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default the plotHeatmap function does some clustering using hdbscan if neither of these is provided.

-cl <- umap_clustering(hscn$data)
+cl <- umap_clustering(hscn$data)
 plotHeatmap(hscn, 
             plotcol = "state", 
diff --git a/articles/ASCN_files/figure-html/unnamed-chunk-10-1.png b/articles/ASCN_files/figure-html/unnamed-chunk-10-1.png
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  • @@ -113,18 +113,18 @@

    Inference#> Bin size: 0.5 Mb #> Inferred LOH error rate: 0.018 #> Emission model for HMM: binomial -#> Average distance from median to expected BAF = 0.0026 +#> Average distance from median to expected BAF = 0.0025 #> Average ploidy = 4 #> Average number of segments = 79 hscn_bb <- callHaplotypeSpecificCN(CNbins, haplotypes, likelihood = "betabinomial") -#> VGLM linear loop 1 : loglikelihood = -313123.2075 -#> VGLM linear loop 2 : loglikelihood = -309168.046 -#> VGLM linear loop 3 : loglikelihood = -308617.872 -#> VGLM linear loop 4 : loglikelihood = -308592.1148 -#> VGLM linear loop 5 : loglikelihood = -308591.7336 -#> VGLM linear loop 6 : loglikelihood = -308591.7302 -#> VGLM linear loop 7 : loglikelihood = -308591.7301 +#> VGLM linear loop 1 : loglikelihood = -313153.5548 +#> VGLM linear loop 2 : loglikelihood = -309219.4605 +#> VGLM linear loop 3 : loglikelihood = -308675.2147 +#> VGLM linear loop 4 : loglikelihood = -308650.0018 +#> VGLM linear loop 5 : loglikelihood = -308649.6334 +#> VGLM linear loop 6 : loglikelihood = -308649.6301 +#> VGLM linear loop 7 : loglikelihood = -308649.6301 print(hscn_bb) #> Haplotype specific copy number object @@ -133,8 +133,8 @@

    Inference#> Bin size: 0.5 Mb #> Inferred LOH error rate: 0.018 #> Emission model for HMM: betabinomial -#> Inferred over dispersion: 0.0096 -#> Tarones Z score: 180.826 +#> Inferred over dispersion: 0.0097 +#> Tarones Z score: 182.22 #> Average distance from median to expected BAF = 0.0026 #> Average ploidy = 4 #> Average number of segments = 79 @@ -173,19 +173,19 @@

    QC plotprint(dim(hscn_bb$data)) #> [1] 1093750 20 all.equal(orderdf(hscn$data), orderdf(hscn_bb$data)) -#> [1] "Component \"alleleA\": 'is.NA' value mismatch: 17582 in current 17518 in target" -#> [2] "Component \"alleleB\": 'is.NA' value mismatch: 17582 in current 17518 in target" -#> [3] "Component \"totalcounts\": 'is.NA' value mismatch: 17582 in current 17518 in target" -#> [4] "Component \"BAF\": 'is.NA' value mismatch: 17582 in current 17518 in target" -#> [5] "Component \"state_min\": Mean relative difference: 0.6628452" -#> [6] "Component \"A\": Mean relative difference: 0.5870447" -#> [7] "Component \"B\": Mean relative difference: 0.7499201" -#> [8] "Component \"state_AS_phased\": 35094 string mismatches" -#> [9] "Component \"state_AS\": 3617 string mismatches" -#> [10] "Component \"LOH\": 2545 string mismatches" -#> [11] "Component \"phase\": 31925 string mismatches" -#> [12] "Component \"state_phase\": 33061 string mismatches" -#> [13] "Component \"state_BAF\": Mean relative difference: 1.106852" +#> [1] "Component \"alleleA\": 'is.NA' value mismatch: 17542 in current 17544 in target" +#> [2] "Component \"alleleB\": 'is.NA' value mismatch: 17542 in current 17544 in target" +#> [3] "Component \"totalcounts\": 'is.NA' value mismatch: 17542 in current 17544 in target" +#> [4] "Component \"BAF\": 'is.NA' value mismatch: 17542 in current 17544 in target" +#> [5] "Component \"state_min\": Mean relative difference: 0.6085515" +#> [6] "Component \"A\": Mean relative difference: 0.6315401" +#> [7] "Component \"B\": Mean relative difference: 0.6422143" +#> [8] "Component \"state_AS_phased\": 33303 string mismatches" +#> [9] "Component \"state_AS\": 2834 string mismatches" +#> [10] "Component \"LOH\": 1824 string mismatches" +#> [11] "Component \"phase\": 30315 string mismatches" +#> [12] "Component \"state_phase\": 31284 string mismatches" +#> [13] "Component \"state_BAF\": Mean relative difference: 0.846035"

    Another thing we can check is the total number of segments identified by the HMM. Using the beta-binomial model should reduce the influence of any noisy regions and thus we might expect fewer segments. We do observe @@ -196,9 +196,9 @@

