# seqCAT: The High Throughput Sequencing Cell Authentication Toolkit

Erik Fasterius

#### 2022-04-26

#### Package

seqCAT 1.18.0

# 1 Introduction

This vignette describes the use of the seqCAT package for authentication, characterisation and evaluation of two or more High Throughput Sequencing samples (HTS; RNA-seq or whole genome sequencing). The principle of the method is built upon previous work, where it was demonstrated that analysing the entirety of the variants found in HTS data provides unprecedented statistical power and great opportunities for functional evaluation of genetic similarities and differences between biological samples (Fasterius et al. 2017; Fasterius and Szigyarto 2018).

The seqCAT package work by creating Single Nucelotide Variant (SNV) profiles of every sample of interest, followed by comparisons between each set to find overall genetic similarity, in addition to detailed analyses of the differences. By analysing your data with this workflow you will not only be able to authenticate your samples to a high degree of confidence, but you will also be able to investigate what genes and transcripts are affected by SNVs differing between your samples, what biological effect they will have, and more. The workflow consists of three separate steps:

    1.  Creation of SNV profiles
    2.  Comparisons of SNV profiles
    3.  Authentication, characterisation and evaluation of profile comparisons

Each step has its own section(s) below demonstrating how to perform the analyses. Input data should be in the form of VCF files, i.e output from variant callers such as the Genome Analysis ToolKit and optionally annotated with software such as SnpEff.

## 1.1 Installation

The latest stable release of this package can be found on Bioconductor and installed with:

    install.packages("BiocManager")
    BiocManager::install("seqCAT")

This will also install any missing packages requires for full functionality, should they not already exist in your system. If you haven’t installed Bioconductor, you can do so by simply calling `BiocManager::install()` without specifying a package, and it will be installed for you. You can read more about this at Bioconductor’s installation page. You can also find the development version of seqCAT on GitHub, which can be installed like so:

    install.packages("devtools")
    devtools::install_github("fasterius/seqCAT")

# 2 Creation of SNV profiles

The first step of the workflow is to create the SNV profile of each sample, which can then be compared to each other. The creation of a SNV profile includes filtering of low-confidence variants, removal of variants below a sequencing depth threshold (`10` by default), de-duplication of variants and an optional removal of mitochondrial variants (`TRUE` by default). For annotated VCF files, only records with the highest SNV impact (i.e. impact on protein function) for each variant is kept, as they are most likely to affect the biology of the cells.

## 2.1 Create individual profiles

Throughout this vignette we will be using some example data, `example.vcf.gz`, which comes from the initial publication of the general process of this method (Fasterius et al. 2017). It is a simplified multi-sample VCF file on a subset of chromosome 12 (containing all variants up to position `25400000`, in order to keep the file size low) for three different colorectal cancer cell lines: HCT116, HKE3 and RKO. The first step is to load the `seqCAT` package and to create SNV profiles for each sample:

    # Load the package
    library("seqCAT")

    # List the example VCF file
    vcf <- system.file("extdata", "example.vcf.gz", package = "seqCAT")

    # Create two SNV profiles
    hct116 <- create_profile(vcf, "HCT116")
    head(hct116)

    ##   chr   pos        rsID               gene          ENSGID          ENSTID REF
    ## 1  12 80385 rs370087224 ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   C
    ## 2  12 80399        None ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   G
    ## 3  12 80422 rs373297723 ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   G
    ## 4  12 80729 rs375960073 ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   A
    ## 5  12 83011 rs370570891 ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   T
    ## 6  12 83012 rs374646339 ABC7-42389800N19.1 ENSG00000226210 ENST00000400706   C
    ##   ALT   impact         effect    feature                biotype DP AD1 AD2 A1
    ## 1   T MODIFIER intron_variant transcript unprocessed_pseudogene 10   8   2  C
    ## 2   A MODIFIER intron_variant transcript unprocessed_pseudogene 10   4   6  G
    ## 3   A MODIFIER intron_variant transcript unprocessed_pseudogene 15  11   4  G
    ## 4   G MODIFIER intron_variant transcript unprocessed_pseudogene 18  13   5  A
    ## 5   C MODIFIER intron_variant transcript unprocessed_pseudogene 10   3   7  T
    ## 6   G MODIFIER intron_variant transcript unprocessed_pseudogene 10   3   7  C
    ##   A2 FILTER warnings sample
    ## 1  T   PASS          HCT116
    ## 2  A   PASS          HCT116
    ## 3  A   PASS          HCT116
    ## 4  G   PASS          HCT116
    ## 5  C   PASS          HCT116
    ## 6  G   PASS          HCT116

The SNV profile lists all the variants present in the VCF file, in addition to any annotations present. This means that information pertaining to the genomic position (`chr` and `pos`), reference and alternative alleles (`REF` and `ALT`), genotype (`A1` and `A2`) and depth (`DP`, variant depth; `AD1` and `AD2`, allelic depth) are always going to be present in all profiles. The profiles creates here also contain additional annotations from SnpEff, such as gene (`ENSGID`), variant impact (`impact`) and variant accession (`rsID`).

## 2.2 Variant filtration

The creation of SNV profiles include several optional variant filtration steps, including criteria for sequencing depth, variant caller-specific thresholds, mitochondrial variants, variants in non-standard chromosomes and variants duplicated at either the gene-level or positional level. The `create_profile` function performs all of these by default (with a sequencing depth of at least 10 and de-duplication at the gene-level), but you can omit or change these as required, like so:

    rko <- create_profile(vcf, "RKO", min_depth = 15, filter_gd = FALSE)

The profile of this sample (`RKO`) was created with a non-standard filter for sequencing depth (`min_depth = 15`), which should only be done if you want a stricter criteria for your profile (such as when you’re only interested in higher-than-standard confidence variants).

You may also choose to not remove variants using the variant caller-specific filtering criteria by passing `filter_vc = FALSE` when creating your profile, although it is recommended to do so in most cases in order to minimise the number of false positive variant calls. Mitochondrial variants are removed by default, but may be kept by passing `filter_mt = FALSE`; the same goes for non-standard chromosomes and the `filter_ns` parameter.

