single-sperm-co-analysis
26 April 2022
Contents
- 1 Introduction
- 2 Locate file path
- 3 File information
- 4 Diagnostic functions
- 5 Input parsing
- 6 Construct
RangedSummarizedExprimentobject - 7 Count crossovers
- 8 Count crossovers for SNP intervals
- 9 Calculate genetic distances
- 10 Calculate genetic distances
- 11 Plot whole genome genetic distances
- 12 Group differences
- 13 Session info
suppressPackageStartupMessages({
library(comapr)
library(SummarizedExperiment)
})1 Introduction
In the previous vignette, we demonstrated how to calculate genetic distances using genotyped markers from a group of samples.
Genetic distance calculate from genotype shifts of markers
In this document, we will focus on building individualized genetic maps from
output files of sscocaller which is available at
here.
2 Locate file path
The comapr package includes a list of toy example output files from the sscocaller
command line tool. The follow code will get the file path that we will use later.
demo_path <-paste0(system.file("extdata",package = "comapr"),"/")We can see that we have two samples (individual donors) and each of them has haplotype states inferred for chr1 to chr5.
list.files(demo_path)
#> [1] "s1_barcodes.txt" "s1_chr1_altCount.mtx" "s1_chr1_snpAnnot.txt"
#> [4] "s1_chr1_totalCount.mtx" "s1_chr1_vi.mtx" "s1_chr1_viSegInfo.txt"
#> [7] "s1_chr2_altCount.mtx" "s1_chr2_snpAnnot.txt" "s1_chr2_totalCount.mtx"
#> [10] "s1_chr2_vi.mtx" "s1_chr2_viSegInfo.txt" "s1_chr3_altCount.mtx"
#> [13] "s1_chr3_snpAnnot.txt" "s1_chr3_totalCount.mtx" "s1_chr3_vi.mtx"
#> [16] "s1_chr3_viSegInfo.txt" "s1_chr4_altCount.mtx" "s1_chr4_snpAnnot.txt"
#> [19] "s1_chr4_totalCount.mtx" "s1_chr4_vi.mtx" "s1_chr4_viSegInfo.txt"
#> [22] "s1_chr5_altCount.mtx" "s1_chr5_snpAnnot.txt" "s1_chr5_totalCount.mtx"
#> [25] "s1_chr5_vi.mtx" "s1_chr5_viSegInfo.txt" "s2_barcodes.txt"
#> [28] "s2_chr1_snpAnnot.txt" "s2_chr1_vi.mtx" "s2_chr1_viSegInfo.txt"
#> [31] "s2_chr2_snpAnnot.txt" "s2_chr2_vi.mtx" "s2_chr2_viSegInfo.txt"
#> [34] "s2_chr3_snpAnnot.txt" "s2_chr3_vi.mtx" "s2_chr3_viSegInfo.txt"
#> [37] "s2_chr4_snpAnnot.txt" "s2_chr4_vi.mtx" "s2_chr4_viSegInfo.txt"
#> [40] "s2_chr5_snpAnnot.txt" "s2_chr5_vi.mtx" "s2_chr5_viSegInfo.txt"3 File information
- *.mtx
- sparse matrix with columns corresponding to the list of sperm cell barcodes and rows corresponding to the list of SNP positions in VCF file
- {sample}_chr1_altCount.mtx, a sparse mtx with entries representing alternative allele counts
- {sample}_chr1_totalCount.mtx, a sparse mtx with entries representing total allele counts
- {sample}_chr1_vi.mtx, a sparse mtx with entries representing inferred viterbi state (haplotype state)
- {sample}_chr1_snpAnnot.txt, the SNP positions and allele
- {sample}_chr1_SegInfo.txt, statistics of viterbi state segments in text file format. It contains consecutive viterbi states for each chromosome with statistics including, starting SNP position, ending SNP position, the number of SNPs supporting the segment, the log likelihood ratio of the viterbi segment and the inferred hidden state.
4 Diagnostic functions
comapr provides quality-check functions for examining SNP coverage per chr and
per cell, chromosome segregation pattern checks, and summary statics for
filtering low confidence crossovers.
