From 6d5fd62cc3f772789599fee2d77ac3a4aff6e9a4 Mon Sep 17 00:00:00 2001 From: mgrasshoff Date: Tue, 9 Jul 2024 21:06:40 +0200 Subject: [PATCH] Fixed loading bug after updating to R4.4. --- DESCRIPTION | 27 +- R/CalculateStrandCorrelation.R | 16 +- R/LoadingMAEGATK_typewise.R | 5 - README.md | 5 +- docs/404.html | 18 +- docs/articles/SIGURD.html | 381 ++++++++++++++++++ .../figure-html/VariantsOfInterest-1.png | Bin 0 -> 158831 bytes docs/articles/index.html | 16 +- docs/authors.html | 22 +- .../bootstrap-5.3.1/bootstrap.bundle.min.js | 7 + .../bootstrap.bundle.min.js.map | 1 + docs/deps/bootstrap-5.3.1/bootstrap.min.css | 5 + docs/deps/data-deps.txt | 4 +- docs/index.html | 43 +- docs/pkgdown.js | 8 +- docs/pkgdown.yml | 8 +- docs/reference/AmpliconSupplementing.html | 12 +- docs/reference/CalculateAlleleFrequency.html | 12 +- docs/reference/CalculateAltReads.html | 12 +- docs/reference/CalculateConsensus.html | 12 +- .../reference/CalculateCorrelationPValue.html | 12 +- docs/reference/CalculateCoverage.html | 12 +- docs/reference/CalculateFisherTestPValue.html | 12 +- docs/reference/CalculateQuality.html | 12 +- .../reference/CalculateStrandCorrelation.html | 12 +- docs/reference/ClonalDefinition.html | 12 +- docs/reference/ClonalDiversity.html | 12 +- docs/reference/CombineSEobjects.html | 12 +- docs/reference/Enrichment_FisherTest.html | 12 +- docs/reference/Filtering.html | 12 +- docs/reference/GetCellInfoPerVariant.html | 12 +- docs/reference/GetVariantInfo.html | 12 +- docs/reference/HeatmapVoi.html | 12 +- docs/reference/LoadingMAEGATK_typewise.html | 12 +- docs/reference/LoadingVCF_typewise.html | 12 +- docs/reference/LoadingVarTrix_typewise.html | 12 +- docs/reference/Merging_SE_list.html | 12 +- docs/reference/RowWiseSplit.html | 12 +- docs/reference/SeparatingMatrixToList.html | 12 +- docs/reference/SetVariantInfo.html | 12 +- docs/reference/UMIQC.html | 12 +- docs/reference/UMI_seq.html | 12 +- docs/reference/VariantBurden.html | 12 +- .../VariantCloneSizeThresholding.html | 12 +- docs/reference/VariantCorrelationHeatmap.html | 12 +- docs/reference/VariantFisherTestHeatmap.html | 12 +- .../VariantQuantileThresholding.html | 12 +- docs/reference/VariantWiseCorrelation.html | 12 +- docs/reference/VariantWiseFisherTest.html | 12 +- docs/reference/char_to_numeric.html | 12 +- docs/reference/combine_NAMES.html | 12 +- docs/reference/combine_SparseMatrix.html | 12 +- docs/reference/computeAFMutMatrix.html | 12 +- docs/reference/getAltMatrix.html | 12 +- docs/reference/getMutMatrix.html | 12 +- docs/reference/getReadMatrix.html | 12 +- docs/reference/getRefMatrix.html | 12 +- docs/reference/get_consensus.html | 12 +- docs/reference/index.html | 12 +- docs/reference/load_object.html | 12 +- docs/reference/save_object.html | 12 +- docs/reference/sdiv.html | 12 +- docs/search.json | 2 +- docs/sitemap.xml | 3 + tests/testthat/test-LoadingMAEGATK_typewise.R | 2 +- ...ltering_Alts_Threshold_ExpectedResults.rds | Bin 1602 -> 1606 bytes .../Filtering_Blacklist_ExpectedResults.rds | Bin 1491 -> 1494 bytes ...iltering_CellThreshold_ExpectedResults.rds | Bin 1525 -> 1527 bytes ...ing_Fraction_Threshold_ExpectedResults.rds | Bin 1244 -> 1245 bytes ...ering_VariantThreshold_ExpectedResults.rds | Bin 1674 -> 1675 bytes .../LoadingMAEGATK_typewise_Test_Data.rds | Bin 1527 -> 1527 bytes ...gMAEGATK_typewise_Test_ExpectedResults.rds | Bin 1674 -> 1675 bytes .../Merging_SE_list_ExpectedResults.rds | Bin 1756 -> 1757 bytes vignettes/{SiGURD.Rmd => SIGURD.Rmd} | 15 +- 74 files changed, 771 insertions(+), 369 deletions(-) create mode 100644 docs/articles/SIGURD.html create mode 100644 docs/articles/SIGURD_files/figure-html/VariantsOfInterest-1.png create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map create mode 100644 docs/deps/bootstrap-5.3.1/bootstrap.min.css rename vignettes/{SiGURD.Rmd => SIGURD.Rmd} (93%) diff --git a/DESCRIPTION b/DESCRIPTION index b52ddb4..1e717d8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,14 +1,14 @@ Package: sigurd Type: Package Title: Single cell Genotyping Using RNA Data -Version: 0.2.51 +Version: 0.2.52 Authors@R: c( person(given = "Martin", family = "Grasshoff", role = c("aut", "cre"), email = "mgrasshoff@ukaachen.de"), - person(given = "Ivan Costa", - family = "Gesteira", + person(given = "Ivan", + family = "Costa", role = c("aut"), email = "ivan.costa@rwth-aachen.de", comment = c(ORCID = "0000-0003-2890-8697"))) @@ -26,31 +26,32 @@ Imports: data.table, dplyr, DropletUtils, - fastmatch, GenomeInfoDb, - ggplot2, grid, Matrix, - MatrixGenerics, - magrittr, methods, parallel, - rcompanion, S4Vectors, Seurat, SummarizedExperiment, scales, tibble, - tidyr, - tidyverse, - VariantAnnotation -RoxygenNote: 7.2.3 + VariantAnnotation, + vegan +RoxygenNote: 7.3.2 Suggests: GenomicRanges, + fastmatch, + ggplot2, IRanges, knitr, + magrittr, + MatrixGenerics, + rcompanion, rmarkdown, SeuratObject, - testthat (>= 3.0.0) + testthat (>= 3.0.0), + tidyr, + tidyverse VignetteBuilder: knitr Config/testthat/edition: 3 diff --git a/R/CalculateStrandCorrelation.R b/R/CalculateStrandCorrelation.R index 575ec70..13ac2ec 100644 --- a/R/CalculateStrandCorrelation.R +++ b/R/CalculateStrandCorrelation.R @@ -20,7 +20,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){ dt <- merge(data.table::data.table(summary(reads_A_fw)), data.table::data.table(summary(reads_A_rev)), by.x = c("i", "j"), by.y = c("i", "j"), - all = TRUE)[x.x >0 | x.y >0] + all = TRUE) + dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,] dt <- data.table::data.table(variant = variants_A[dt[[1]]], cell_id = dt[[2]], fw = dt[[3]], rev = dt[[4]]) @@ -34,14 +35,11 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){ rownames(reads_C_rev) <- paste0(chromosome_prefix, "_", 1:nrow(reads_C_rev), "_", ref_allele, "_C") reads_C_fw <- reads_C_fw[ref_allele != "C",] reads_C_rev <- reads_C_rev[ref_allele != "C",] -# dt <- merge(data.table::data.table(summary(reads_C_fw)), -# data.table::data.table(summary(reads_C_rev)), -# by.x = c("i", "j"), by.y = c("i", "j"), -# all = TRUE)[x.x > 0 | x.y > 0] dt <- merge(data.table::data.table(summary(reads_C_fw)), data.table::data.table(summary(reads_C_rev)), by.x = c("i", "j"), by.y = c("i", "j"), - all = TRUE)[!!dplyr::sym("x.x") > 0 | !!dplyr::sym("x.y") > 0] + all = TRUE) + dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,] dt <- data.table::data.table(variant = variants_C[dt[[1]]], cell_id = dt[[2]], fw = dt[[3]], rev = dt[[4]]) @@ -58,7 +56,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){ dt <- merge(data.table::data.table(summary(reads_G_fw)), data.table::data.table(summary(reads_G_rev)), by.x = c("i", "j"), by.y = c("i", "j"), - all = TRUE)[x.x >0 | x.y >0] + all = TRUE) + dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,] dt <- data.table::data.table(variant = variants_G[dt[[1]]], cell_id = dt[[2]], fw = dt[[3]], rev = dt[[4]]) @@ -75,7 +74,8 @@ CalculateStrandCorrelation <- function(SE, chromosome_prefix = "chrM"){ dt <- merge(data.table::data.table(summary(reads_T_fw)), data.table::data.table(summary(reads_T_rev)), by.x = c("i", "j"), by.y = c("i", "j"), - all = TRUE)[x.x >0 | x.y >0] + all = TRUE) + dt <- dt[dt[,x.x] > 0 | dt[,x.y] > 0,] dt <- data.table::data.table(variant = variants_T[dt[[1]]], cell_id = dt[[2]], fw = dt[[3]], rev = dt[[4]]) diff --git a/R/LoadingMAEGATK_typewise.R b/R/LoadingMAEGATK_typewise.R index ae0701d..5983eb3 100755 --- a/R/LoadingMAEGATK_typewise.R +++ b/R/LoadingMAEGATK_typewise.R @@ -150,11 +150,6 @@ LoadingMAEGATK_typewise <- function(samples_file, samples_path = NULL, patient, coverage_depth_per_cell <- gsub("_._.$", "", coverage_depth_per_cell) coverage_depth_per_cell <- !duplicated(coverage_depth_per_cell) coverage_depth_per_cell <- coverage[coverage_depth_per_cell,] - cell_ids <- colnames(coverage_depth_per_cell) - variant_names <- rownames(coverage_depth_per_cell) - coverage_depth_per_cell <- suppressWarnings(matrix(coverage, nrow = length(variant_names), ncol = length(cell_ids))) - colnames(coverage_depth_per_cell) <- cell_ids - rownames(coverage_depth_per_cell) <- variant_names coverage_depth_per_variant <- rowMeans(coverage) coverage_depth_per_cell <- colMeans(coverage_depth_per_cell) meta_data_col <- data.frame(Cell = colnames(consensus), Patient = patient, Sample = substr(x = colnames(consensus), start = 1, stop = nchar(colnames(consensus))-(cellbarcode_length+1)), AverageCoverage = coverage_depth_per_cell) diff --git a/README.md b/README.md index 26c5dee..a7a0ba2 100644 --- a/README.md +++ b/README.md @@ -15,13 +15,14 @@ We employ a imputation approach that utilizes the correlation between mitochondr You can install sigurd using the following code. The vignette requires data that is currently not published, but is provided as a reference. ```{r} + install.packages("devtools") devtools::install_github("https://github.com/CostaLab/sigurd.git", build_vignettes = FALSE) require(sigurd) ``` -# SiGURD +# SIGURD We have provided a small example data set for SiGURD. It consists of chromosome 9 and MT for one MPN sample. The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mutations In Cancer web site, http://cancer.sanger.ac.uk/cosmic Bamford et al (2004) The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer, 91,355-358. @@ -32,7 +33,7 @@ The mutation data was obtained from the Sanger Institute Catalogue Of Somatic Mu ``` -# Current Features v0.2.51 +# Current Features v0.2.52 - Loading data from VarTrix and MAEGATK. - Transforming the data to be compatible for joint analysis. diff --git a/docs/404.html b/docs/404.html index 820957e..300a913 100644 --- a/docs/404.html +++ b/docs/404.html @@ -7,10 +7,10 @@ Page not found (404) • sigurd - - + + - + + + + + + + +SIGURD • sigurd + + + + + + + + + + Skip to contents + + +
+ + + + +
+
+ + + + +

R version: R version 4.4.1 (2024-06-14)

+

Package version: 0.2.52

+
+

Your input file. +

+
+sample_path <- system.file("extdata", "Input_Example_local.csv", package = "sigurd")
+sample_file <- read.csv(sample_path)
+print(sample_file)
+
##                      patient                    sample  source             type
+## 1                    Sample1             Minus_Sample1 VarTrix scRNAseq_Somatic
+## 2                    Sample1             Minus_Sample1 VarTrix      scRNAseq_MT
+## 3                    Sample1             Minus_Sample1 MAEGATK      scRNAseq_MT
+## 4                    Sample1              Plus_Sample1 VarTrix scRNAseq_Somatic
+## 5                    Sample1              Plus_Sample1 VarTrix      scRNAseq_MT
+## 6                    Sample1              Plus_Sample1 MAEGATK      scRNAseq_MT
+## 7     SW_CellLineMix_All_mr3    SW_CellLineMix_All_mr3 MAEGATK      Amplicon_MT
+## 8  SW_CellLineMix_RNAseq_mr3 SW_CellLineMix_RNAseq_mr3 MAEGATK      scRNAseq_MT
+## 9      TenX_BPDCN712_All_mr3     TenX_BPDCN712_All_mr3 MAEGATK      Amplicon_MT
+## 10  TenX_BPDCN712_RNAseq_mr3  TenX_BPDCN712_RNAseq_mr3 MAEGATK      scRNAseq_MT
+##                                                   bam
+## 1  ~/test_data/Minus_Sample1/possorted_genome_bam.bam
+## 2  ~/test_data/Minus_Sample1/possorted_genome_bam.bam
+## 3  ~/test_data/Minus_Sample1/possorted_genome_bam.bam
+## 4   ~/test_data/Plus_Sample1/possorted_genome_bam.bam
+## 5   ~/test_data/Plus_Sample1/possorted_genome_bam.bam
+## 6   ~/test_data/Plus_Sample1/possorted_genome_bam.bam
+## 7                                                NADA
+## 8                                                NADA
+## 9                                                NADA
+## 10                                               NADA
+##                                                                                        input_path
+## 1                                                                    ~/test_data/VarTrix/Somatic/
+## 2                                                                         ~/test_data/VarTrix/MT/
+## 3                                             ~/test_data/MAEGATK/Minus_Sample1/final/maegatk.rds
+## 4                                                                    ~/test_data/VarTrix/Somatic/
+## 5                                                                         ~/test_data/VarTrix/MT/
+## 6                                              ~/test_data/MAEGATK/Plus_Sample1/final/maegatk.rds
+## 7        ~/test_data/MAESTER_data/SW_CellLineMix_All_mr3/final/SW_CellLineMix_All_mr3_maegatk.rds
+## 8  ~/test_data/MAESTER_data/SW_CellLineMix_RNAseq_mr3/final/SW_CellLineMix_RNAseq_mr3_maegatk.rds
+## 9          ~/test_data/MAESTER_data/TenX_BPDCN712_All_mr3/final/TenX_BPDCN712_All_mr3_maegatk.rds
+## 10   ~/test_data/MAESTER_data/TenX_BPDCN712_RNAseq_mr3/final/TenX_BPDCN712_RNAseq_mr3_maegatk.rds
+##                                                   cells
+## 1  ~/test_data/Minus_Sample1/Minus_Sample1_barcodes.tsv
+## 2  ~/test_data/Minus_Sample1/Minus_Sample1_barcodes.tsv
+## 3  ~/test_data/Minus_Sample1/Minus_Sample1_barcodes.tsv
+## 4    ~/test_data/Plus_Sample1/Plus_Sample1_barcodes.tsv
+## 5    ~/test_data/Plus_Sample1/Plus_Sample1_barcodes.tsv
+## 6    ~/test_data/Plus_Sample1/Plus_Sample1_barcodes.tsv
+## 7                                                  NADA
+## 8                                                  NADA
+## 9                                                  NADA
+## 10                                                 NADA
+
+
+