Duplicate variants can either be removed at the gene-level (`filter_gd = TRUE` by default) or at the positional level (`filter_pd = FALSE` by default). The former will remove variants that have more than one entry on a per-gene basis (i.e. variants that affect more than one transcript for the same gene), but keep multiple entries for variants affecting more than one gene; the impact is used to determine which of the same-gene variants to keep. The latter will remove duplicated variants purely by their position, regardless of the genes or transcripts they affect. It is thus important to know what type of downstream analyses you want to perform at this stage: if analysis of which genes or transcripts are affected by the variants is desired, duplicates should either be remove at the gene-level or not at all; if only not, variant duplicates may be remove at the positional level.

Filtration can also be performed after the initial SNV profile creation, using two separate functions:

    # Filter on sequencing depth
    rko_filtered <- filter_variants(rko, min_depth = 20)

    # Filter position-level variants duplicates
    rko_deduplicated <- filter_duplicates(rko, filter_pd = TRUE)

## 2.3 Create multiple profiles

The `create_profiles` function is a convenience wrapper for `create_profile`, which will create SNV profiles for each VCF file in a given input directory and return them as a list. You can use it for all the VCFs in a directory or a subset specified by a string, like so:

    # Directory with VCF files
    vcf_dir <- system.file("extdata", package = "seqCAT")

    # Create profiles for each VCF with "sample1" in its name
    profiles <- create_profiles(vcf_dir, pattern = "sample1")

## 2.4 Create COSMIC profiles

It is also possible to to compare your samples’ variants to some external source. Such a source is the Catalogue of somatic mutations in cancer, or COSMIC. (Forbes et al. 2015) COSMIC has over a thousand cell line-specific mutational profiles as well as a comprehensive list of cancer mutations in across many carcinoma samples, and is thus a very useful resource.

In order to use the COSMIC database, you need to sign up for an account at their website and get permission to download their files (which is given free of charge to academia and non-profit organisation, but requires a commersial license for for-profit organisations). SeqCAT can analyse both the cell line-specific (the `CosmicCLP_MutantExport.tsv.gz` file) and cancer mutation data (the `CosmicCompleteTargetedScreensMutantExport.tsv.gz` file) that can be found here. As redistributing this data is not allowed, this package includes an extremely minimal subset of the original files, only useful for examples in this vignette and unit testing. Do not use these file for your own analyses, as your results will neither be complete nor accurate!

Here we present an example of how to analyse some cell line-specific COSMIC data. The first thing to check is to see if your specific cell line is available in the database, which can be accomplished using the `list_cosmic` function:

    file <- system.file("extdata", "subset_CosmicCLP_MutantExport.tsv.gz",
                        package = "seqCAT")
    cell_lines <- list_cosmic(file)
    head(cell_lines)

    ## [1] "639V"  "A427"  "A549"  "AGS"   "AMO1"  "AN3CA"

This gives us a simple vector containing all the available sample names in the COSMIC database (this version of the file is for the GRCh37 assembly). You can search it for a cell line of your choice:

    any(grepl("HCT116", cell_lines))

    ## [1] TRUE

All COSMIC-related functions perform some simplification of sample names (as there is variation in the usage of dashes, dots and other symbols), and are case-insensitive. When you have asserted that your sample of interest is available, you can then read the profile for that sample using the `read_cosmic` function:

    cosmic <- read_cosmic(file, "HCT116")
    head(cosmic)

    ##    chr      pos REF ALT A1 A2 gene          ENSTID gene_cds_length hgnc_id
    ## 43  12 25398281   C   T  C  T KRAS ENST00000311936             567    6407
    ##           sample id_sample id_tumour    primary_site site_subtype_1
    ## 43 COSMIC.HCT116    905936    823462 LARGE_INTESTINE          colon
    ##    site_subtype_2 site_subtype_3 primary_histology histology_subtype_1
    ## 43             NS             NS         carcinoma                  NS
    ##    histology_subtype_2 histology_subtype_3 genome_wide_screen      id     cds
    ## 43                  NS                  NS                  y COSM532 c.38G>A
    ##        aa             description loh grch snp fathmm_prediction fathmm_score
    ## 43 p.G13D Substitution - Missense   u   37   n        PATHOGENIC      0.97875
    ##                                  somatic_status verification_status pubmed_pmid
    ## 43 Reported in another cancer sample as somatic            Verified          NA
    ##    id_study                          institute
    ## 43      619 Developmental Therapeutics Program
    ##                               institute_address catalogue_number sample_source
    ## 43 National Cancer Institute,Frederick,MD 21701                      cell-line
    ##    tumour_origin age
    ## 43       primary  NA

You now have a small, COSMIC SNV profile for your cell line, which you can compare to any other profile you may have data for (more on this below). You can also check how many variants are listed in COSMIC for your particular cell:

    nrow(cosmic)

    ## [1] 1

Here we only see a single variant for the HCT116 cell line, which is only because of the extreme small subset of the COSMIC databse being used here. HCT116 has, in fact, over 2000 listed COSMIC SNVs, making it one of the more abundantly characterised cell lines available (as most cell lines has only a few hundred SNVs listed in COSMIC). A COSMIC profile of a couple of hundred variants is more common, though, and any analysis based only on COSMIC variants is thus inherently limited.

