Cell Ranger ARC2.0 (latest), printed on 12/22/2024
The multiomic secondary analysis results from Cell Ranger ARC involve the following analysis steps:
And the output of the secondary analysis resides in the out/analysis directory with the following structure:
analysis
├── clustering
│ ├── atac
│ │ ├── graphclust
│ │ │ ├── clusters.csv
│ │ │ ├── differential_accessibility.csv
│ │ │ └── differential_expression.csv
│ │ ├── kmeans_2_clusters
│ │ │ ├── clusters.csv
│ │ │ ├── differential_accessibility.csv
│ │ │ └── differential_expression.csv
│ │ ├── kmeans_3_clusters
│ │ │ ├── clusters.csv
│ │ │ ├── differential_accessibility.csv
│ │ │ └── differential_expression.csv
│ │ ├── kmeans_4_clusters
│ │ │ ├── clusters.csv
│ │ │ ├── differential_accessibility.csv
│ │ │ └── differential_expression.csv
│ │ └── kmeans_5_clusters
│ │ ├── clusters.csv
│ │ ├── differential_accessibility.csv
│ │ └── differential_expression.csv
│ └── gex
│ ├── graphclust
│ │ ├── clusters.csv
│ │ ├── differential_accessibility.csv
│ │ └── differential_expression.csv
│ ├── kmeans_2_clusters
│ │ ├── clusters.csv
│ │ ├── differential_accessibility.csv
│ │ └── differential_expression.csv
│ ├── kmeans_3_clusters
│ │ ├── clusters.csv
│ │ ├── differential_accessibility.csv
│ │ └── differential_expression.csv
│ ├── kmeans_4_clusters
│ │ ├── clusters.csv
│ │ ├── differential_accessibility.csv
│ │ └── differential_expression.csv
│ └── kmeans_5_clusters
│ ├── clusters.csv
│ ├── differential_accessibility.csv
│ └── differential_expression.csv
├── dimensionality_reduction
│ ├── atac
│ │ ├── lsa_components.csv
│ │ ├── lsa_dispersion.csv
│ │ ├── lsa_features_selected.csv
│ │ ├── lsa_projection.csv
│ │ ├── lsa_variance.csv
│ │ ├── tsne_projection.csv
│ │ └── umap_projection.csv
│ └── gex
│ ├── pca_components.csv
│ ├── pca_dispersion.csv
│ ├── pca_features_selected.csv
│ ├── pca_projection.csv
│ ├── pca_variance.csv
│ ├── tsne_projection.csv
│ └── umap_projection.csv
├── feature_linkage
│ ├── feature_linkage.bedpe
│ └── feature_linkage_matrix.h5
└── tf_analysis
├── filtered_tf_bc_matrix
│ ├── barcodes.tsv.gz
│ ├── matrix.mtx.gz
│ └── motifs.tsv
├── filtered_tf_bc_matrix.h5
└── peak_motif_mapping.bed
The primary dimensionality reduction method is Principal Component Analysis (PCA) for GEX and Latent Semantic Analysis (LSA) for ATAC. PCA is run on the normalized filtered gene-barcode matrix to reduce the number of feature (gene) dimensions. Only gene expression features are used as PCA features. Likewise, LSA is run on the normalized filtered peak-barcode matrix. PCA (LSA) analysis produces four output files in the directory analysis/dimensionality_reduction/gex/ with prefix of pca_ (lsa_) in the sub-directory gex/ ( atac/).
The first is a projection of each cell onto the first N principal components (default GEX: N=10; ATAC: N=15).
