# seqCAT: The High Throughput Sequencing Cell Authentication Toolkit

Erik Fasterius

#### 2019-08-14

#### Package

seqCAT 1.6.3

# 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).

seqCAT 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. seqCAT’s 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 annotated with software such as SnpEff.

## 1.1 Installation

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

    if (!requireNamespace("BiocManager", quietly=TRUE))
        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 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. In order to decrease the computation time for large comparison sets and to facilitate re-analyses with different parameters each SNV profile is saved on the harddrive as a normal `.txt` file. 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.

The creation of a SNV profile includes filtering of low-confidence variants and removal of variants below a sequencing depth threshold (`10` 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. Creation of annotated SNV profiles is also implemented in Python (section 2.2), which is faster than the standard implementation in R (section 2.1).

## 2.1 Create profiles with R

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.

    # Load the package
    library("seqCAT")

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

    # Create two SNV profiles
    create_profile(vcf, "HCT116", "hct116.profile.txt")
    create_profile(vcf, "RKO", "rko.profile.txt", min_depth = 15)

This creates SNV profiles for the two samples found in the example data (`HCT116` and `RKO`) and saves them as `hct116.profile.txt` and `rko.profile.txt` in the current directory, respectively. The profile of the second sample was created with a non-standard filter for sequencing 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 below the variant caller-specific filtering criteria by passing `filter = 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.

## 2.2 Create profiles faster with Python

Annotated SNV profiles can also be created with Python, another scripting language, if you have installed it. You will also need to install the PyVCF module, in order for it to run. The `Python` version can create SNV profiles approximately five to ten times quicker than its `R` equivalent for annotated VCF files. This is not important for most users, but is nevertheless included for cases with many annotated VCF files where extra speed is desirable.

    create_profile(vcf, "RKO", "RKO.profile.txt", python = 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. 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
    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. COSMIC has over a thousand cell line-specific mutational profiles, 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)

    ## GRanges object with 1 range and 39 metadata columns:
    ##      seqnames    ranges strand |        gene          ENSTID gene_cds_length
    ##         <Rle> <IRanges>  <Rle> | <character>     <character>       <integer>
    ##   43       12  25398281      * |        KRAS ENST00000311936             567
    ##        hgnc_id        sample id_sample id_tumour    primary_site
    ##      <integer>   <character> <integer> <integer>     <character>
    ##   43      6407 COSMIC.HCT116    905936    823462 large_intestine
    ##      site_subtype_1 site_subtype_2 site_subtype_3 primary_histology
    ##         <character>    <character>    <character>       <character>
    ##   43          colon             NS             NS         carcinoma
    ##      histology_subtype_1 histology_subtype_2 histology_subtype_3
    ##              <character>         <character>         <character>
    ##   43                  NS                  NS                  NS
    ##      genome_wide_screen          id         cds          aa
    ##             <character> <character> <character> <character>
    ##   43                  y     COSM532     c.38G>A      p.G13D
    ##                  description         loh      grch         snp
    ##                  <character> <character> <integer> <character>
    ##   43 Substitution - Missense           u        37           n
    ##      fathmm_prediction fathmm_score
    ##            <character>    <numeric>
    ##   43        PATHOGENIC      0.97875
    ##                                    somatic_status verification_status
    ##                                       <character>         <character>
    ##   43 Reported in another cancer sample as somatic            Verified
    ##      pubmed_pmid  id_study                          institute
    ##        <logical> <integer>                        <character>
    ##   43        <NA>       619 Developmental Therapeutics Program
    ##                                 institute_address catalogue_number
    ##                                       <character>      <character>
    ##   43 National Cancer Institute,Frederick,MD 21701
    ##      sample_source tumour_origin       age         REF         ALT
    ##        <character>   <character> <numeric> <character> <character>
    ##   43     cell-line       primary      <NA>           C           T
    ##               A1          A2
    ##      <character> <character>
    ##   43           C           T
    ##   -------
    ##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

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:

    length(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.