## 2.5 Working with profiles on disk

While computation time is usually not an issue for simple binary comparisons (i.e. comparisons with only two samples), this can quickly become a concern for analyses where samples are compared to several others (A vs B, A vs C, …, and so on); this is doubly true for annotated VCF files. It can thus be highly useful to save profiles to disk in some cases, in order to facilitate re-analysis at a later stage. This can be done with the `write_profile` function:

    write_profile(hct116, "hct116.profile.txt")

You may also store profiles in several other formats, including BED, GTF and GFF; these are automatically detected based on the filename:

    write_profile(hct116, "hct116.profile.bed")

The `write_profiles` function is a convenience-wrapper from `write_profile`, which can save many profiles (stored in a list) to disk at the same time:

    write_profiles(profiles, format = "GTF", directory = "./")

Profiles stored on disk may be read into R again at a later time using the `read_profile` function:

    hct116 <- read_profile("hct116.profile.txt")

The `read_profiles` function is a convenience-wrapper for `read_profile`, which will automatically read all the profiles present in a given directory (based on the `pattern` argument) and return them as a list.

    profile_list <- read_profiles(profile_dir = "./", pattern = ".gtf")
    head(profile_list[[1]])

    ##   chr   pos        rsID    gene          ENSGID          ENSTID REF ALT
    ## 1   1 16229        None DDX11L1 ENSG00000223972 ENST00000456328   C   A
    ## 2   1 16298 rs200451305 DDX11L1 ENSG00000223972 ENST00000456328   C   T
    ## 3   1 16495 rs141130360 DDX11L1 ENSG00000223972 ENST00000450305   G   C
    ## 4   1 16495 rs141130360  WASH7P ENSG00000227232 ENST00000423562   G   C
    ## 5   1 16534 rs201459529 DDX11L1 ENSG00000223972 ENST00000450305   C   T
    ## 6   1 16534 rs201459529  WASH7P ENSG00000227232 ENST00000423562   C   T
    ##     impact                  effect    feature
    ## 1 MODIFIER downstream_gene_variant transcript
    ## 2 MODIFIER downstream_gene_variant transcript
    ## 3 MODIFIER downstream_gene_variant transcript
    ## 4 MODIFIER          intron_variant transcript
    ## 5 MODIFIER downstream_gene_variant transcript
    ## 6 MODIFIER          intron_variant transcript
    ##                              biotype DP AD1 AD2 A1 A2 FILTER warnings  sample
    ## 1               processed_transcript 27  21   6  C  A   PASS          sample1
    ## 2               processed_transcript 19   8  11  C  T   PASS          sample1
    ## 3 transcribed_unprocessed_pseudogene 33  24   9  G  C   PASS          sample1
    ## 4             unprocessed_pseudogene 33  24   9  G  C   PASS          sample1
    ## 5 transcribed_unprocessed_pseudogene 22  13   9  C  T   PASS          sample1
    ## 6             unprocessed_pseudogene 22  13   9  C  T   PASS          sample1

# 3 Comparing SNV profiles

## 3.1 Comparing full profiles

Once each relevant sample has its own SNV profile the comparisons can be performed. SNV profiles contain most of the relevant annotation data from the original VCF file, including SNV impacts, gene/transcript IDs and mutational (rs) ID. The `DP` (depth) field lists the total sequencing depth of this variant, while the specific allelic depths can be found in `AD1` and `AD2`. The alleles of each variant can be found in `A1` and `A2`.

Once each profile has been defined, the genotypes of the overlapping variants between them can be compared using the `compare_profiles` function. Only variants found in both profiles are considered to overlap by default, as similarity calculations between profiles where some variants only have confident calls in one of the samples may be inappropriate. An SNV is considered a match if it has an identical genotype in both profiles.

    hct116_rko <- compare_profiles(hct116, rko)
    head(hct116_rko)

    ##   chr   pos sample_1 sample_2 match        rsID               gene
    ## 1  12 80729   HCT116      RKO match rs375960073 ABC7-42389800N19.1
    ## 2  12 83508   HCT116      RKO match rs374142069 ABC7-42389800N19.1
    ## 3  12 83560   HCT116      RKO match rs368663404 ABC7-42389800N19.1
    ## 4  12 83979   HCT116      RKO match rs369733672 ABC7-42389800N19.1
    ## 5  12 84000   HCT116      RKO match rs374158904 ABC7-42389800N19.1
    ## 6  12 84096   HCT116      RKO match rs376990822 ABC7-42389800N19.1
    ##            ENSGID          ENSTID REF ALT   impact         effect    feature
    ## 1 ENSG00000226210 ENST00000400706   A   G MODIFIER intron_variant transcript
    ## 2 ENSG00000226210 ENST00000400706   T   G MODIFIER intron_variant transcript
    ## 3 ENSG00000226210 ENST00000400706   G   T MODIFIER intron_variant transcript
    ## 4 ENSG00000226210 ENST00000400706   T   C MODIFIER intron_variant transcript
    ## 5 ENSG00000226210 ENST00000400706   C   G MODIFIER intron_variant transcript
    ## 6 ENSG00000226210 ENST00000400706   C   G MODIFIER intron_variant transcript
    ##                  biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116 A2.HCT116
    ## 1 unprocessed_pseudogene        18         13          5         A         G
    ## 2 unprocessed_pseudogene        26         21          5         T         G
    ## 3 unprocessed_pseudogene        21         14          7         G         T
    ## 4 unprocessed_pseudogene        51         42          9         T         C
    ## 5 unprocessed_pseudogene        52         42         10         C         G
    ## 6 unprocessed_pseudogene        65         49         16         C         G
    ##   FILTER.HCT116 warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO FILTER.RKO
    ## 1          PASS                     15       8       7      A      G       PASS
    ## 2          PASS                     17       6      11      T      G       PASS
    ## 3          PASS                     16       4      12      G      T       PASS
    ## 4          PASS                     23      14       9      T      C       PASS
    ## 5          PASS                     18       9       9      C      G       PASS
    ## 6          PASS                     18      10       8      C      G       PASS
    ##   warnings.RKO
    ## 1
    ## 2
    ## 3
    ## 4
    ## 5
    ## 6

The resulting dataframe retains all the information from each input profile (including any differing annotation, should they exist), and lists the depths and alleles by adding the sample names as suffixes to the relevant column names. An optional parameter, `mode`, can also be supplied: the default value (`"intersection"`) discards any non-overlapping variants in the comparison, while setting it to `"union"` will retain them.

    hct116_rko_union <- compare_profiles(hct116, rko, mode = "union")
    head(hct116_rko_union)

