XCIR implements statistical models designed for the analysis of the X-chromosome. Specifically, it provides mixture-models for the estimation of the fraction of inactivation of each parental X (skewing or mosaicism) and tests to identify X-linked genes escape the inactivation mechanism.
In this vignette, we present a typical pipeline from allele-specific RNA-Seq counts to subject level estimates of mosaicism and XCI-states for all X-linked genes.
XCIR's pipeline for calling XCI-states at the subject usually involve
readVCF4, readRNASNPs)annnotateX, addAnno)getGenicDP)betaBinomXI)
a. Skewing estimate
b. XCI-callsInternally, XCIR uses data.table for efficient computation and most functions
in the package will return a data.table object. Naturally, data.frame are
accepted as inputs and the conversion of the outputs is trivial.
library(XCIR)
library(data.table)
We load a small dataset of two samples to display the requirements and highlight some of the pre-processing functions.
vcff <- system.file("extdata/AD_example.vcf", package = "XCIR")
vcf <- readVCF4(vcff)
head(vcf)
## CHROM POS REF ALT sample AD_hap1 AD_hap2
## 1: X 167755 G T samp1 0 0
## 2: X 167755 G T samp2 0 0
## 3: X 167824 T C samp1 0 0
## 4: X 167824 T C samp2 0 0
## 5: X 167885 A T samp1 0 0
## 6: X 167885 A T samp2 0 0
This dataset contains the minimum information required to go through the
XCIR workflow.
The function readVCF4 is only provided to help extract essential information
but the data can be loaded through other means as long as allele specific
expression is present and both the SNP and sample are clearly identified.
REF & ALT columns are naturally present in all vcf files
but can be safely omitted for further processing.
In order to obtain allele specific expression for the X-linked genes, we first
need to map SNPs to genes and ensure that they are heterozygous. We provide
a function to map SNPs to genes using infromation extracted from ensembl through
biomaRt.
annoX <- annotateX(vcf)
head(annoX)
## ID GENE GENE_pos CHROM POS REF ALT sample AD_hap1 AD_hap2
## 1: X:10112338 WWC3 10048060 X 10112338 C A samp1 17 43
## 2: X:10112338 WWC3 10048060 X 10112338 C A samp2 22 64
## 3: X:11785924 MSL3 11785074 X 11785924 A G samp1 18 22
## 4: X:11786839 MSL3 11785074 X 11786839 T C samp2 14 16
## 5: X:12903659 TLR7 12896850 X 12903659 A T samp1 10 38
## 6: X:12906030 TLR7 12896850 X 12906030 G A samp2 47 21
This adds a GENE column to the dataset and removes SNPs with a lower totak read count or a read count that is too small on one of the alleles (homozygous SNPs).
In some cases, the data may already be filtered for heterozygous SNPs (e.g: If genotyping information is also available for the sample). In this case, the minimum read threshold for both alleles can be lowered or removed.
annoXgeno <- annotateX(vcf, het_cutoff = 0)
By default, annotateX aligns to hg19. Other versions can be passed through
the release argument.
Finally, another option for annotations is through the seqminer package. For
more information see addAnno's man page and annotatePlain in the seqminer
manual.
Again, this is provided for convenience but annotations mapping to genes through other means is perfectly valid as long as a new GENE column is added to the table.
Now that we have annotated SNPs, we can summarize the counts for each gene to make independant calls.