Your vcf files. +

+

These files are needed for VarTrix and not MGATK/MAEGATK. Since +MAEGATK only analyses the mitochondrial genome, you only need to decide +on a chromosomal prefix for your data. See the loading of data +below.

+
+vcf_path <- system.file("extdata", "CosmicSubset_filtered.vcf", package = "sigurd")
+vcf <- VariantAnnotation::readVcf(vcf_path)
+vcf_info <- VariantAnnotation::info(vcf)
+print(vcf)
+
## class: CollapsedVCF 
+## dim: 1684 0 
+## rowRanges(vcf):
+##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
+## info(vcf):
+##   DataFrame with 10 columns: GENE, STRAND, GENOMIC_ID, LEGACY_ID, CDS, AA, H...
+## info(header(vcf)):
+##               Number Type    Description                        
+##    GENE       1      String  Gene name                          
+##    STRAND     1      String  Gene strand                        
+##    GENOMIC_ID 1      String  Genomic Mutation ID                
+##    LEGACY_ID  1      String  Legacy Mutation ID                 
+##    CDS        1      String  CDS annotation                     
+##    AA         1      String  Peptide annotation                 
+##    HGVSC      1      String  HGVS cds syntax                    
+##    HGVSP      1      String  HGVS peptide syntax                
+##    HGVSG      1      String  HGVS genomic syntax                
+##    CNT        1      Integer How many samples have this mutation
+## geno(vcf):
+##   List of length 0:
+
+print(vcf_info)
+
## DataFrame with 1684 rows and 10 columns
+##             GENE      STRAND  GENOMIC_ID    LEGACY_ID            CDS
+##      <character> <character> <character>  <character>    <character>
+## 1           ABL1           +          NA COSN17133235  c.136+2107G>C
+## 2           ABL1           +          NA COSN14774721  c.136+2599C>T
+## 3           ABL1           +          NA COSN17133236  c.136+3198G>C
+## 4           ABL1           +          NA COSN17133237  c.136+4488G>C
+## 5           ABL1           +          NA COSN17133050  c.136+5055C>T
+## ...          ...         ...         ...          ...            ...
+## 1680         WT1           -          NA  COSN6609219    c.872+82G>T
+## 1681         WT1           -          NA COSN17132919    c.872+16G>A
+## 1682         WT1           -          NA COSN17134797    c.770-57C>T
+## 1683         WT1           -          NA  COSM5020955       c.594C>T
+## 1684       ZRSR2           +          NA  COSM3035276 c.1338_1343dup
+##                  AA                  HGVSC                  HGVSP
+##         <character>            <character>            <character>
+## 1               p.? ENST00000372348.6:c...                     NA
+## 2               p.? ENST00000372348.6:c...                     NA
+## 3               p.? ENST00000372348.6:c...                     NA
+## 4               p.? ENST00000372348.6:c...                     NA
+## 5               p.? ENST00000372348.6:c...                     NA
+## ...             ...                    ...                    ...
+## 1680            p.? ENST00000332351.7:c...                     NA
+## 1681            p.? ENST00000332351.7:c...                     NA
+## 1682            p.? ENST00000332351.7:c...                     NA
+## 1683      p.N198%3D ENST00000332351.7:c... ENSP00000331327.3:p...
+## 1684 p.S447_R448dup ENST00000307771.7:c... ENSP00000303015.7:p...
+##                       HGVSG       CNT
+##                 <character> <integer>
+## 1          9:g.130716562G>C        10
+## 2          9:g.130717054C>T        10
+## 3          9:g.130717653G>C        10
+## 4          9:g.130718943G>C        10
+## 5          9:g.130719510C>T        11
+## ...                     ...       ...
+## 1680       11:g.32427874C>A        74
+## 1681       11:g.32427940C>T       132
+## 1682       11:g.32428115G>A       108
+## 1683       11:g.32434752G>A        73
+## 1684 X:g.15823131_1582313..        10
+
+vcf_path_mt <- system.file("extdata", "chrM_Input_VCF_NoMAF_Filtering.vcf", package = "sigurd")
+vcf_mt <- VariantAnnotation::readVcf(vcf_path_mt)
+vcf_mt_info <- VariantAnnotation::info(vcf_mt)
+print(vcf_mt)
+
## class: CollapsedVCF 
+## dim: 49708 0 
+## rowRanges(vcf):
+##   GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
+## info(vcf):
+##   DataFrame with 1 column: ID
+## info(header(vcf)):
+##       Number Type      Description
+##    ID A      Character Mutation   
+## geno(vcf):
+##   List of length 0:
+
+print(vcf_mt_info)
+
## DataFrame with 49708 rows and 1 column
+##                             ID
+##                <CharacterList>
+## chrM:1_G/A               1_G>A
+## chrM:3_T/A               3_T>A
+## chrM:4_C/A               4_C>A
+## chrM:6_C/A               6_C>A
+## chrM:8_G/A               8_G>A
+## ...                        ...
+## chrM:16564_A/T       16564_A>T
+## chrM:16565_C/T       16565_C>T
+## chrM:16566_G/T       16566_G>T
+## chrM:16567_A/T       16567_A>T
+## chrM:16569_G/T       16569_G>T
+
+
+