$ cd /home/jdoe/runs/sample345/outs $ head -2 analysis/dimensionality_reduction/gex/pca_projection.csv Barcode,PC-1,PC-2,PC-3,PC-4,PC-5,PC-6,PC-7,PC-8,PC-9,PC-10 AAACAGCCAAGCTAAA-1,-17.688234040781857,3.7950159394896508,0.12134779569343124,8.891889169739237,1.6561792607584174,3.3562135574248586,2.1045793835246203,-5.304589200171137,-0.5285869980603226,-2.316716491709393
$ head -2 analysis/dimensionality_reduction/atac/lsa_projection.csv Barcode,PC-1,PC-2,PC-3,PC-4,PC-5,PC-6,PC-7,PC-8,PC-9,PC-10,PC-11,PC-12,PC-13,PC-14,PC-15 AAACAGCCAAGCTAAA-1,-20.240988995299652,-9.98961195205192,-3.975713841955313,-3.6970519526233816,0.5924742121181492,0.2541630680205914,-1.8285930634181444,1.3091645487857684,-0.1932357739169616,0.09950491463448573,1.3779137917059847,-1.5110824109207137,-0.421621592950534,-0.0952461164327349,-0.20614805513560971
The second file is a components matrix which indicates how much each feature contributed (the loadings) to each principal component. Features that were not included in the PCA analysis have all of their loading values set to zero.
$ head -2 analysis/dimensionality_reduction/gex/pca_components.csv PC,ENSG00000228327,ENSG00000237491,ENSG00000177757,ENSG00000225880,...,ENSG00000160310 1,-0.0044,0.0039,-0.0024,-0.0016,...,-0.0104
$ head -2 analysis/dimensionality_reduction/atac/lsa_components.csv PC,chr1:9695143-9697582,chr1:9698212-9701041,... 1,-0.5482991923678618,-0.6374211593177428,...
The third file contains the Ensembl IDs of the features for GEX and peaks for ATAC with the highest dispersion that were selected for use in the principal component calculations.
$ head -5 analysis/dimensionality_reduction/gex/pca_features_selected.csv Feature 1,ENSG00000160223 2,ENSG00000142178 3,ENSG00000160221 4,ENSG00000272825
$ head -5 analysis/dimensionality_reduction/atac/lsa_features_selected.csv Feature 1,chr21:39348843-39349752 2,chr21:33542536-33543357 3,chr21:42009762-42010706 4,chr21:39388418-39389224
The fourth file records the proportion of total variance explained by each principal component. When choosing the number of principal components that are significant, it is useful to look at the plot of variance explained as a function of PC rank - when the numbers start to flatten out, subsequent PCs are unlikely to represent meaningful variation in the data.
$ head -5 analysis/dimensionality_reduction/gex/pca_variance.csv PC,Proportion.Variance.Explained 1,0.01009176733941617 2,0.0031696809558130652 3,0.002391878968412864 4,0.0020683529204892654
$ head -5 analysis/dimensionality_reduction/atac/lsa_variance.csv PC,Proportion.Variance.Explained 1,0.2210095789548742 2,0.03476394600838236 3,0.005925095778867349 4,0.003582945659343343
The final file lists the normalized dispersion of each feature, after binning features by their mean expression across the dataset. This provides a useful measure of variability of each feature.
$ head -5 analysis/dimensionality_reduction/gex/pca_dispersion.csv Feature,Normalized.Dispersion ENSG00000228327,2.0138970131886671 ENSG00000237491,1.3773662040549017 ENSG00000177757,-0.28102027567224191 ENSG00000225880,1.9887312950109921
$ head -5 analysis/dimensionality_reduction/atac/lsa_dispersion.csv Feature,Normalized.Dispersion chr1:9695143-9697582,0.02029960904777695 chr1:9698212-9701041,0.10379770925583033 chr1:9825253-9827762,-1.0 chr1:9829746-9830116,25.528012093307737
After running PCA or LSA, t-distributed Stochastic Neighbor Embedding (t-SNE) is run to visualize cells in a 2-D space.