4.1 perCellChrQC
pcQC <- perCellChrQC(sampleName="s1",chroms=c("chr1"),
path=paste0(demo_path,"/"),
barcodeFile=NULL)5 Input parsing
Input-parsing functions are implemented in comapr to construct
RangedSummarizedExpriment object that parses files generated
from sscocaller and filter out low-confidence COs at the same time. For the
demo dataset, these filters do not have any effects:
minSNP = 0,
minlogllRatio = 50,
bpDist = 100,
maxRawCO=10,
minCellSNP = 16 Construct RangedSummarizedExpriment object
We first construct RangedSummarizedExpriment object from parsing output files
from sscocaller and filter out low-confidence crossovers.
s1_rse_state <- readHapState("s1",chroms=c("chr1","chr2"),
path=demo_path,barcodeFile=NULL,
minSNP = 0,
minlogllRatio = 50,
bpDist = 100,
maxRawCO=10,
minCellSNP = 1)
s2_rse_state <- readHapState("s2",chroms=c("chr1","chr2"),
path=demo_path,
barcodeFile=NULL,
minSNP = 0,
minlogllRatio = 50,
bpDist = 100,
maxRawCO=10,
minCellSNP = 1)s1_rse_state
#> class: RangedSummarizedExperiment
#> dim: 12 5
#> metadata(10): ithSperm Seg_start ... bp_dist barcode
#> assays(1): vi_state
#> rownames: NULL
#> rowData names(0):
#> colnames(5): BC1 BC2 BC3 BC4 BC5
#> colData names(1): barcodesThe Viterbi states for SNP markers are stored in the “assay” slot:
assay(s1_rse_state)
#> 12 x 5 sparse Matrix of class "dgCMatrix"
#> BC1 BC2 BC3 BC4 BC5
#> [1,] 1 2 1 2 1
#> [2,] 1 . . . .
#> [3,] . 2 . . .
#> [4,] . . . . 1
#> [5,] 2 1 . . .
#> [6,] 2 1 1 2 .
#> [7,] 1 2 1 2 1
#> [8,] 1 . . . .
#> [9,] . 2 . . .
#> [10,] . . . . 1
#> [11,] 2 1 . . .
#> [12,] 2 1 1 2 .The rowRanges contains the SNP’s positions:
rowRanges(s1_rse_state)
#> GRanges object with 12 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 3000 *
#> [2] chr1 3200 *
#> [3] chr1 4000 *
#> [4] chr1 4500 *
#> [5] chr1 5500 *
#> ... ... ... ...
#> [8] chr2 3200 *
#> [9] chr2 4000 *
#> [10] chr2 4500 *
#> [11] chr2 5500 *
#> [12] chr2 6000 *
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengths6.1 Formate sample group factor
We have read in the Viterbi states for cells from two samples: s1 and s2. We now combine them into one data object for downstream analysis.
6.2 Add sample group factor
colData(s1_rse_state)
#> DataFrame with 5 rows and 1 column
#> barcodes
#> <character>
#> BC1 BC1
#> BC2 BC2
#> BC3 BC3
#> BC4 BC4
#> BC5 BC5
colData(s1_rse_state)$sampleGroup <- "s1"
colData(s2_rse_state)$sampleGroup <- "s2"6.3 Combine two groups
We now call combineHapState function to combine sample s1 and sample s2
into one RangedSummarizedExperiment object.
twoSample <- combineHapState(s1_rse_state,
s2_rse_state)Now the assay data slot contains the Viterbi states across SNP positions for
the combined samples.
twoSample <- combineHapState(s1_rse_state,s2_rse_state)Now the twoSample object contains the cells from both samples with assay slot
contains the Viterbi states and rowRanges contains position annotaitons for
the list SNP markers.
assay(twoSample)
#> 12 x 10 sparse Matrix of class "dgCMatrix"
#> [[ suppressing 10 column names 'BC1', 'BC2', 'BC3' ... ]]
#>
#> [1,] 1 2 1 2 1 1 2 1 2 1
#> [2,] 1 . . . . . . . . .
#> [3,] . 2 . . . . 2 . . .
#> [4,] . . . . 1 . . . . 1
#> [5,] 2 1 . . . . 1 . . .
#> [6,] 2 1 1 2 . 1 1 1 2 .