# 3 Comparing SNV profiles

## 3.1 Comparing full profiles

Once each relevant sample has its own SNV profile the comparisons can be performed. First, each profile is read using the `read_profile` function, which outputs `GRanges` objects for fast and efficient comparisons.

    hct116 <- read_profile("hct116.profile.txt", "HCT116")
    rko <- read_profile("rko.profile.txt", "RKO")
    head(hct116)

    ## GRanges object with 6 ranges and 17 metadata columns:
    ##     seqnames    ranges strand |        rsID               gene
    ##        <Rle> <IRanges>  <Rle> | <character>        <character>
    ##   1       12     80385      * | rs370087224 ABC7-42389800N19.1
    ##   2       12     80399      * |        None ABC7-42389800N19.1
    ##   3       12     80422      * | rs373297723 ABC7-42389800N19.1
    ##   4       12     80729      * | rs375960073 ABC7-42389800N19.1
    ##   5       12     83011      * | rs370570891 ABC7-42389800N19.1
    ##   6       12     83012      * | rs374646339 ABC7-42389800N19.1
    ##              ENSGID          ENSTID         REF         ALT      impact
    ##         <character>     <character> <character> <character> <character>
    ##   1 ENSG00000226210 ENST00000400706           C           T    MODIFIER
    ##   2 ENSG00000226210 ENST00000400706           G           A    MODIFIER
    ##   3 ENSG00000226210 ENST00000400706           G           A    MODIFIER
    ##   4 ENSG00000226210 ENST00000400706           A           G    MODIFIER
    ##   5 ENSG00000226210 ENST00000400706           T           C    MODIFIER
    ##   6 ENSG00000226210 ENST00000400706           C           G    MODIFIER
    ##             effect     feature                biotype        DP       AD1
    ##        <character> <character>            <character> <integer> <integer>
    ##   1 intron_variant  transcript unprocessed_pseudogene        10         8
    ##   2 intron_variant  transcript unprocessed_pseudogene        10         4
    ##   3 intron_variant  transcript unprocessed_pseudogene        15        11
    ##   4 intron_variant  transcript unprocessed_pseudogene        18        13
    ##   5 intron_variant  transcript unprocessed_pseudogene        10         3
    ##   6 intron_variant  transcript unprocessed_pseudogene        10         3
    ##           AD2          A1          A2    warnings      sample
    ##     <integer> <character> <character> <character> <character>
    ##   1         2           C           T                  HCT116
    ##   2         6           G           A                  HCT116
    ##   3         4           G           A                  HCT116
    ##   4         5           A           G                  HCT116
    ##   5         7           T           C                  HCT116
    ##   6         7           C           G                  HCT116
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

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 read, 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, as similarity calculations between profiles where some variants only have confident calls in one of the samples are 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
    ##   warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO warnings.RKO
    ## 1                     15       8       7      A      G
    ## 2                     17       6      11      T      G
    ## 3                     16       4      12      G      T
    ## 4                     23      14       9      T      C
    ## 5                     18       9       9      C      G
    ## 6                     18      10       8      C      G

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
    ##   warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO warnings.RKO
    ## 1
    ## 2
    ## 3
    ## 4                     16      11       5      C      G
    ## 5                     15       8       7      A      G
    ## 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 warnings.HCT116 gene_cds_length.COSMIC.HCT116
    ## 1         C         T                                           567
    ##   hgnc_id.COSMIC.HCT116 id_sample.COSMIC.HCT116 id_tumour.COSMIC.HCT116
    ## 1                  6407                  905936                  823462
    ##   primary_site.COSMIC.HCT116 site_subtype_1.COSMIC.HCT116
    ## 1            large_intestine                        colon
    ##   site_subtype_2.COSMIC.HCT116 site_subtype_3.COSMIC.HCT116
    ## 1                           NS                           NS
    ##   primary_histology.COSMIC.HCT116 histology_subtype_1.COSMIC.HCT116
    ## 1                       carcinoma                                NS
    ##   histology_subtype_2.COSMIC.HCT116 histology_subtype_3.COSMIC.HCT116
    ## 1                                NS                                NS
    ##   genome_wide_screen.COSMIC.HCT116 id.COSMIC.HCT116 cds.COSMIC.HCT116
    ## 1                                y          COSM532           c.38G>A
    ##   aa.COSMIC.HCT116 description.COSMIC.HCT116 loh.COSMIC.HCT116
    ## 1           p.G13D   Substitution - Missense                 u
    ##   grch.COSMIC.HCT116 snp.COSMIC.HCT116 fathmm_prediction.COSMIC.HCT116
    ## 1                 37                 n                      PATHOGENIC
    ##   fathmm_score.COSMIC.HCT116                 somatic_status.COSMIC.HCT116
    ## 1                    0.97875 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
    ## 1 National Cancer Institute,Frederick,MD 21701
    ##   catalogue_number.COSMIC.HCT116 sample_source.COSMIC.HCT116
    ## 1                                                  cell-line
    ##   tumour_origin.COSMIC.HCT116 age.COSMIC.HCT116 A1.COSMIC.HCT116
    ## 1                     primary                                  C
    ##   A2.COSMIC.HCT116
    ## 1                T

