identification.tab’ next to the input VCF file if you did not specify the output file path.

# 2 Add CCLE and CoSMIC CLP CL data

Please add the CCLE and COSMIC CLP Cancer Cell Line (CCL) data manually due to legal regulations! Else only the vanilla 60 CellMiner CLs will be available for identification. You can, however, manually add custom CLs.

### Current release (CCLE: DepMap Public 22Q1; COSMIC: v95)

‘CosmicCLP_MutantExport.tsv.gz’, unpack with e.g. gunzip on linux or 7zip on windows from http://cancer.sanger.ac.uk/cell_lines/download. Registration for the website is without charge and not complicated.

initiate_canonical_databases(cosmic_file = 'path/to/cosmic/CosmicCLP_MutantExport.tsv', ccle_file = 'path/to/ccle/CCLE_mutations.csv', ccle_sample_file = 'path/to/sample_info/sample_info.csv', ref_gen = 'GRCH38')

Once the initialization succeeds, about 2000 cancer cell line training sample for about 1200 different cancer cell lines are available in the Uniquorn’s database.

Note: Currently (March 2022), the COSMIC and CCLE data are available for the reference Genome versio GRCh38. It is neccesary, that the reference genome for the training samples is specified if the version is not GRCh37.

### Older releases

Note: The mentioned COSMIC and CCLE files are obsolescent and are not compatible with the current version of Uniquorn (2.3.7).

‘CosmicCLP_MutantExport.tsv.gz’, unpack with e.g. gunzip on linux or 7zip on windows from http://cancer.sanger.ac.uk/cell_lines/download

‘CCLE_hybrid_capture1650_hg19_NoCommonSNPs_NoNeutralVariants_CDS_2012.05.07.maf’ from https://portals.broadinstitute.org/ccle/data under Legacy Data -> Hybrid capture sequencing.

Registration for both websites is without charge and not complicated.

initiate_canonical_databases(ccle_file = 'path_to_ccle/CCLE_hybrid_capture1650_hg19_NoCommonSNPs_NoNeutralVariants_CDS_2012.05.07.maf', cosmic_file = 'path_to_cosmic/CosmicCLP_MutantExport.tsv.gz', ref_gen = "GRCH37")

Once the initialization succeeds, about 2000 cancer cell line training sample for about 1200 different cancer cell lines are available in the Uniquorn’s database.

Note: The CoSMIC CLP data is available for the reference Genome version GRCh38. It is neccesary, that the reference genome for the training samples is specified if the version is not GRCh37.

initiate_canonical_databases(ccle_file = 'path_to_ccle/CCLE_hybrid_capture1650_hg19_NoCommonSNPs_NoNeutralVariants_CDS_2012.05.07.maf', cosmic_file = 'path_to_cosmic/CosmicCLP_MutantExport.tsv.gz', ref_gen = "GRCH38")

# 3 Add training CL samples & utility functions

If you want to identify CL samples not contained in the ‘canonical’ CL set, you can add your own custom CL samples. These samples will be treated just as the ‘canonical’ training-datasets from e.g. CCLE. Note, however, that it is strongly recommended to add at least 10 sample because overfitting might occur if too little custom training-samples are available.

add_custom_vcf_to_database( "path_to_file/my_own_CL_samples.vcf" )

Likewisely, if you want to remove the sample:

remove_custom_vcf_from_database( "Name_of_my_CL_custom_sample" )

If you want to see which CLs are contained:

show_contained_cls( ref_gen = "GRCH37" )

If you want to know which mutations are overall contained in the training set for a particular database:

show_contained_mutations( ref_gen = "GRCH37" )

Same if you want to know which genomic loci are associated with a particular CCL:

show_contained_mutations_for_cl("SF_268_CELLMINER")

# BED files and Broad Institute IGV visualization

Note as well, that there are BED files for the IGV Browser added as well, so that one can see the training, query and missed mutations in the genome. This feature can be switched of by setting the option output_bed_file in the identify_vcf_file function FALSE.

Contact: