seqCAT: The High Throughput Sequencing Cell Authentication Toolkit
2020-10-27
Package
seqCAT 1.12.0
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 comparisonsEach 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 HCT116The 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] TRUEAll 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 NAYou 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] 1Here 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 sample13 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
## 6The 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
## 63.2 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 primaryYou 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 68.7Here, 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 94.4Notice 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] 194.3 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$pos <= 30000000, ]
head(hct116_rko_region)## chr pos sample_1 sample_2 match rsID gene ENSGID
## 247 12 25358650 HCT116 RKO match rs12245 LYRM5 ENSG00000205707
## 248 12 25358828 HCT116 RKO match rs12587 LYRM5 ENSG00000205707
## 249 12 25358943 HCT116 RKO match rs8720 LYRM5 ENSG00000205707
## 250 12 25358969 HCT116 RKO match rs1137196 LYRM5 ENSG00000205707
## 251 12 25359328 HCT116 RKO match rs1137189 LYRM5 ENSG00000205707
## 252 12 25359352 HCT116 RKO match rs1137188 LYRM5 ENSG00000205707
## ENSTID REF ALT impact effect
## 247 [ENST00000381356,ENST00000557540] A T MODIFIER downstream_gene_variant
## 248 [ENST00000381356,ENST00000557540] T G MODIFIER downstream_gene_variant
## 249 [ENST00000381356,ENST00000557540] T C MODIFIER downstream_gene_variant
## 250 [ENST00000381356,ENST00000557540] T G MODIFIER downstream_gene_variant
## 251 [ENST00000381356,ENST00000557540] A T MODIFIER downstream_gene_variant
## 252 [ENST00000381356,ENST00000557540] G A MODIFIER downstream_gene_variant
## feature biotype DP.HCT116 AD1.HCT116 AD2.HCT116 A1.HCT116
## 247 transcript protein_coding 351 196 155 A
## 248 transcript protein_coding 382 224 158 T
## 249 transcript protein_coding 380 223 157 T
## 250 transcript protein_coding 306 184 122 T
## 251 transcript protein_coding 436 282 154 A
## 252 transcript protein_coding 407 242 165 G
## A2.HCT116 FILTER.HCT116 warnings.HCT116 DP.RKO AD1.RKO AD2.RKO A1.RKO
## 247 T PASS 414 217 197 A
## 248 G PASS 422 244 178 T
## 249 C PASS 420 238 182 T
## 250 G PASS 349 200 149 T
## 251 T PASS 508 297 211 A
## 252 A PASS 507 270 237 G
## A2.RKO FILTER.RKO warnings.RKO
## 247 T PASS
## 248 G PASS
## 249 C PASS
## 250 G PASS
## 251 T PASS
## 252 A PASSYou might also be interested in a specific gene or transcript, of special importance to your study:
hct116_rko_eps8_t <- hct116_rko[hct116_rko$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_CODONHere 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$pos == 25398281, ]
nrow(hct116_rko_kras)## [1] 0The reason that we don’t find this particular variant in the HCT116 vs. RKO
comparison is that it is not present in the RKO profile, either because it
isn’t a mutation in RKO or because there was no confident variant call for that
particular position. The compare_profiles function only looks at overlapping
positions by default, so we will have to look at the individual profiles
instead. seqCAT has two functions to help with this: list_variants and
plot_variant_list:
The list_variants function looks for the genotypes of each specified variant
in each provided SNV profile. First, let’s create a small set of interesting
variants we want to look closer at:
known_variants <- data.frame(chr = c(12, 12, 12, 12),
pos = c(25358650, 21788465, 21797029, 25398281),
gene = c("LYRM5", "LDHB", "LDHB", "KRAS"),
stringsAsFactors = FALSE)
known_variants## chr pos gene
## 1 12 25358650 LYRM5
## 2 12 21788465 LDHB
## 3 12 21797029 LDHB
## 4 12 25398281 KRASThe minimum information needed are the chr and pos columns; any additional
columns (such as gene, here) will just be passed along for later use. We can
now pass this set (along with our SNV profiles) to the list_variants
function:
variant_list <- list_variants(list(hct116, rko), known_variants)
variant_list## chr pos gene HCT116 RKO
## 1 12 21788465 LDHB G/T G/G
## 2 12 21797029 LDHB G/G G/G
## 3 12 25358650 LYRM5 A/T A/T
## 4 12 25398281 KRAS C/T 0While this gives you a nice little list of the genotypes of your specified
variants, we can also visualise this using the plot_variant_list function. It
takes a slightly modified version of the output from the list_variants
function: it may only contain the genotype columns. We thus need to create row
names to identify the variants, like this:
# Set row names to "chr: pos (gene)"
row.names(variant_list) <- paste0(variant_list$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 99.2We 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 PASSYou 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 16.7It 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 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
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##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] seqCAT_1.12.0 VariantAnnotation_1.36.0
## [3] Rsamtools_2.6.0 Biostrings_2.58.0
## [5] XVector_0.30.0 SummarizedExperiment_1.20.0
## [7] Biobase_2.50.0 MatrixGenerics_1.2.0
## [9] matrixStats_0.57.0 GenomicRanges_1.42.0
## [11] GenomeInfoDb_1.26.0 IRanges_2.24.0
## [13] S4Vectors_0.28.0 BiocGenerics_0.36.0
## [15] BiocStyle_2.18.0
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.2 tidyr_1.1.2 bit64_4.0.5
## [4] assertthat_0.2.1 askpass_1.1 BiocManager_1.30.10
## [7] BiocFileCache_1.14.0 blob_1.2.1 BSgenome_1.58.0
## [10] GenomeInfoDbData_1.2.4 yaml_2.2.1 progress_1.2.2
## [13] pillar_1.4.6 RSQLite_2.2.1 lattice_0.20-41
## [16] glue_1.4.2 digest_0.6.27 colorspace_1.4-1
## [19] htmltools_0.5.0 Matrix_1.2-18 XML_3.99-0.5
## [22] pkgconfig_2.0.3 biomaRt_2.46.0 magick_2.5.0
## [25] bookdown_0.21 zlibbioc_1.36.0 purrr_0.3.4
## [28] scales_1.1.1 BiocParallel_1.24.0 tibble_3.0.4
## [31] openssl_1.4.3 farver_2.0.3 ggplot2_3.3.2
## [34] generics_0.0.2 ellipsis_0.3.1 GenomicFeatures_1.42.0
## [37] magrittr_1.5 crayon_1.3.4 memoise_1.1.0
## [40] evaluate_0.14 xml2_1.3.2 tools_4.0.3
## [43] prettyunits_1.1.1 hms_0.5.3 lifecycle_0.2.0
## [46] stringr_1.4.0 munsell_0.5.0 DelayedArray_0.16.0
## [49] AnnotationDbi_1.52.0 compiler_4.0.3 rlang_0.4.8
## [52] grid_4.0.3 RCurl_1.98-1.2 rappdirs_0.3.1
## [55] labeling_0.4.2 bitops_1.0-6 rmarkdown_2.5
## [58] gtable_0.3.0 DBI_1.1.0 curl_4.3
## [61] R6_2.4.1 GenomicAlignments_1.26.0 knitr_1.30
## [64] dplyr_1.0.2 rtracklayer_1.50.0 bit_4.0.4
## [67] stringi_1.5.3 Rcpp_1.0.5 vctrs_0.3.4
## [70] dbplyr_1.4.4 tidyselect_1.1.0 xfun_0.18References
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). Nature Publishing Group: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.