    ##   chr   pos sample_1 sample_2       match        rsID               gene
    ## 1  12 80385   HCT116      RKO HCT116_only rs370087224 ABC7-42389800N19.1
    ## 2  12 80399   HCT116      RKO HCT116_only        None ABC7-42389800N19.1
    ## 3  12 80422   HCT116      RKO HCT116_only rs373297723 ABC7-42389800N19.1
    ## 4  12 80610   HCT116      RKO    RKO_only        None ABC7-42389800N19.1
    ## 5  12 80729   HCT116      RKO       match rs375960073 ABC7-42389800N19.1
    ## 6  12 83011   HCT116      RKO HCT116_only rs374646339 ABC7-42389800N19.1
    ##            ENSGID          ENSTID REF ALT   impact         effect    feature
    ## 1 ENSG00000226210 ENST00000400706   C   T MODIFIER intron_variant transcript
    ## 2 ENSG00000226210 ENST00000400706   G   A MODIFIER intron_variant transcript
    ## 3 ENSG00000226210 ENST00000400706   G   A MODIFIER intron_variant transcript
    ## 4 ENSG00000226210 ENST00000400706   C   G MODIFIER intron_variant transcript
    ## 5 ENSG00000226210 ENST00000400706   A   G MODIFIER intron_variant transcript
    ## 6 ENSG00000226210 ENST00000400706   C   G MODIFIER intron_variant transcript
    ##                  biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116 A2.HCT116
    ## 1 unprocessed_pseudogene        10          8          2         C         T
    ## 2 unprocessed_pseudogene        10          4          6         G         A
    ## 3 unprocessed_pseudogene        15         11          4         G         A
    ## 4 unprocessed_pseudogene
    ## 5 unprocessed_pseudogene        18         13          5         A         G
    ## 6 unprocessed_pseudogene        10          3          7         C         G
    ##   FILTER.HCT116 warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO FILTER.RKO
    ## 1          PASS
    ## 2          PASS
    ## 3          PASS
    ## 4                                   16      11       5      C      G       PASS
    ## 5          PASS                     15       8       7      A      G       PASS
    ## 6          PASS
    ##   warnings.RKO
    ## 1
    ## 2
    ## 3
    ## 4
    ## 5
    ## 6

## 3.2 Comparing to COSMIC profiles

If you only want to analyse a subset of your data or as a orthogonal method complementary to others, you could compare your profile to a COSMIC profile. This works in the same way as comparing to another full profile, but gives slightly different output:

    hct116_cosmic <- compare_profiles(hct116, cosmic)
    head(hct116_cosmic)

    ##   chr      pos sample_1      sample_2 match        rsID          ENSGID
    ## 1  12 25398281   HCT116 COSMIC.HCT116 match rs112445441 ENSG00000133703
    ##     impact           effect    feature        biotype gene
    ## 1 MODERATE missense_variant transcript protein_coding KRAS
    ##                              ENSTID REF ALT DP.HCT116 AD1.HCT116 AD2.HCT116
    ## 1 [ENST00000256078,ENST00000311936]   C   T       180         96         84
    ##   A1.HCT116 A2.HCT116 FILTER.HCT116 warnings.HCT116 A1.COSMIC.HCT116
    ## 1         C         T          PASS                                C
    ##   A2.COSMIC.HCT116 gene_cds_length.COSMIC.HCT116 hgnc_id.COSMIC.HCT116
    ## 1                T                           567                  6407
    ##   id_sample.COSMIC.HCT116 id_tumour.COSMIC.HCT116 primary_site.COSMIC.HCT116
    ## 1                  905936                  823462            LARGE_INTESTINE
    ##   site_subtype_1.COSMIC.HCT116 site_subtype_2.COSMIC.HCT116
    ## 1                        colon                           NS
    ##   site_subtype_3.COSMIC.HCT116 primary_histology.COSMIC.HCT116
    ## 1                           NS                       carcinoma
    ##   histology_subtype_1.COSMIC.HCT116 histology_subtype_2.COSMIC.HCT116
    ## 1                                NS                                NS
    ##   histology_subtype_3.COSMIC.HCT116 genome_wide_screen.COSMIC.HCT116
    ## 1                                NS                                y
    ##   id.COSMIC.HCT116 cds.COSMIC.HCT116 aa.COSMIC.HCT116 description.COSMIC.HCT116
    ## 1          COSM532           c.38G>A           p.G13D   Substitution - Missense
    ##   loh.COSMIC.HCT116 grch.COSMIC.HCT116 snp.COSMIC.HCT116
    ## 1                 u                 37                 n
    ##   fathmm_prediction.COSMIC.HCT116 fathmm_score.COSMIC.HCT116
    ## 1                      PATHOGENIC                    0.97875
    ##                   somatic_status.COSMIC.HCT116
    ## 1 Reported in another cancer sample as somatic
    ##   verification_status.COSMIC.HCT116 pubmed_pmid.COSMIC.HCT116
    ## 1                          Verified
    ##   id_study.COSMIC.HCT116            institute.COSMIC.HCT116
    ## 1                    619 Developmental Therapeutics Program
    ##                institute_address.COSMIC.HCT116 catalogue_number.COSMIC.HCT116
    ## 1 National Cancer Institute,Frederick,MD 21701
    ##   sample_source.COSMIC.HCT116 tumour_origin.COSMIC.HCT116 age.COSMIC.HCT116
    ## 1                   cell-line                     primary

You can use all the functions for downstream analyses for comparisons with COSMIC data, but your options for functional analyses will be limited, given that the COSMIC database is biased towards well-known and characterised mutations. It is, however, an excellent way to authenticate your cell lines and to assert the status of the mutations that exist in the analysed cells.