When high quality phasing information is available, SNPs are reliably assigned to the correct haplotype and the allele specific counts of all SNPs within a gene can be summed to get a better estimate of the fraction of each parental cell (mosaicism).
genic <- getGenicDP(annoX, highest_expr = TRUE)
head(genic)
## ID GENE GENE_pos CHROM POS REF ALT sample AD_hap1 AD_hap2
## 1: X:10112338 WWC3 10048060 X 10112338 C A samp1 17 43
## 2: X:10112338 WWC3 10048060 X 10112338 C A samp2 22 64
## 3: X:11785924 MSL3 11785074 X 11785924 A G samp1 18 22
## 4: X:11786839 MSL3 11785074 X 11786839 T C samp2 14 16
## 5: X:12903659 TLR7 12896850 X 12903659 A T samp1 10 38
## 6: X:12906030 TLR7 12896850 X 12906030 G A samp2 47 21
## gender n_snps tot
## 1: female 1 60
## 2: female 1 86
## 3: female 1 40
## 4: female 1 30
## 5: female 1 48
## 6: female 1 68
When this isn't the case, we can only safely use one SNP. Therefore, we limit our data to the most highly expressed SNP in each gene.
genic <- getGenicDP(annoX, highest_expr = FALSE)
For this section, we load a simulated example inlcuded with the package. Read counts for allele specific expression (ASE) at heterozygous SNP and skewing based on pre-determined mode and overdispersion parameters are simulated. The list of training genes is included.
data <- fread(system.file("extdata/data34_vignette.tsv", package = "XCIR"))
xcig <- readLines(system.file("extdata/xcig_vignette.txt", package = "XCIR"))
head(data)
## sample gender CHROM GENE true_status AD_hap1 AD_hap2 tot
## 1: sample30 female X gene_test_1 test_subject 32 281 313
## 2: sample31 female X gene_test_1 test_subject 53 260 313
## 3: sample32 female X gene_test_1 test_subject 77 236 313
## 4: sample33 female X gene_test_1 test_subject 79 234 313
## 5: sample34 female X gene_test_1 test_subject 87 226 313
## 6: sample35 female X gene_test_1 test_subject 66 247 313
The data presented here contains the minimum necessary information to start imediately at the modelling step.
Raw data can be read from a VCF file using readVCF4. The only requirements
being that the allelic depth (AD) field should be recorded in the VCF.
The main function of the package betaBinomXI allows to fit a simple beta-binomial
distribution to the expression of genes in the training set.
bb <- betaBinomXI(data, xciGenes = xcig, model = "BB")
This will estimate the skewing for each individual as well as test for XCI-escape for each gene in each sample.
In this example, the training set contains artificial sequencing errors in some of the samples, such that a simple beta-binomial may not be the best choice to fit the training data.
The plotQC function plots the estimated skewing along with the observed
allele specific expression fraction in the training genes.
It can be used to spot outliers (such as sequencing errors or escape genes in the training set) to use a better fitting model.
plotQC(bb[sample == "sample36"], xcig = xcig)
s36 <- data[sample == "sample36"]
For example, looking at the QC plot for the sample above, we observe that a few training genes have a suspiciously low \(f_g\) (i.e: very highly skewed). This is often due to sequencing error making an homozygous SNP appear as heterozygous.
Here, we let the AIC based model selection procedure select the best fitting model for that sample.
s36fit <- betaBinomXI(s36, model = "AUTO", xciGenes = xcig, plot = TRUE)
The function correctly identified the outliers and selected the “MM” model, which is a mixture model with a Beta-binomial component for the true heterozygous SNPs and a binomial mixture to fit the sequencing errors.
This can naturally be applied to the full dataset for subject specific model selection.
auto <- betaBinomXI(data, xciGenes = xcig, model = "AUTO")
The returned table contains a skewing estimate f for every subject and a p-value for XCI-escape test for each sample/gene combination.