Loading and filtering the input data. +

+

We load the data per patient and merge all the associated samples +automatically. In the input file, you have to include which software +tool was used for the analysis. The source can either be vartrix or +maegatk/mgatk. The respective loading function will then only load the +files intended for it. The types of data available are: - +scRNAseq_Somatic: the standard 10X results analysed for somatic +variants. - scRNAseq_MT: the standard 10X results analysed for MT +variants. - Amplicon_Somatic: amplicon data analysed for somatic +variants. - Amplicon_MT: amplicon data analysed for MT variants.

+

Since the MT results are denser, they take longer to load.

+
+Sample1_scRNAseq_Somatic <- LoadingVarTrix_typewise(samples_file = sample_path, vcf_path = vcf_path, patient = "Sample1", type_use = "scRNAseq_Somatic")
+
## [1] "Loading the data for patient Sample1."
+## [1] "We read in the  central input file."
+## [1] "We subset to the relevant files."
+## [1] "We get the different samples."
+## [1] "We load the SNV files."
+## [1] "We read the variants."
+## [1] "We read in the cell barcodes output by CellRanger as a list."
+## [1] "We read in the vcf file."
+## [1] "We generate more accessible names."
+## [1] "We read in the different sparse genotype matrices as a list."
+## [1] "We have a slot per type of input data."
+## [1] "Loading sample 1 of 2"
+## [1] "Loading sample 2 of 2"
+## [1] "We generate a large data.frame of all the snv matrices."
+## [1] "We remove the matrix lists."
+## [1] "We remove variants, that are not detected in at least 2 cells."
+## [1] "We remove cells that are always NoCall."
+## [1] "scRNAseq_Somatic Variants: 73"
+## [1] "scRNAseq_Somatic Cells: 571"
+## [1] "We transform the sparse matrices to matrices, so we can calculate the fraction."
+## [1] "We generate a SummarizedExperiment object containing the fraction and the consensus matrices."
+
+samples_file = sample_path
+patient = "Sample1"
+type_use = "scRNAseq_MT"
+samples_path = NULL
+patient_column = "patient"
+type_use = "scRNAseq_MT"
+chromosome_prefix = "chrM"
+min_cells = 2
+barcodes_path = NULL
+cellbarcode_length = 18
+verbose = TRUE
+Sample1_scRNAseq_MT <- LoadingMAEGATK_typewise(samples_file = sample_path, patient = "Sample1", type_use = "scRNAseq_MT")
+
## [1] "Loading the data for patient Sample1."
+## [1] "We read in the central input file."
+## [1] "We subset to the relevant files."
+## [1] "We get the different samples."
+## [1] "We read in the cell barcodes output by CellRanger as a list."
+## [1] "We load the MAEGATK output files."
+## [1] "Loading sample 1 of 2"
+## [1] "Loading sample 2 of 2"
+## [1] "We merge the samples."
+## [1] "We get the allele frequency."
+## [1] "We get the coverage information."