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
    create_profile(vcf, "HKE3", "hke3.profile.txt")
    hke3 <- read_profile("hke3.profile.txt", "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, a = 1, 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)
    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    feature
    ## 247 ENST00000381356   A   T MODIFIER downstream_gene_variant transcript
    ## 248 ENST00000381356   T   G MODIFIER downstream_gene_variant transcript
    ## 249 ENST00000381356   T   C MODIFIER downstream_gene_variant transcript
    ## 250 ENST00000381356   T   G MODIFIER downstream_gene_variant transcript
    ## 251 ENST00000381356   A   T MODIFIER downstream_gene_variant transcript
    ## 252 ENST00000381356   G   A MODIFIER downstream_gene_variant transcript
    ##            biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116 A2.HCT116
    ## 247 protein_coding       351        196        155         A         T
    ## 248 protein_coding       382        224        158         T         G
    ## 249 protein_coding       380        223        157         T         C
    ## 250 protein_coding       306        184        122         T         G
    ## 251 protein_coding       436        282        154         A         T
    ## 252 protein_coding       407        242        165         G         A
    ##     warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO warnings.RKO
    ## 247                    414     217     197      A      T
    ## 248                    422     244     178      T      G
    ## 249                    420     238     182      T      C
    ## 250                    349     200     149      T      G
    ## 251                    508     297     211      A      T
    ## 252                    507     270     237      G      A

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              effect    feature
    ## 243 ENST00000350669   G   T MODIFIER 3_prime_UTR_variant transcript
    ## 244 ENST00000350669   A   G      LOW    sequence_feature      helix
    ##            biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116 A2.HCT116
    ## 243 protein_coding      1353        754        599         G         T
    ## 244 protein_coding      5157          2       5155         G         G
    ##     warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO A2.RKO warnings.RKO
    ## 243                   1347    1347              G      G
    ## 244                   4253       2    4251      G      G

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, 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)
    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
    ## 1 ENST00000504074   G   C MODIFIER downstream_gene_variant transcript
    ## 2 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript
    ## 3 ENST00000458783   C   T MODIFIER   upstream_gene_variant transcript
    ## 4 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript
    ## 5 ENST00000458783   T   C MODIFIER   upstream_gene_variant transcript
    ## 6 ENST00000458783   A   G MODIFIER   upstream_gene_variant transcript
    ##   biotype DP.HKE3 AD1.HKE3 AD2.HKE3 A1.HKE3 A2.HKE3 warnings.HKE3
    ## 1 lincRNA      15        8        7       G       C
    ## 2   miRNA      45       39        6       A       G
    ## 3   miRNA      38       38                C       C
    ## 4   miRNA      38       27       11       A       G
    ## 5   miRNA      63       46       17       T       C
    ## 6   miRNA      44       35        9       A       G

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 Reading multiple profiles

The `read_profiles` function is a convenience-wrapper for `read_profile`, which will automatically read all the profiles present in a given directory and return them as a list, which is then easily parsed to the `compare_many` function.

    profile_list <- read_profiles("./")
    head(profile_list[[1]])

