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10x Genomics
Chromium Single Cell Gene Expression

Run Analysis

The count, aggr and reanalyze pipelines output several CSV files which contain automated secondary analysis results. A subset of these results are used to render the Analysis View in the run summary.

Dimensionality Reduction

Before clustering the cells, Principal Component Analysis (PCA) is run on the normalized filtered gene-barcode matrix to reduce the number of feature (gene) dimensions. This produces a projection of each cell onto the first N principal components. By default N=10; when running reanalyze, you can choose to increase it.

$ cd /home/jdoe/runs/sample345/outs
$ head -2 analysis/pca/10_components/projection.csv
Barcode,PC-1,PC-2,PC-3,PC-4,PC-5,PC-6,PC-7,PC-8,PC-9,PC-10
AAACATACAACGAA-1,-0.2765,-5.7056,6.5324,-12.2736,-1.4390,-1.1656,-0.1754,-2.9748,3.3785,1.6539

This also produces a components matrix which indicates how much each gene contributed to each principal component.

$ head -2 analysis/pca/10_components/components.csv
PC,ENSG00000228327,ENSG00000237491,ENSG00000177757,ENSG00000225880,...,ENSG00000160310
1,-0.0044,0.0039,-0.0024,-0.0016,...,-0.0104

This also produces 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/pca/10_components/variance.csv
PC,Proportion.Variance.Explained
1,0.0056404970744118104
2,0.0038897311237809061
3,0.0028803714818085419
4,0.0020830581822081206

We also compute the normalized dispersion of each gene, after binning genes by their mean expression across the dataset. This provides a useful measure of variability of each gene.

$ head -5 analysis/pca/10_components/dispersion.csv
Gene,Normalized.Dispersion
ENSG00000228327,2.0138970131886671
ENSG00000237491,1.3773662040549017
ENSG00000177757,-0.28102027567224191
ENSG00000225880,1.9887312950109921

Visualization

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

$ head -5 analysis/tsne/2_components/projection.csv
Barcode,TSNE-1,TSNE-2
AAACATACAACGAA-1,-13.5494,1.4674
AAACATACTACGCA-1,-2.7325,-10.6347
AAACCGTGTCTCGC-1,12.9590,-1.6369
AAACGCACAACCAC-1,-9.3585,-6.7300

Clustering

K-means clustering is then run to group cells together that have similar expression profiles, based on their projection into PCA space. K-means is run for many values of K=2,...,N where K corresponds to the number of clusters. By default N=10; when running reanalyze, you can choose to increase it. The corresponding results for each K is separated into its own directory.

$ ls analysis/kmeans
10_clusters  3_clusters  5_clusters  7_clusters  9_clusters
2_clusters   4_clusters  6_clusters  8_clusters

For each K, cellranger produces cluster assignments for each cell.

$ head -5 analysis/kmeans/3_clusters/clusters.csv
Barcode,Cluster
AAACATACAACGAA-1,2
AAACATACTACGCA-1,2
AAACCGTGTCTCGC-1,1
AAACGCACAACCAC-1,3

Differential Expression

cellranger also produces a table indicating which genes are differentially expressed in each cluster relative to the other clusters. For each gene we compute three values per cluster:

This is located in a different directory than the kmeans results, but follows the same structure, with each value of K separated into its own directory.

$ head -5 analysis/diffexp/3_clusters/differential_expression.csv

Gene ID,Gene 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

Multiple Species

If you analyzed a multi-species experiment, the analysis output will look different. For example, the human-mouse mixing experiment is run to verify system functionality. It consists of mixing approximately 600 human (HEK293T) cells and 600 mouse (3T3) cells in a 1:1 ratio.

cellranger produces a single analysis CSV file indicating whether each GEM contains only a single human cell (hg19), a single mouse cell (mm10) or multiple mouse and human cells (Multiplet).

$ cd /home/jdoe/runs/sample345/outs
$ head -5 analysis/gem_classification.csv
barcode,hg19,mm10,call
AAACATACACCTCC-1,3,815,mm10
AAACATACACCTGA-1,14,780,mm10
AAACATACACGTGT-1,2,439,mm10
AAACATACAGACTC-1,700,776,Multiplet