The multiomic secondary analysis results from Cell Ranger ARC involve the following analysis steps:

• Dimensionality Reduction

  • PCA for ATAC
  • t-SNE
  • UMAP

• Cluster-based Analyses

  • Clustering
  • Differential Enrichment Analysis

• Feature Linkage

• Transcription Factor Analysis

  • Peak-motif Occurence Mappings

And the output of the secondary analysis resides in the out/analysis directory with the following structure:

Copy
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

PCA/LSA

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).

Copy
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.

Copy
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.

Copy
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.

Copy
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.

Copy
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

t-SNE

After running PCA or LSA, t-distributed Stochastic Neighbor Embedding (t-SNE) is run to visualize cells in a 2-D space.

Copy
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

UMAP

After running PCA or LSA, Uniform Manifold Approximation and Projection (UMAP) is run to visualize cells in a 2-D space.

Copy
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).

Copy
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.

Copy
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 the algorithm describes. For each feature, whether it is a gene, peak, or transcription factor motif, we compute these three values per cluster:

• The mean UMI counts per cell of this feature in cluster i
• The log2 fold-change of this feature's expression in cluster i relative to all other clusters
• The p-value denoting the significance of this feature's expression in cluster i relative to other clusters, adjusted to account for the number of hypotheses (i.e. the number of features) being tested

Both differential_expression.csv and differential_accessibility.csv are located in the same directory as the clustering results.

Copy
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.

Copy
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:

The distance between features in a feature linkage is defined as follows:

• For linkages between a gene and a peak: the base pair between the transcription start site (TSS) and the center of the peak. When a gene has multiple TSS, the position of TSS is defined as the center between the leftmost TSS and rightmost TSS.
• For linkages between two peaks: the base pairs between the centers of the two peaks.

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:

Copy
(root)
├── score
├── significance
├── indices
├── indptr
└── features [HDF5 group]
    ├─ _all_tag_keys
    ├─ feature_type
    ├─ genome
    ├─ id
    ├─ interval
    └─ name

and the member specifications are as follows:

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

• HDF5 filtered_tf_bc_matrix.h5 and MEX filtered_tf_bc_matrix, following the same format of joint feature-barcode matrix
• Peak-motif occurrence mappings BED peak_motif_mapping.bed

Copy
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.

Copy
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