    QC plotsegs_bb <- create_segments(hscn_bb$data, field = "state_AS_phased") print(dim(segs)) -#> [1] 38704 5 +#> [1] 38937 5 print(dim(segs_bb)) -#> [1] 35291 5 +#> [1] 35352 5
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znLk}@Tw_LE?X@>nMiolT)>DB9;D22x@CT~@uRlx<_KsRbx5oYMC;d`A&?jng1&>VL F{2%p$;1U1; diff --git a/articles/basic_scdnaseq.html b/articles/basic_scdnaseq.html index 6cad73e..a3c1082 100644 --- a/articles/basic_scdnaseq.html +++ b/articles/basic_scdnaseq.html @@ -32,7 +32,7 @@ signals - 0.11.0 + 0.11.1
    @@ -136,7 +136,7 @@

    Single cell profiles
    -clustering <- umap_clustering(CNbins, field = "copy")
    +clustering <-
    umap_clustering(CNbins, field = "copy")

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    diff --git a/authors.html b/authors.html index 14705ab..e96e685 100644 --- a/authors.html +++ b/authors.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 @@ -76,13 +76,13 @@

    Citation

    Williams MJ, Funnell T (2024). signals: Single Cell Genomes with Allele Specificity. -R package version 0.11.0. +R package version 0.11.1.

    @Manual{,
       title = {signals: Single Cell Genomes with Allele Specificity},
       author = {Marc J Williams and Tyler Funnell},
       year = {2024},
    -  note = {R package version 0.11.0},
    +  note = {R package version 0.11.1},
     }
    diff --git a/index.html b/index.html index 60cb82d..1f4104a 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/news/index.html b/news/index.html index f997ecd..124d58c 100644 --- a/news/index.html +++ b/news/index.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 @@ -60,6 +60,10 @@

    Changelog

    +
    + +
    • for umap_clustering, default is now to not use PCA
    • +
    • updates to plotting
    • diff --git a/pkgdown.yml b/pkgdown.yml index 26c68aa..7d0e4d8 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -6,4 +6,4 @@ articles: ASCN: ASCN.html basic_scdnaseq: basic_scdnaseq.html BetaBinom: BetaBinom.html -last_built: 2024-10-07T13:19Z +last_built: 2024-10-17T13:16Z diff --git a/reference/add_gene_locations_to_seurat.html b/reference/add_gene_locations_to_seurat.html index c43b7a2..30496fb 100644 --- a/reference/add_gene_locations_to_seurat.html +++ b/reference/add_gene_locations_to_seurat.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1
    diff --git a/reference/callAlleleSpecificCN.html b/reference/callAlleleSpecificCN.html index f1224ee..19c49d0 100644 --- a/reference/callAlleleSpecificCN.html +++ b/reference/callAlleleSpecificCN.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/callAlleleSpecificCNfromHSCN.html b/reference/callAlleleSpecificCNfromHSCN.html index 76f8d34..685c5c1 100644 --- a/reference/callAlleleSpecificCNfromHSCN.html +++ b/reference/callAlleleSpecificCNfromHSCN.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/callHaplotypeSpecificCN.html b/reference/callHaplotypeSpecificCN.html index 07f6d5d..552f4a6 100644 --- a/reference/callHaplotypeSpecificCN.html +++ b/reference/callHaplotypeSpecificCN.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 @@ -285,8 +285,6 @@

    Examples

    #> Clustering chromosome 1 #> Creating CN matrix... #> Calculating UMAP dimensionality reduction... -#> An error occurred in umap calculation: Initial data contains NA values: is n_components too high? -#> Rerunning UMAP after adding small jitter to data points... #> Clustering cells using hdbscan... #> Identified 2 clusters #> Distribution of clusters: @@ -393,8 +391,6 @@

    Examples

    #> Clustering chromosome 3 #> Creating CN matrix... #> Calculating UMAP dimensionality reduction... -#> An error occurred in umap calculation: Initial data contains NA values: is n_components too high? -#> Rerunning UMAP after adding small jitter to data points... #> Clustering cells using hdbscan... #> Identified 2 clusters #> Distribution of clusters: @@ -410,8 +406,6 @@

    Examples

    #> Clustering chromosome 5 #> Creating CN matrix... #> Calculating UMAP dimensionality reduction... -#> An error occurred in umap calculation: Initial data contains NA values: is n_components too high? -#> Rerunning UMAP after adding small jitter to data points... #> Clustering cells using hdbscan... #> Identified 2 clusters #> Distribution of clusters: diff --git a/reference/coord_to_arm.html b/reference/coord_to_arm.html index af57399..ede8c5a 100644 --- a/reference/coord_to_arm.html +++ b/reference/coord_to_arm.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/getBins.html b/reference/getBins.html index acd8be5..3a14d71 100644 --- a/reference/getBins.html +++ b/reference/getBins.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/index.html b/reference/index.html index 3d5a838..4e30dc9 100644 --- a/reference/index.html +++ b/reference/index.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 @@ -119,6 +119,10 @@

    All functions scale_fill_scCN()

    Fill scale constructor for scCN (single cell Copy Number) colors

    + +

    umap_clustering()