#> [7,] 1 2 1 2 1 1 2 1 2 1
#> [8,] 1 . . . . . . . . .
#> [9,] . 2 . . . . 2 . . .
#> [10,] . . . . 1 . . . . 1
#> [11,] 2 1 . . . . 1 . . .
#> [12,] 2 1 1 2 . 1 1 1 2 .7 Count crossovers
The countCOs function can then find out the crossover intervals according to
the Viterbi states for each cell and the number of crossovers per cell is then
calculated.
8 Count crossovers for SNP intervals
countCOs function will find the crossover intervals for each samples and
attribute the observed crossovers from each sample to the corresponding intervals.
cocounts <- countCOs(twoSample)The rowRanges from the resulting data object now denotes the crossover interval
and the assay slot now contains the number of crossovers in each cell.
Now rowRanges contains the intervals
rowRanges(cocounts)
#> GRanges object with 6 ranges and 0 metadata columns:
#> seqnames ranges strand
#> <Rle> <IRanges> <Rle>
#> [1] chr1 3201-3999 *
#> [2] chr1 4001-4499 *
#> [3] chr1 4501-5499 *
#> [4] chr2 3201-3999 *
#> [5] chr2 4001-4499 *
#> [6] chr2 4501-5499 *
#> -------
#> seqinfo: 2 sequences from an unspecified genome; no seqlengthsThe colData slot still contains the annotations of each cell.
colData(cocounts)
#> DataFrame with 10 rows and 2 columns
#> barcodes sampleGroup
#> <character> <character>
#> BC1 BC1 s1
#> BC2 BC2 s1
#> BC3 BC3 s1
#> BC4 BC4 s1
#> BC5 BC5 s1
#> BCa BCa s2
#> BCb BCb s2
#> BCc BCc s2
#> BCd BCd s2
#> BCe BCe s29 Calculate genetic distances
The genetic distances can be calculated by using mapping functions such as the
Kosambi or Haldane and assay slot contains the number of crossovers
found in each sample across these intervals.
assay(cocounts)
#> DataFrame with 6 rows and 10 columns
#> BC1 BC2 BC3 BC4 BC5 BCa BCb
#> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
#> 1 0.347826 0.000000 0 0 0 0 0.000000
#> 2 0.217391 0.333333 0 0 0 0 0.333333
#> 3 0.434783 0.666667 0 0 0 0 0.666667
#> 4 0.347826 0.000000 0 0 0 0 0.000000
#> 5 0.217391 0.333333 0 0 0 0 0.333333
#> 6 0.434783 0.666667 0 0 0 0 0.666667
#> BCc BCd BCe
#> <numeric> <numeric> <numeric>
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 0 0
#> 5 0 0 0
#> 6 0 0 010 Calculate genetic distances
calGeneticDist function will convert the raw crossover frequencies to genetic
distances via selected mapping function (ie. kosambi or haldane).
dist_gr <- calGeneticDist(co_count = cocounts, mapping_fun = "k")
dist_gr
#> class: RangedSummarizedExperiment
#> dim: 6 10
#> metadata(0):
#> assays(1): co_count
#> rownames: NULL
#> rowData names(2): raw_rate kosambi
#> colnames(10): BC1 BC2 ... BCd BCe
#> colData names(2): barcodes sampleGroupThe genetic distances for each interval are stored in rowData.
rowData(dist_gr)
#> DataFrame with 6 rows and 2 columns
#> raw_rate kosambi
#> <numeric> <numeric>
#> 1 0.0347826 3.48389
#> 2 0.0884058 8.93447
#> 3 0.1768116 18.47894
#> 4 0.0347826 3.48389
#> 5 0.0884058 8.93447
#> 6 0.1768116 18.47894The above genetic distances have been calculated using all samples. We can also specify the group factor so that we can calculate genetic distances for different sample groups:
## sampleGroup is a column in the colData slot
dist_gr <- calGeneticDist(co_count = cocounts,
group_by = "sampleGroup",
mapping_fun = "k")Now the group/Sample specific distances are stored in rowData
rowData(dist_gr)$kosambi
#> s1 s2
#> [1,] 7.001937 0.00000
#> [2,] 11.198036 6.70660
#> [3,] 23.647496 13.66359
#> [4,] 7.001937 0.00000
#> [5,] 11.198036 6.70660
#> [6,] 23.647496 13.6635911 Plot whole genome genetic distances
We construct a GRange object from the dist_gr first.