+## [1] "We get the number of alternative reads per variant."
+## [1] "We get the quality information."
+## [1] "We get the number of reference reads."
+## [1] "Calculating the strand concordance."
+## [1] "We calculate the consensus information."
+## [1] "We perform some filtering to reduce the memory needed."
+## [1] "We remove variants, which are not covered in at least 2 cells ."
+## [1] "We remove cells that are always NoCall."
+## [1] "We add the information to the merged matrices."
+
+Sample1_combined <- CombineSEobjects(se_1 = Sample1_scRNAseq_Somatic, se_2 = Sample1_scRNAseq_MT, suffixes = c("_somatic", "_MT"))
+
+rm(Sample1_scRNAseq_Somatic, Sample1_scRNAseq_MT)
+Sample1_combined <- Filtering(Sample1_combined, min_cells_per_variant = 2, fraction_threshold = 0.05)
+
## [1] "We assume that cells with a fraction smaller than our threshold are actually NoCall."
+## [1] "We set consensus values to 0 (NoCall) and fraction values to 0."
+## [1] "We do not set fractions between 0.05 and 1 to 1."
+## [1] "This way, we retain the heterozygous information."
+## [1] "We remove all the variants that are always NoCall."
+## [1] "We remove variants, that are not at least detected in 2 cells."
+## [1] "We remove all cells that are not >= 1 (Ref) for at least 1 variant."
+
+Sample1_combined <- VariantBurden(Sample1_combined)
+
+
+

Determing MT variants of interest. +

+

This thresholding was adapted from Miller et al. https://github.com/petervangalen/MAESTER-2021 https://www.nature.com/articles/s41587-022-01210-8

+

The heatmap needs some time to plot, since the cells are +clustered.

+
+voi_ch <- VariantQuantileThresholding(SE = Sample1_combined, min_coverage = 2, quantiles = c(0.1, 0.9), thresholds = c(0.1, 0.9))
+
## [1] "We set NoCall cells as NA."
+## [1] "Get the mean allele frequency and coverage."
+## [1] "Get the quantiles of the VAFs of each variant."
+## [1] "Thresholding using the quantile approach."
+
+hm <- HeatmapVoi(SE = Sample1_combined, voi = voi_ch)
+print(hm)
+

+
+
+

Association of Variants +

+

Using Fisher’s Exact test, we find co-present variants. You can also +use the correlation between variants. For this, we combine the somatic +and the MT results. Since the possible number of tests/correlations is +quite large, you can use multiple cores to perform the calculations.

+
+#Sample1_split_rows <- RowWiseSplit(Sample1_combined, remove_nocalls = FALSE, n_cores = 19)
+
+#results_fishertest  <- VariantWiseFisherTest(Sample1_split_rows, n_cores = 19)
+#rm(Sample1_split_rows)
+
+#variant_association_heatmap <- VariantFisherTestHeatmap(results_fishertest, patient = "Sample1", min_alt_cells = 3)
+#print(variant_association_heatmap)
+
+
+
+ + + +
+ + + +
+
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