# 4 Evaluating binary comparisons

## 4.1 Similarity and global statistics

When you have your matched, overlapping SNVs, it’s time to analyse and characterise them. The first thing you might want to check are the global similarities and summary statistics, which can be done with the `calculate_similarity` function. The `concordance` is simply the number of matching genotypes divided by the total number of overlapping variants, while the `similarity score` is a weighted measure of the concordance in the form of a binomial experiment, taking into account the number of overlapping variants available:

\[Similarity = \frac{s + a}{n + a + b}\]

… where `s` is the number of matching genotypes, `n` is the total number of overlapping SNVs, `a` and `b` being the parameters used to weigh the concordance in favour of comparisons with more overlaps. The default parameters of `1` and `5` were selected to yield an equivalent cutoff to one suggested by Yu et al. (2015), which results in a lower limit 44 of perfectly matching overlapping variants with a similarity score of 90. The similarity score is thus a better measure of biological equivalency than just the concordance.

    similarity <- calculate_similarity(hct116_rko)
    similarity

    ##   sample_1 sample_2 variants_1 variants_2 overlaps matches concordance
    ## 1   HCT116      RKO        259        259      259     181        69.9
    ##   similarity_score
    ## 1             68.7

Here, you can see a summary of the relevant statistics for your particular comparison: the number of total variants from each profile (if the comparison was done with `mode = "union"`, otherwise this number will just be equivalent to the overlaps), the number of overlaps between your two samples, the number of matching genotypes, their concordance as well as their similarity score. The cutoff used by Yu et al. for cell line authenticity was `90 %` for their 48 SNP panel, something that could be considered the baseline for this method as well. The score, `68.7`, is well below that cutoff, and we can thus be certain that these two cells are indeed not the same (as expected). While hard thresholds for similarity are inadvisable, a general guideline is that comparisons with scores above `90` can be considered similar while those below can be considered dissimilar. While a score just below `90` does not mean that the cells definitely are different, it does mean that more rigorous evaluation needs to be performed in order to ensure their biological equivalency. Are there specific genes or regions that are of special interest, for example? If so, it might be informative to specifically investigate the similarity there (more on this below).

You may additionally change the parameters of the score (if you, for example, want a stricter calculation). You may also supply the `calculate_similarity` function with an existing dataframe with summary data produced previously, in order to aggregate scores and statistics for an arbitrary number of comparisons.

    # Create and read HKE3 profile
    hke3 <- create_profile(vcf, "HKE3")

    # Compare HCT116 and HKE3
    hct116_hke3 <- compare_profiles(hct116, hke3)

    # Add HCT116/HKE3 similarities to HCT116/RKO similarities
    similarities <- calculate_similarity(hct116_hke3, similarity, b = 10)
    similarities

    ##   sample_1 sample_2 variants_1 variants_2 overlaps matches concordance
    ## 1   HCT116      RKO        259        259      259     181        69.9
    ## 2   HCT116     HKE3        493        493      493     475        96.3
    ##   similarity_score
    ## 1             68.7
    ## 2             94.4

Notice that the new `similarities` dataframe contains both the comparisons of HCT116/RKO and HCT116/HKE3, and we can clearly see that HCT116 and HKE3 are indeed very similar, as expected (HKE3 was derived from HCT116). This is true even when using a higher value for the `b` parameter. Any number of samples can be added using the `calculate_similarity` function, for use in further downstream analyses.

## 4.2 Evaluation of SNV impacts

An SNV’s impact represent the putative effect that variant may have on the function of the resulting protein, and ranges from HIGH through MODERATE, LOW and MODIFIER, in decreasing order of magnitude. HIGH impact variants may, for example, lead to truncated proteins due to the introduction of a stop codon, while MODIFIER variants have little to no effect on the protein at all. While there is no guarantee that a specific phenotype arises from a HIGH rather than a MODERATE impact variant (for example), it may be informative to look at the impact distribution of the overlapping SNVs between two profiles. This can easily be performed by the `plot_impacts` function:

    impacts <- plot_impacts(hct116_rko)

    ## Warning: `separate_()` was deprecated in tidyr 1.2.0.
    ## Please use `separate()` instead.
    ## This warning is displayed once every 8 hours.
    ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

    impacts

This function takes a comparison dataframe as input and plots the impact distribution of the overlapping variants. It has a number of arguments with defaults, such as if you want to add text with the actual numbers to the plot (`annotate = TRUE` by default), if you want to show the legend (`legend = TRUE` by default) and what colours you want to plot the match-categories with (`palette = c("#0D2D59", "#1954A6")` by default, two shades of blue). We can see that most of the SNVs are present in the MODIFIER impact category, and that there is not a single mismatched HIGH impact SNV. (You can also visualise the impact distribution between your sample and the COSMIC database in exactly the same way.)

You might also want to look at only a subset of variants, e.g. only the variants with HIGH or MODERATE impacts, which is easily achieved with some data manipulation:

    hct116_rko_hm <- hct116_rko[hct116_rko$impact == "HIGH" |
                                hct116_rko$impact == "MODERATE", ]
    nrow(hct116_rko_hm)

    ## [1] 19

## 4.3 Evaluation of specific chromosomes, regions, genes and transcripts

You might be interested in a specific chromosome or a region on a chromosome, and it might be useful to work with data for only that subset. This operation is easily performed on a comparison dataframe:

    hct116_rko_region <- hct116_rko[hct116_rko$chr == 12 &
                                    hct116_rko$pos >= 25000000 &
                                    hct116_rko$pos <= 30000000, ]
    head(hct116_rko_region)

    ##     chr      pos sample_1 sample_2 match      rsID  gene          ENSGID
    ## 247  12 25358650   HCT116      RKO match   rs12245 LYRM5 ENSG00000205707
    ## 248  12 25358828   HCT116      RKO match   rs12587 LYRM5 ENSG00000205707
    ## 249  12 25358943   HCT116      RKO match    rs8720 LYRM5 ENSG00000205707
    ## 250  12 25358969   HCT116      RKO match rs1137196 LYRM5 ENSG00000205707
    ## 251  12 25359328   HCT116      RKO match rs1137189 LYRM5 ENSG00000205707
    ## 252  12 25359352   HCT116      RKO match rs1137188 LYRM5 ENSG00000205707
    ##                                ENSTID REF ALT   impact                  effect
    ## 247 [ENST00000381356,ENST00000557540]   A   T MODIFIER downstream_gene_variant
    ## 248 [ENST00000381356,ENST00000557540]   T   G MODIFIER downstream_gene_variant
    ## 249 [ENST00000381356,ENST00000557540]   T   C MODIFIER downstream_gene_variant
    ## 250 [ENST00000381356,ENST00000557540]   T   G MODIFIER downstream_gene_variant
    ## 251 [ENST00000381356,ENST00000557540]   A   T MODIFIER downstream_gene_variant
    ## 252 [ENST00000381356,ENST00000557540]   G   A MODIFIER downstream_gene_variant
    ##        feature        biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116
    ## 247 transcript protein_coding       351        196        155         A
    ## 248 transcript protein_coding       382        224        158         T
    ## 249 transcript protein_coding       380        223        157         T
    ## 250 transcript protein_coding       306        184        122         T
    ## 251 transcript protein_coding       436        282        154         A
    ## 252 transcript protein_coding       407        242        165         G
    ##     A2.HCT116 FILTER.HCT116 warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO
    ## 247         T          PASS                    414     217     197      A
    ## 248         G          PASS                    422     244     178      T
    ## 249         C          PASS                    420     238     182      T
    ## 250         G          PASS                    349     200     149      T
    ## 251         T          PASS                    508     297     211      A
    ## 252         A          PASS                    507     270     237      G
    ##     A2.RKO FILTER.RKO warnings.RKO
    ## 247      T       PASS
    ## 248      G       PASS
    ## 249      C       PASS
    ## 250      G       PASS
    ## 251      T       PASS
    ## 252      A       PASS