p_gr <- granges(dist_gr)
mcols(p_gr) <- rowData(dist_gr)$kosambi
We can plot whole-genome genetic distances
plotWholeGenome(p_gr)
We can also do per chromosome plot
plotGeneticDist(p_gr,chr = "chr1",cumulative = TRUE)
12 Group differences
bootstrapDist function generates the sampling distribution of the difference
in total genetic distances for the two groups under comparisons.
set.seed(100)
bootsDiff <- bootstrapDist(co_gr = cocounts,B=10,
group_by = "sampleGroup")
#> Warning in log((1 + 2 * rb_rate)/(1 - 2 * rb_rate)): NaNs producedhist(bootsDiff)
From the bootsDiff data object, we can find a 95% confidence interval to test
whether the difference is statistically significant.
quantile(bootsDiff,c(0.025,0.975),na.rm =TRUE)
#> 2.5% 97.5%
#> -46.42516 245.06169An alternative re-sampling method, permuteDist, can be applied to generate a
null distribution for the group difference by reshuffling the group labels across
the samples.
set.seed(100)
perms <- permuteDist(cocounts,B=1000,group_by = "sampleGroup")A p-value can be calculated using the statmod::permp function (Phipson and Smyth 2010).
statmod::permp(x = sum(perms$permutes>=perms$observed_diff,
na.rm = TRUE),
nperm = sum(!is.na(perms$permutes)),
n1 = perms$nSample[1],
n2 = perms$nSample[2])
#> [1] 0.514513313 Session info
sessionInfo()
#> R version 4.2.0 RC (2022-04-19 r82224)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] SummarizedExperiment_1.26.0 Biobase_2.56.0
#> [3] MatrixGenerics_1.8.0 matrixStats_0.62.0
#> [5] BiocParallel_1.30.0 GenomicRanges_1.48.0
#> [7] GenomeInfoDb_1.32.0 IRanges_2.30.0
#> [9] S4Vectors_0.34.0 BiocGenerics_0.42.0
#> [11] comapr_1.0.0 BiocStyle_2.24.0
#>
#> loaded via a namespace (and not attached):
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#> [4] circlize_0.4.14 biovizBase_1.44.0 htmlTable_2.4.0
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#> [31] assertthat_0.2.1 Matrix_1.4-1 fastmap_1.1.0
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#> [37] prettyunits_1.1.1 tools_4.2.0 gtable_0.3.0
#> [40] glue_1.6.2 GenomeInfoDbData_1.2.8 reshape2_1.4.4
#> [43] dplyr_1.0.8 rappdirs_0.3.3 Rcpp_1.0.8.3
#> [46] jquerylib_0.1.4 vctrs_0.4.1 Biostrings_2.64.0
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#> [94] bit_4.0.4 tidyselect_1.1.2 plyr_1.8.7
#> [97] magrittr_2.0.3 bookdown_0.26 R6_2.5.1
#> [100] magick_2.7.3 generics_0.1.2 Hmisc_4.7-0
#> [103] DelayedArray_0.22.0 DBI_1.1.2 withr_2.5.0
#> [106] pillar_1.7.0 foreign_0.8-82 survival_3.3-1
#> [109] KEGGREST_1.36.0 RCurl_1.98-1.6 nnet_7.3-17
#> [112] tibble_3.1.6 crayon_1.5.1 utf8_1.2.2
#> [115] plotly_4.10.0 BiocFileCache_2.4.0 rmarkdown_2.14
#> [118] jpeg_0.1-9 progress_1.2.2 grid_4.2.0
#> [121] data.table_1.14.2 blob_1.2.3 digest_0.6.29
#> [124] tidyr_1.2.0 munsell_0.5.0 viridisLite_0.4.0
#> [127] Gviz_1.40.0 bslib_0.3.1Phipson, Belinda, and Gordon K Smyth. 2010. “Permutation P-Values Should Never Be Zero: Calculating Exact P-Values When Permutations Are Randomly Drawn.” Stat. Appl. Genet. Mol. Biol. 9 (October): Article39.