    + +

    Perform UMAP dimensionality reduction and HDBSCAN clustering on copy number data

    diff --git a/reference/plotBAFperstate.html b/reference/plotBAFperstate.html index 8ff03c8..21d4084 100644 --- a/reference/plotBAFperstate.html +++ b/reference/plotBAFperstate.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/plotCNprofile.html b/reference/plotCNprofile.html index 21c47ee..1a79518 100644 --- a/reference/plotCNprofile.html +++ b/reference/plotCNprofile.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/plotCNprofileBAF.html b/reference/plotCNprofileBAF.html index 4f98017..4a32cf7 100644 --- a/reference/plotCNprofileBAF.html +++ b/reference/plotCNprofileBAF.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/plotHeatmap.html b/reference/plotHeatmap.html index e5c9d4f..3a1ff4b 100644 --- a/reference/plotHeatmap.html +++ b/reference/plotHeatmap.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/plot_clusters_used_for_phasing.html b/reference/plot_clusters_used_for_phasing.html index 0ac5908..2bc756f 100644 --- a/reference/plot_clusters_used_for_phasing.html +++ b/reference/plot_clusters_used_for_phasing.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/rephasebins.html b/reference/rephasebins.html index 1d1cea3..4d32be1 100644 --- a/reference/rephasebins.html +++ b/reference/rephasebins.html @@ -19,7 +19,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/scale_color_scCN.html b/reference/scale_color_scCN.html index a4303e2..553ab7d 100644 --- a/reference/scale_color_scCN.html +++ b/reference/scale_color_scCN.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/scale_fill_scCN.html b/reference/scale_fill_scCN.html index d023678..859dd85 100644 --- a/reference/scale_fill_scCN.html +++ b/reference/scale_fill_scCN.html @@ -17,7 +17,7 @@ signals - 0.11.0 + 0.11.1 diff --git a/reference/umap_clustering.html b/reference/umap_clustering.html new file mode 100644 index 0000000..a07a220 --- /dev/null +++ b/reference/umap_clustering.html @@ -0,0 +1,180 @@ + +Perform UMAP dimensionality reduction and HDBSCAN clustering on copy number data — umap_clustering • signals + + +
    +
    + + + +
    +
    + + +
    +

    This function takes copy number data, performs UMAP dimensionality reduction, +and then applies HDBSCAN clustering to identify cell populations. It can handle +both standard copy number data and haplotype-specific copy number (HSCN) data.

    +
    + +
    +
    umap_clustering(
    +  CNbins,
    +  n_neighbors = 10,
    +  min_dist = 0.1,
    +  minPts = 30,
    +  seed = NULL,
    +  field = "copy",
    +  umapmetric = "correlation",
    +  hscn = FALSE,
    +  pca = NULL
    +)
    +
    + +
    +

    Arguments

    + + +
    CNbins
    +

    A data frame containing copy number data. Must include columns +for 'cell_id' and the specified `field`.

    + + +
    n_neighbors
    +

    Integer. The number of neighbors to consider in UMAP. Default is 10.

    + + +
    min_dist
    +

    Numeric. The minimum distance between points in UMAP. Default is 0.1.

    + + +
    minPts
    +

    Integer. The minimum number of points to form a cluster in HDBSCAN. Default is 30.

    + + +
    seed
    +

    Integer or NULL. Random seed for reproducibility. Default is NULL.

    + + +
    field
    +

    Character. The column name in `CNbins` to use for copy number values. Default is "copy".

    + + +
    umapmetric
    +

    Character. The distance metric to use in UMAP. Default is "correlation".

    + + +
    hscn
    +

    Logical. Whether to use haplotype-specific copy number data. Default is FALSE.

    + + +
    pca
    +

    Integer or NULL. Number of principal components to use in UMAP. If NULL, pca not used, this is the default.

    + +
    +
    +

    Value

    +

    A list containing:

    +
    clustering
    +

    A data frame with UMAP coordinates and cluster assignments for each cell.

    + +
    hdbscanresults
    +

    The results of the HDBSCAN clustering.

    + +
    umapresults
    +

    The results of the UMAP dimensionality reduction.

    + +
    tree
    +

    A phylogenetic tree object representing the hierarchical structure of the clusters.

    + +
    +
    +

    Details

    +

    The function performs the following steps: +1. Creates a copy number matrix from the input data. +2. Applies UMAP dimensionality reduction. +3. Performs HDBSCAN clustering on the UMAP results. +4. Generates a phylogenetic tree from the clustering results.

    +

    If `hscn` is TRUE, the function expects columns 'copy' and 'BAF' in `CNbins`, +and creates separate matrices for A and B alleles.

    +

    The function automatically adjusts `n_neighbors` if there are too few cells. +If UMAP fails, it attempts to rerun with small jitter added to the data points. +The function will reduce `minPts` if only one cluster is initially found.

    +
    + +
    + +
    + + +
    + +
    +

    Site built with pkgdown 2.1.1.

    +
    + +
    + + + + + + + + diff --git a/sitemap.xml b/sitemap.xml index 85a7762..a07ed13 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -26,5 +26,6 @@ /reference/rephasebins.html /reference/scale_color_scCN.html /reference/scale_fill_scCN.html +/reference/umap_clustering.html

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