$ head -5 analysis/dimensionality_reduction/atac/umap_projection.csv Barcode,TSNE-1,TSNE-2 AAACAGCCAAGCTAAA-1,9.2136315704327,-5.795182388646322 AAACAGCCAAGGTAAC-1,-10.9596148671472,17.742914355441265 AAACAGCCAGTAGGTG-1,2.869065977385947,-17.55872285065259 AAACAGCCATAATGTC-1,12.495664530357228,1.9561615760448785
$ head -5 analysis/dimensionality_reduction/gex/umap_projection.csv Barcode,TSNE-1,TSNE-2 AAACAGCCAAGCTAAA-1,11.19783100504234,-32.672655215753544 AAACAGCCAAGGTAAC-1,-24.09848935339985,0.6469769415490979 AAACAGCCAGTAGGTG-1,4.678939739563926,-27.328395716680745 AAACAGCCATAATGTC-1,21.49243070779123,-27.122233774496824
After running PCA or LSA, Uniform Manifold Approximation and Projection (UMAP) is run to visualize cells in a 2-D space.
$ head -5 analysis/dimensionality_reduction/atac/umap_projection.csv Barcode,UMAP-1,UMAP-2 AAACAGCCAAGCTAAA-1,7.3394675,-5.621648 AAACAGCCAAGGTAAC-1,-7.112387,0.9921901 AAACAGCCAGTAGGTG-1,4.2560987,-6.852242 AAACAGCCATAATGTC-1,6.77568,-5.4857235
$ head -5 analysis/dimensionality_reduction/gex/umap_projection.csv Barcode,UMAP-1,UMAP-2 AAACAGCCAAGCTAAA-1,7.312935,-7.3619266 AAACAGCCAAGGTAAC-1,-8.567425,-1.38729 AAACAGCCAGTAGGTG-1,8.221492,-6.5541673 AAACAGCCATAATGTC-1,5.5689363,-7.2709103
The ATAC and GEX data per cell barcode is sparse and clustering the data using the large number of features can help discover different cell populations in the sample. Moreover, the clustering helps us detect differentially accessible peaks or differentially expressed genes in each population.
Clustering is then run to group cells together that have similar expression profiles, based on their projection into PCA space (GEX) or LSA space (ATAC). Graph-based clustering (under graphclust) is run once as it does not require a pre-specified number of clusters. K-means (under kmeans) is run for many values of K=2,...,N, where K corresponds to the number of clusters (default N=5).
$ ls analysis/clustering/atac graphclust kmeans_2_clusters kmeans_3_clusters kmeans_4_clusters kmeans_5_clusters
For each clustering, cellranger-arc produces cluster assignments for each cell.
$ head -5 analysis/clustering/atac/kmeans_3_clusters/clusters.csv Barcode,Cluster AAACATACAACGAA-1,2 AAACATACTACGCA-1,2 AAACCGTGTCTCGC-1,1 AAACGCACAACCAC-1,3
$ head -5 analysis/clustering/gex/kmeans_3_clusters/clusters.csv Barcode,Cluster AAACATACAACGAA-1,2 AAACATACTACGCA-1,2 AAACCGTGTCTCGC-1,1 AAACGCACAACCAC-1,3
For each clustering setting generated for either ATAC or GEX matrix and by either K-means or graph clustering method, cellranger-arc then produces a table indicating which genes are differentially expressed (differential_expression.csv) and a table indicating which peaks and transcription factor motifs are differentially accessible (differential_accessibility.csv) in each cluster relative to all other clusters, as per the algorithms described here. For each feature, whether it is a gene, peak, or transcription factor motif, we compute these three values per cluster:
Both differential_expression.csv and differential_accessibility.csv are located in the same directory as the clustering results.