It is then trivial to annotate the XCI status for each gene based on the selected significance threshold.
auto[, status := ifelse(p_value < 0.05, "E", "S")]
auto[, .N, by = "status"]
## status N
## 1: S 750
## 2: E 394
The table returned by betaBinomXI is comprehensive. In order to summarize
results at the subject level, sample_clean returns informations
relevant to each sample, such as estimated skew and the model used.
sc <- sample_clean(auto)
head(sc)
## sample model f a_est b_est
## 1: sample30 MM 0.09754767 3.341427e+01 309.1287
## 2: sample31 BB 0.24807246 1.642445e+06 4978382.5780
## 3: sample32 MM3 0.23260832 1.333344e+03 4398.7996
## 4: sample33 BB 0.24142586 3.016987e+05 947954.8098
## 5: sample34 MM 0.25126586 6.533422e+01 194.6861
## 6: sample35 MM 0.24574585 4.982794e+05 1529341.2573
Although one of XCIR's major strength is in its ability to make individual
level calls, it can be of interest to look at the classification of X-linked
genes in the entire dataset.
For every gene in the dataset, getXCIstate reports the number of samples
where a call was made (Ntot), the percentage of them in which it escaped (pe)
and an overall classification based on the following cutoffs
xcis <- getXCIstate(auto)
head(xcis)
## GENE Ntot Nesc pe XCIstate
## 1: gene_test_1 11 0 0.00000000 S
## 2: gene_test_11 11 8 0.72727273 VE
## 3: gene_test_13 11 1 0.09090909 S
## 4: gene_test_14 11 10 0.90909091 E
## 5: gene_test_16 11 9 0.81818182 E
## 6: gene_test_17 11 11 1.00000000 E
sessionInfo()
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] data.table_1.12.8 XCIR_1.2.0 knitr_1.28
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 lattice_0.20-41
## [3] prettyunits_1.1.1 Rsamtools_2.4.0
## [5] Biostrings_2.56.0 assertthat_0.2.1
## [7] digest_0.6.25 BiocFileCache_1.12.0
## [9] cellranger_1.1.0 R6_2.4.1
## [11] GenomeInfoDb_1.24.0 stats4_4.0.0
## [13] RSQLite_2.2.0 evaluate_0.14
## [15] highr_0.8 ggplot2_3.3.0
## [17] httr_1.4.1 pillar_1.4.3
## [19] zlibbioc_1.34.0 rlang_0.4.5
## [21] GenomicFeatures_1.40.0 progress_1.2.2
## [23] readxl_1.3.1 curl_4.3
## [25] blob_1.2.1 S4Vectors_0.26.0
## [27] Matrix_1.2-18 labeling_0.3
## [29] BiocParallel_1.22.0 stringr_1.4.0
## [31] munsell_0.5.0 RCurl_1.98-1.2
## [33] bit_1.1-15.2 biomaRt_2.44.0
## [35] DelayedArray_0.14.0 compiler_4.0.0
## [37] rtracklayer_1.48.0 xfun_0.13
## [39] pkgconfig_2.0.3 askpass_1.1
## [41] BiocGenerics_0.34.0 openssl_1.4.1
## [43] tidyselect_1.0.0 SummarizedExperiment_1.18.0
## [45] tibble_3.0.1 GenomeInfoDbData_1.2.3
## [47] IRanges_2.22.0 matrixStats_0.56.0
## [49] XML_3.99-0.3 crayon_1.3.4
## [51] dplyr_0.8.5 dbplyr_1.4.3
## [53] GenomicAlignments_1.24.0 bitops_1.0-6
## [55] rappdirs_0.3.1 grid_4.0.0
## [57] gtable_0.3.0 lifecycle_0.2.0
## [59] DBI_1.1.0 seqminer_8.0
## [61] magrittr_1.5 scales_1.1.0
## [63] stringi_1.4.6 farver_2.0.3
## [65] XVector_0.28.0 ellipsis_0.3.0
## [67] vctrs_0.2.4 tools_4.0.0
## [69] bit64_0.9-7 BSgenome_1.56.0
## [71] Biobase_2.48.0 glue_1.4.0
## [73] purrr_0.3.4 hms_0.5.3
## [75] parallel_4.0.0 colorspace_1.4-1
## [77] AnnotationDbi_1.50.0 GenomicRanges_1.40.0
## [79] memoise_1.1.0 VariantAnnotation_1.34.0