    ## GRanges object with 6 ranges and 17 metadata columns:
    ##     seqnames    ranges strand |        rsID               gene
    ##        <Rle> <IRanges>  <Rle> | <character>        <character>
    ##   1       12     80385      * | rs370087224 ABC7-42389800N19.1
    ##   2       12     80399      * |        None ABC7-42389800N19.1
    ##   3       12     80422      * | rs373297723 ABC7-42389800N19.1
    ##   4       12     80729      * | rs375960073 ABC7-42389800N19.1
    ##   5       12     83011      * | rs370570891 ABC7-42389800N19.1
    ##   6       12     83012      * | rs374646339 ABC7-42389800N19.1
    ##              ENSGID          ENSTID         REF         ALT      impact
    ##         <character>     <character> <character> <character> <character>
    ##   1 ENSG00000226210 ENST00000400706           C           T    MODIFIER
    ##   2 ENSG00000226210 ENST00000400706           G           A    MODIFIER
    ##   3 ENSG00000226210 ENST00000400706           G           A    MODIFIER
    ##   4 ENSG00000226210 ENST00000400706           A           G    MODIFIER
    ##   5 ENSG00000226210 ENST00000400706           T           C    MODIFIER
    ##   6 ENSG00000226210 ENST00000400706           C           G    MODIFIER
    ##             effect     feature                biotype        DP       AD1
    ##        <character> <character>            <character> <integer> <integer>
    ##   1 intron_variant  transcript unprocessed_pseudogene        10         8
    ##   2 intron_variant  transcript unprocessed_pseudogene        10         4
    ##   3 intron_variant  transcript unprocessed_pseudogene        15        11
    ##   4 intron_variant  transcript unprocessed_pseudogene        18        13
    ##   5 intron_variant  transcript unprocessed_pseudogene        10         3
    ##   6 intron_variant  transcript unprocessed_pseudogene        10         3
    ##           AD2          A1          A2    warnings      sample
    ##     <integer> <character> <character> <character> <character>
    ##   1         2           C           T                  hct116
    ##   2         6           G           A                  hct116
    ##   3         4           G           A                  hct116
    ##   4         5           A           G                  hct116
    ##   5         7           T           C                  hct116
    ##   6         7           C           G                  hct116
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

## 5.3 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 3.6.1 (2019-07-05)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 18.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.9-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] seqCAT_1.6.3                VariantAnnotation_1.30.1
    ##  [3] Rsamtools_2.0.0             Biostrings_2.52.0
    ##  [5] XVector_0.24.0              SummarizedExperiment_1.14.1
    ##  [7] DelayedArray_0.10.0         BiocParallel_1.18.1
    ##  [9] matrixStats_0.54.0          Biobase_2.44.0
    ## [11] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0
    ## [13] IRanges_2.18.1              S4Vectors_0.22.0
    ## [15] BiocGenerics_0.30.0         BiocStyle_2.12.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] httr_1.4.1               tidyr_0.8.3
    ##  [3] bit64_0.9-7              assertthat_0.2.1
    ##  [5] BiocManager_1.30.4       blob_1.2.0
    ##  [7] BSgenome_1.52.0          GenomeInfoDbData_1.2.1
    ##  [9] yaml_2.2.0               progress_1.2.2
    ## [11] pillar_1.4.2             RSQLite_2.1.2
    ## [13] backports_1.1.4          lattice_0.20-38
    ## [15] glue_1.3.1               digest_0.6.20
    ## [17] colorspace_1.4-1         htmltools_0.3.6
    ## [19] Matrix_1.2-17            XML_3.98-1.20
    ## [21] pkgconfig_2.0.2          biomaRt_2.40.3
    ## [23] bookdown_0.12            zlibbioc_1.30.0
    ## [25] purrr_0.3.2              scales_1.0.0
    ## [27] tibble_2.1.3             ggplot2_3.2.1
    ## [29] GenomicFeatures_1.36.4   lazyeval_0.2.2
    ## [31] magrittr_1.5             crayon_1.3.4
    ## [33] memoise_1.1.0            evaluate_0.14
    ## [35] tools_3.6.1              prettyunits_1.0.2
    ## [37] hms_0.5.0                stringr_1.4.0
    ## [39] munsell_0.5.0            AnnotationDbi_1.46.0
    ## [41] compiler_3.6.1           rlang_0.4.0
    ## [43] grid_3.6.1               RCurl_1.95-4.12
    ## [45] labeling_0.3             bitops_1.0-6
    ## [47] rmarkdown_1.14           gtable_0.3.0
    ## [49] DBI_1.0.0                R6_2.4.0
    ## [51] GenomicAlignments_1.20.1 knitr_1.24
    ## [53] dplyr_0.8.3              rtracklayer_1.44.2
    ## [55] bit_1.1-14               zeallot_0.1.0
    ## [57] stringi_1.4.3            Rcpp_1.0.2
    ## [59] vctrs_0.2.0              tidyselect_0.2.5
    ## [61] xfun_0.8

# 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.

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.