You might also be interested in a specific gene or transcript, of special importance to your study:

    hct116_rko_eps8_t <- hct116_rko[hct116_rko$ENSTID == "ENST00000281172", ]
    hct116_rko_vamp1 <- hct116_rko[hct116_rko$ENSGID == "ENSG00000139190", ]
    hct116_rko_ldhb <- hct116_rko[hct116_rko$gene == "LDHB", ]
    head(hct116_rko_ldhb)

    ##     chr      pos sample_1 sample_2    match      rsID gene          ENSGID
    ## 243  12 21788465   HCT116      RKO mismatch      None LDHB ENSG00000111716
    ## 244  12 21797029   HCT116      RKO    match rs1650294 LDHB ENSG00000111716
    ##                                ENSTID REF ALT   impact
    ## 243 [ENST00000350669,ENST00000542765]   G   T MODIFIER
    ## 244 [ENST00000350669,ENST00000539782]   A   G      LOW
    ##                                            effect            feature
    ## 243 [3_prime_UTR_variant,non_coding_exon_variant]         transcript
    ## 244         [sequence_feature,synonymous_variant] [helix,transcript]
    ##                              biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116
    ## 243 [protein_coding,retained_intron]      1353        754        599         G
    ## 244                   protein_coding      5157          2       5155         G
    ##     A2.HCT116 FILTER.HCT116 warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO
    ## 243         T          PASS                   1347    1347              G
    ## 244         G          PASS                   4253       2    4251      G
    ##     A2.RKO FILTER.RKO                     warnings.RKO
    ## 243      G       PASS
    ## 244      G       PASS WARNING_TRANSCRIPT_NO_STOP_CODON

Here we see two mutations in the LDHB gene, one mismatching MODIFIER variant and one matching LOW variant. This is a good approach to check for known mutations in your dataset. For example, the HCT116 cell line is supposed to have a KRASG13D mutation. We might look for this using its known `rsID` or position:

    hct116_rko_kras <- hct116_rko[hct116_rko$rsID == "rs112445441", ]
    hct116_rko_kras <- hct116_rko[hct116_rko$chr == 12 &
                                  hct116_rko$pos == 25398281, ]
    nrow(hct116_rko_kras)

    ## [1] 0

The reason that we don’t find this particular variant in the HCT116 vs. RKO comparison is that it is not present in the RKO profile, either because it isn’t a mutation in RKO or because there was no confident variant call for that particular position. The `compare_profiles` function only looks at overlapping positions by default, so we will have to look at the individual profiles instead. `seqCAT` has two functions to help with this: `list_variants` and `plot_variant_list`:

The `list_variants` function looks for the genotypes of each specified variant in each provided SNV profile. First, let’s create a small set of interesting variants we want to look closer at:

    known_variants <- data.frame(chr  = c(12, 12, 12, 12),
                                 pos  = c(25358650, 21788465, 21797029, 25398281),
                                 gene = c("LYRM5", "LDHB", "LDHB", "KRAS"),
                                 stringsAsFactors = FALSE)
    known_variants

    ##   chr      pos  gene
    ## 1  12 25358650 LYRM5
    ## 2  12 21788465  LDHB
    ## 3  12 21797029  LDHB
    ## 4  12 25398281  KRAS

The minimum information needed are the `chr` and `pos` columns; any additional columns (such as `gene`, here) will just be passed along for later use. We can now pass this set (along with our SNV profiles) to the `list_variants` function:

    variant_list <- list_variants(list(hct116, rko), known_variants)
    variant_list

    ##   chr      pos  gene HCT116 RKO
    ## 1  12 21788465  LDHB    G/T G/G
    ## 2  12 21797029  LDHB    G/G G/G
    ## 3  12 25358650 LYRM5    A/T A/T
    ## 4  12 25398281  KRAS    C/T   0

While this gives you a nice little list of the genotypes of your specified variants, we can also visualise this using the `plot_variant_list` function. It takes a slightly modified version of the output from the `list_variants` function: it may only contain the genotype columns. We thus need to create row names to identify the variants, like this:

    # Set row names to "chr: pos (gene)"
    row.names(variant_list) <- paste0(variant_list$chr, ":", variant_list$pos,
                                      " (", variant_list$gene, ")")

    # Remove "chr", "pos" and "gene" columns
    to_remove <- c("chr", "pos", "gene")
    variant_list <- variant_list[, !names(variant_list) %in% to_remove]

    # Plot the genotypes in a grid
    genotype_grid <- plot_variant_list(variant_list)

    ## Warning: `gather_()` was deprecated in tidyr 1.2.0.
    ## Please use `gather()` instead.
    ## This warning is displayed once every 8 hours.
    ## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

    ## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
    ## "none")` instead.

    genotype_grid

This gives us an easily overviewed image of what variants are present in which samples, and their precise genotype. We can see that the KRASG13D mutation is indeed present in the HCT116, but not in RKO. We can also see that RKO has a homozygous `G/G` genotype for one of the LDHB variants, while HCT116 is heterozygous (`T/G`) for the same. (Please note that this data was aligned and analysed using the GRCh37 / hg19 assembly and that listed positions might not be accurate for other assemblies.)