$ head -5 analysis/clustering/atac/graphclust/differential_expression.csv Feature ID,Feature Name,Cluster 1 Mean UMI Counts,Cluster 1 Log2 fold change,Cluster 1 Adjusted p value,Cluster 2 Mean UMI Counts,Cluster 2 Log2 fold change,Cluster 2 Adjusted p value,Cluster 3 Mean UMI Counts,Cluster 3 Log2 fold change,Cluster 3 Adjusted p value ENSG00000228327,RP11-206L10.2,0.0056858989363338264,2.6207666981569986,0.00052155805898912184,0.0,-0.75299726644507814,0.64066099091888962,0.00071455453829430329,-2.3725403666493312,0.0043023680184636837 ENSG00000237491,RP11-206L10.9,0.00012635330969630726,-0.31783275717885928,0.40959138980118809,0.0,3.8319652342760779,0.11986963938734894,0.0,0.56605908868652577,0.39910771338768203 ENSG00000177757,FAM87B,0.0,-2.9027952579000154,0.0,0.0,3.2470027335549219,0.19129034227967889,0.00071455453829430329,3.1510215894076818,0.0 ENSG00000225880,LINC00115,0.0003790599290889218,-5.71015017995762,8.4751637615375386e-28,0.20790015775229512,7.965820981010868,1.3374521290889345e-46,0.0017863863457357582,-2.2065304152104019,0.00059189960914085744
$ head -5 analysis/clustering/atac/graphclust/differential_accessibility.csv Feature ID,Feature Name,Cluster 1 Mean Counts,Cluster 1 Log2 fold change,Cluster 1 Adjusted p value,Cluster 2 Mean Counts,Cluster 2 Log2 fold change,Cluster 2 Adjusted p value,Cluster 3 Mean Counts,Cluster 3 Log2 fold change,Cluster 3 Adjusted p value chr1:9695129-9697582,chr1:9695129-9697582,0.014098403818774368,-5.823451487250574,2.2659671842098193e-06,4.185745651762137e-09,-1.3874516676069444,0.5918812904596457,1.9512762483589925,7.238430090771634,5.00258305609651e-09 chr1:9698210-9701041,chr1:9698210-9701041,0.013761153212430422,-6.1502095503083165,7.855686702156565e-07,0.046489553517204636,-3.0232327143356246,0.01647646310191049,2.2844378973176838,6.5025499776936115,4.703658999567952e-13 . . . AHR_HUMAN.H11MO.0.B,AHR_HUMAN.H11MO.0.B,1.5229979744677225e-09,-0.558490289359965,1.0,1.5229979744575502e-09,1.41990325445066,1.0,1.5229979744838465e-09,2.5 360529002402097,1.0 AIRE_HUMAN.H11MO.0.C,AIRE_HUMAN.H11MO.0.C,382.4895824324451,-1.366896997726535,0.007214824200990991,4098.191143669588,0.031632664734601475,1.0,124.229272550 17468,2.136369782757689,0.0015585067057439586
Notice that the table differential_accessibility.csv for any specific clustering includes differential analysis results for both peaks and transcription factor motifs.
The feature_linkage.bedpe file in outs/analysis/feature_linkage is a tab-delimited file containing information of feature linkages inferred from the pipeline. It follows the BEDPE specification from bedtools and can be directly loaded to the Integrative Genome Viewer (IGV). See the Feature Linkage Algorithm page for details on how Cell Ranger ARC produces feature linkages.