# 5 Evaluating multiple comparisons

Many scientific studies compare more than just two datasets, not to mention meta-studies and large-scale comparisons. It is therefore important to be able to characterise and evaluate many-to-one or many-to-many cases as well - the `seqCAT` package provides a number of functions and procedures for doing so.

## 5.1 Performing multiple profile comparisons

The first step of such an analysis is to create and read SNV profiles for each sample that is to be evaluated (please see section 2). The example data used here has three different samples: HCT116, HKE3 and RKO. The `compare_many` function is a helper function for creating either one-to-many or many-to-many SNV profile comparisons, and returns a `list` of the global similarities for all combinations of profiles and their respective data (for downstream analyses):

    # Create list of SNV profiles
    profiles <- list(hct116, hke3, rko)

    # Perform many-to-many comparisons
    many <- compare_many(profiles)
    many[[1]]

    ##   sample_1 sample_2 variants_1 variants_2 overlaps matches concordance
    ## 1   HCT116   HCT116        523        523      523     523       100.0
    ## 2   HCT116     HKE3        493        493      493     475        96.3
    ## 3   HCT116      RKO        259        259      259     181        69.9
    ## 4     HKE3     HKE3       1604       1604     1604    1604       100.0
    ## 5     HKE3      RKO        299        299      299     204        68.2
    ## 6      RKO      RKO        583        583      583     583       100.0
    ##   similarity_score
    ## 1             99.1
    ## 2             95.4
    ## 3             68.7
    ## 4             99.7
    ## 5             67.2
    ## 6             99.2

We can here see the summary statistics of all three combinations of the cell lines in the example data. Notice that `compare_many` will only perform a comparison that has not already been performed, i.e. it will not perform the RKO vs. HCT116 comparison if it has already performed HCT116 vs. RKO. Also notice that it does perform self-comparisons (i.e. HCT116 vs. HCT116), which is useful for downstream visualisations.

The similarities are stored in the first element of the results (`many[[1]]`), while the data for each comparison is stored in the second (`many[[2]]`). The second element is itself also a list, whose indices correspond to the row names of the similarity object. If we, for example, are interested in the HKE3 self-comparison, we can see that its row name is `4`. We can then access its data like this:

    hke3_hke3 <- many[[2]][[4]]
    head(hke3_hke3)

    ##   chr   pos sample_1 sample_2 match        rsID         gene          ENSGID
    ## 1  12 73805     HKE3     HKE3 match rs375835195 RP11-598F7.1 ENSG00000249054
    ## 2  12 75190     HKE3     HKE3 match rs374099059   AC215219.1 ENSG00000238823
    ## 3  12 75308     HKE3     HKE3 match rs370314061   AC215219.1 ENSG00000238823
    ## 4  12 75337     HKE3     HKE3 match rs147539459   AC215219.1 ENSG00000238823
    ## 5  12 76316     HKE3     HKE3 match rs370768066   AC215219.1 ENSG00000238823
    ## 6  12 76349     HKE3     HKE3 match  rs71412503   AC215219.1 ENSG00000238823
    ##            ENSTID REF ALT   impact                  effect    feature biotype
    ## 1 ENST00000504074   G   C MODIFIER downstream_gene_variant transcript lincRNA
    ## 2 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript   miRNA
    ## 3 ENST00000458783   C   T MODIFIER   upstream_gene_variant transcript   miRNA
    ## 4 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript   miRNA
    ## 5 ENST00000458783   T   C MODIFIER   upstream_gene_variant transcript   miRNA
    ## 6 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript   miRNA
    ##   DP.HKE3 AD1.HKE3 AD2.HKE3 A1.HKE3 A2.HKE3 FILTER.HKE3 warnings.HKE3
    ## 1      15        8        7       G       C        PASS
    ## 2      45       39        6       A       G        PASS
    ## 3      38       38                C       C        PASS
    ## 4      38       27       11       A       G        PASS
    ## 5      63       46       17       T       C        PASS
    ## 6      44       35        9       A       G        PASS

You may also specify the `a` and `b` similarity score parameters, as above. If you are interested in only a one-to-many comparison (for cases when you have a “true” baseline profile to compare against), you can do this by also specifying the `one = <profile>` parameter in the function call. This is useful if you have a COSMIC profile to compare against, for example:

    many_cosmic <- compare_many(profiles, one = cosmic)
    many_cosmic[[1]]

    ##        sample_1      sample_2 variants_1 variants_2 overlaps matches
    ## 1 COSMIC.HCT116 COSMIC.HCT116          1          1        1       1
    ## 2 COSMIC.HCT116        HCT116          1          1        1       1
    ## 3 COSMIC.HCT116          HKE3          1          1        1       1
    ## 4 COSMIC.HCT116           RKO          1          1        0       0
    ##   concordance similarity_score
    ## 1         100             28.6
    ## 2         100             28.6
    ## 3         100             28.6
    ## 4         NaN             16.7

It is important to note that performing many comparisons like this may take quite some time, depending on the number of profiles and how much data each profile has. By returning all the data in a list you may then save each comparison to a file, for later re-analysis without having to re-do the comparisons.

## 5.2 Visualising multiple comparisons

A useful and straightforward way of visualising multiple profile comparisons is to use a heatmap. We can use the summary statistics listed in the similarity object from above as input to the function `plot_heatmap`, which gives you a simple overview of all your comparisons:

    heatmap <- plot_heatmap(many[[1]])
    heatmap

Here we see a blue colour gradient for the similarity score of the three cell lines, which are clustered according to their similarity (using `cluster = TRUE`, as default). You may change the size of the text annotations using `annotation_size = 5` (default) or suppress them entirely (`annotate = FALSE`). You may also suppress the legend (`legend = FALSE`), change the main colour of the gradient (`colour = "#1954A6"` by default) or change the limits of the gradient (`limits = c(0, 50, 90, 100)` by default). The choice of gradient limits are based on clarity (comparisons with a similarity score less than 50, i.e. those that likely have too few overlapping variants to begin with, are suppressed) and the previously mentioned 90 % concordance threshold (Yu et al. 2015).

This heatmap makes it clear that HCT116 and HKE3 are, indeed, very similar to each other, while RKO differs from them both. These types of heatmaps can be created for an arbitrary number of samples, which will then give a great overview of the global similarities of all the samples studied. This can be used to evaluate the quality of the datasets (e.g. to see which comparisons have very few overlaps), find similarity clusters and potential unexpected outliers. If a sample stands out in a heatmap such as this, that is grounds for further investigation, using both the methods described above and more classical evaluations of sequencing data (read quality, adapter contamination, alignments, variant calling, and so on).

# Citation

If you are using seqCAT to analyse your data, please cite the following article:

> seqCAT: a Bioconductor R-package for variant analysis of high throughput sequencing data
> Fasterius E. and Al-Khalili Szigyarto C.
> F1000Research (2018), 7:1466
> https://f1000research.com/articles/7-1466

# Session info

    ## R version 4.2.0 RC (2022-04-19 r82224)
    ## Platform: x86_64-apple-darwin17.0 (64-bit)
    ## Running under: macOS Mojave 10.14.6
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] seqCAT_1.18.0               VariantAnnotation_1.42.0
    ##  [3] Rsamtools_2.12.0            Biostrings_2.64.0
    ##  [5] XVector_0.36.0              SummarizedExperiment_1.26.0
    ##  [7] Biobase_2.56.0              MatrixGenerics_1.8.0
    ##  [9] matrixStats_0.62.0          GenomicRanges_1.48.0
    ## [11] GenomeInfoDb_1.32.0         IRanges_2.30.0
    ## [13] S4Vectors_0.34.0            BiocGenerics_0.42.0
    ## [15] BiocStyle_2.24.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] bitops_1.0-7             bit64_4.0.5              filelock_1.0.2
    ##  [4] progress_1.2.2           httr_1.4.2               tools_4.2.0
    ##  [7] bslib_0.3.1              utf8_1.2.2               R6_2.5.1
    ## [10] colorspace_2.0-3         DBI_1.1.2                tidyselect_1.1.2
    ## [13] prettyunits_1.1.1        bit_4.0.4                curl_4.3.2
    ## [16] compiler_4.2.0           cli_3.3.0                xml2_1.3.3
    ## [19] DelayedArray_0.22.0      labeling_0.4.2           rtracklayer_1.56.0
    ## [22] bookdown_0.26            sass_0.4.1               scales_1.2.0
    ## [25] rappdirs_0.3.3           stringr_1.4.0            digest_0.6.29
    ## [28] rmarkdown_2.14           pkgconfig_2.0.3          htmltools_0.5.2
    ## [31] highr_0.9                dbplyr_2.1.1             fastmap_1.1.0
    ## [34] BSgenome_1.64.0          rlang_1.0.2              RSQLite_2.2.12
    ## [37] farver_2.1.0             jquerylib_0.1.4          BiocIO_1.6.0
    ## [40] generics_0.1.2           jsonlite_1.8.0           BiocParallel_1.30.0
    ## [43] dplyr_1.0.8              RCurl_1.98-1.6           magrittr_2.0.3
    ## [46] GenomeInfoDbData_1.2.8   Matrix_1.4-1             munsell_0.5.0
    ## [49] Rcpp_1.0.8.3             fansi_1.0.3              lifecycle_1.0.1
    ## [52] stringi_1.7.6            yaml_2.3.5               zlibbioc_1.42.0
    ## [55] BiocFileCache_2.4.0      grid_4.2.0               blob_1.2.3
    ## [58] parallel_4.2.0           crayon_1.5.1             lattice_0.20-45
    ## [61] GenomicFeatures_1.48.0   hms_1.1.1                KEGGREST_1.36.0
    ## [64] magick_2.7.3             knitr_1.38               pillar_1.7.0
    ## [67] rjson_0.2.21             biomaRt_2.52.0           XML_3.99-0.9
    ## [70] glue_1.6.2               evaluate_0.15            BiocManager_1.30.17
    ## [73] png_0.1-7                vctrs_0.4.1              gtable_0.3.0
    ## [76] purrr_0.3.4              tidyr_1.2.0              assertthat_0.2.1
    ## [79] cachem_1.0.6             ggplot2_3.3.5            xfun_0.30
    ## [82] restfulr_0.0.13          tibble_3.1.6             GenomicAlignments_1.32.0
    ## [85] AnnotationDbi_1.58.0     memoise_2.0.1            ellipsis_0.3.2

# References

Fasterius, Erik, Cinzia Raso, Susan Kennedy, Nora Rauch, Pär Lundin, Walter Kolch, Mathias Uhlén, and Cristina Al-Khalili Szigyarto. 2017. “A novel RNA sequencing data analysis method for cell line authentication.” PloS One 12 (2): e0171435.

Fasterius, Erik, and Cristina Al-Khalili Szigyarto. 2018. “Analysis of public RNA- sequencing data reveals biological consequences of genetic heterogeneity in cell line populations.” Scientific Reports 8(1) (1): 1–11. http://dx.doi.org/10.1038/s41598-018-29506-3 papers3://publication/doi/10.1038/s41598-018-29506-3.

Forbes, Simon A, David Beare, Prasad Gunasekaran, Kenric Leung, Nidhi Bindal, Harry Boutselakis, Minjie Ding, et al. 2015. “COSMIC: exploring the world’s knowledge of somatic mutations in human cancer.” Nucleic Acids Research 43 (Database issue): D805–11. http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed{\&}id=25355519{\&}retmode=ref{\&}cmd=prlinks papers3://publication/doi/10.1093/nar/gku1075.

Yu, Mamie, Suresh K Selvaraj, May M Y Liang-Chu, Sahar Aghajani, Matthew Busse, Jean Yuan, Genee Lee, et al. 2015. “A resource for cell line authentication, annotation and quality control.” Nature 520 (7547): 307–11.