$ head -5 analysis/feature_linkage/feature_linkage.bedpe chr1 817064 817593 chr1 998050 998051 <FAM87B_promoter><AL645608.7> 0.3074 . . 7.3085 180722 peak-gene chr1 906622 907202 chr1 998050 998051 <AL645608.6_distal><AL645608.7> 0.3544 . . 6.1586 91138 peak-gene chr1 817064 817593 chr1 999980 1000172 <FAM87B_promoter><HES4> 0.4095 . . 13.1158 182747 peak-gene chr1 906622 907202 chr1 999980 1000172 <AL645608.6_distal><HES4> 0.4341 . . 15.8455 93164 peak-gene
The columns are defined as follows:
| Column Number | Name | Description |
|---|---|---|
| 1 | chrom1 | The name of the chromosome on which the first end of the feature exists. |
| 2 | start1 | The zero-based starting position of the first end of the feature on chrom1. |
| 3 | end1 | The zero-based ending position of the first end of the feature on chrom1. |
| 4 | chrom2 | The name of the chromosome on which the second end of the feature exists. |
| 5 | start2 | The zero-based starting position of the second end of the feature on chrom2. |
| 6 | end2 | The zero-based ending position of the second end of the feature on chrom2. |
| 7 | name | Defines the name of the linkage with the format of <name1><name2>, in which name1 and name2 are based on gene symbol or peak annotation. |
| 8 | score | Linkage correlation, ranging from -1 to 1. |
| 9 | strand1 | Set to ".". |
| 10 | strand2 | Set to ".". |
| 11 | significance | Linkage significance: -log10 p-value after multiple testing correction (false discovery rate). Capped at 299. |
| 12 | distance | Distance in base pairs from feature 2 to feature 1. |
| 13 | linkage_type | Can be "peak-peak", "gene-peak" or "peak-gene" depending on the type of gene or peak for feature 1 and feature 2. |
The distance between features in a feature linkage is defined as follows:
Note that linkage distance can be positive or negative. Positive distance means the genomic coordinates are larger in feature 2 than in feature 1. Because the symmetric nature of feature linkage, only linkages with positive or zero distances are output to feature_linkage.bedpe.
The feature_linkage_matrix.h5 file is a compressed HDF5 file containing the sparse matrices of feature linkage correlation and significance, as well as the feature references. The file hierarchy is as follows:
(root)
├── score
├── significance
├── indices
├── indptr
└── features [HDF5 group]
├─ _all_tag_keys
├─ feature_type
├─ genome
├─ id
├─ interval
└─ name
and the member specifications are as follows:
| Column | Type | Description |
|---|---|---|
score | float64 | Linkage correlation, ranging from -1 to 1. |
significance | float64 | Linkage significance: -log10 p-value after multiple testing correction (false discovery rate). Capped at 299. |
indices | int64 | CSR format index array of the matrix. |
indptr | int64 | CSR format index pointer array of the matrix. |
feature_type | string | The type of feature reference to which this feature belongs (Gene Expression or Peaks). |
genome | string | The genome reference for a given feature (e.g., "GRCh38" or "mm10"). For non-gene expression features, this entry is an empty string. |
id | string | The unique id corresponding to this feature (Ensembl gene IDs for genes or peak coordinates for peaks). |
interval | string | Specifies TSS coordinates for genes, or peak coordinates for peaks. |
name | string | A human-readable name associated with this feature (gene symbol for gene features and peak coordinates for peak features). |
The HDF5 group features contains information regarding the feature reference(s) used for the analysis. The datasets within the features group represent columns in a table containing one row per feature. Values in the feature_idx column described in the previous section provide indices into the rows of this hypothetical table.
The linkage correlation and linkage significance matrices are n_feature x n_feature sparse matrices sharing the same sparsity pattern, which is defined by indices and indptr.
Cell Ranger ARC performs a motif scan on peaks and generates a motif-barcode matrix. The output files are located at analysis/tf_analysis, including
tf_analysis ├── filtered_tf_bc_matrix │ ├── barcodes.tsv.gz │ ├── matrix.mtx.gz │ └── motifs.tsv ├── filtered_tf_bc_matrix.h5 └── peak_motif_mapping.bed
The peak_motif_mapping.bed file is a BED file containing peak coordinates and motif names as the fourth column. Each row represents the occurrence of one motif in one peak as evidenced by the motif scan; a single peak can occur multiple times associated with different motifs.
$ head -5 analysis/tf_analysis/peak_motif_mapping.bed chr1 629732 630166 MAFG::NFE2L1_MA0089.1 chr1 629732 630166 Sox5_MA0087.1 chr1 633796 634260 SHOX_MA0630.1 chr1 633796 634260 VAX2_MA0723.1 chr1 633796 634260 Sox5_MA0087.1