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# Secondary Analysis Results

The spaceranger count pipeline outputs several CSV files which contain automated secondary analysis results. A subset of these results are used to render the Analysis View in the Web Summary.

## Dimensionality reduction

Before clustering, Principal Component Analysis (PCA) is run on the normalized filtered feature-barcode matrix to reduce the number of feature dimensions. Gene expression features are used as PCA features. The PCA analysis produces four output files.

analysis/pca
└── gene_expression_10_components
├── components.csv
├── dispersion.csv
├── features_selected.csv
├── projection.csv
└── variance.csv


The first is a projection of each spot onto the first N principal components. By default N=10.

$cd /home/jdoe/runs/sample345/outs$ column -s, -t < analysis/pca/gene_expression_10_components/projection.csv | less -S

Barcode             PC-1                   PC-2                    PC-3                   PC-4                   PC-5                    PC-6                    PC-7                   PC-8                    PC-9                    PC-10
AACACTTGGCAAGGAA-1  4.346822844040275      -9.073988527954281      -3.9348855477667715    4.4143616349096835     0.570902992727801       5.916871998370152       2.480636375841689      -0.06798408872536493    -0.19559617312320177    1.8447556106163412
AACAGGATTCATAGTT-1  -1.615594647200382     -1.4893042055593746     7.5739700779328665     -3.594441916107372     -0.34089358717427726    2.111157673540723       0.7226241085802059     -3.9462479306752436     0.7109160992468775      0.2148672225802757
AACAGGTTATTGCACC-1  11.032392266516446     -8.48766121740853       -3.061209741692746     -1.0179508777455186    -0.3086495689242125     -1.7476955635612388     -4.667269353092443     3.0867661655728873      3.177976646698517       3.4325955564744035
AACAGGTTCACCGAAG-1  0.02261690362615809    -1.1836459670547157     -0.4219683969014265    -0.9704969551004782    0.042818261398003474    0.7016418174052369      0.5984518607384657     0.4370020158231471      5.6108084569945715      -0.5928326084763261
AACAGTCAGGCTCCGC-1  23.551530490594487     1.485566122772231       -4.061849114221165     -3.572810445316029     0.7253401628543874      8.335238428414028       -0.27411229186554853   -1.419600005890016      8.151194312679634       -0.4650714219420635


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/pca/gene_expression_10_components/components.csv PC,ENSG00000228327,ENSG00000237491,ENSG00000177757,ENSG00000225880,...,ENSG00000160310 1,-0.0044,0.0039,-0.0024,-0.0016,...,-0.0104  The third 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. $ column -s, -t < analysis/pca/gene_expression_10_components/variance.csv | less -S

PC  Proportion.Variance.Explained
1   0.006020454455283148
2   0.0014744138318528535
3   0.0012400447266735174
4   0.0009462466452900335
5   0.0009012382233475119
6   0.0008795663315577918
7   0.0008772635528060896
8   0.0008770449415125795
9   0.0008671600964701859
10  0.0008598483035027898


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.

$column -s, -t < analysis/pca/gene_expression_10_components/dispersion.csv | less -S Feature Normalized.Dispersion ENSG00000187634 0.6831683505253648 ENSG00000188976 -0.14721475503619233 ENSG00000187961 2.2333235330589933 ENSG00000187583 -0.1377803092462445 ENSG00000187642 -0.4131854711145404 ENSG00000188290 -0.6689923111662834 ENSG00000187608 -1.0069025521553716 ENSG00000188157 0.1691687357833229 ENSG00000237330 2.0109141055507394 ENSG00000131591 -1.4170406794742954 ENSG00000162571 2.501396789174146  ## t-SNE For gene expression, after running PCA, t-distributed Stochastic Neighbor Embedding (t-SNE) is run to visualize spots in a 2-D space. $ column -s, -t < analysis/tsne/gene_expression_2_components/projection.csv | less -S

Barcode             TSNE-1                 TSNE-2
AACACTTGGCAAGGAA-1  1.2672117740192608     25.047625819665186
AACAGGATTCATAGTT-1  0.04778171834588573    3.116509598383599
AACAGGTTATTGCACC-1  18.80364109918134      18.684080610445474
AACAGGTTCACCGAAG-1  1.99715394789933       -9.208697881938745
AACAGTCAGGCTCCGC-1  38.15012452500775      2.0611329330125514
AACAGTCCACGCGGTG-1  -1.9209290038167077    -32.80566322209981
AACATAGTCTATCTAC-1  24.641739427754395     4.132453609694308
AACATCTTAAGGCTCA-1  22.693280619738776     -4.616978161185022
AACCAATCTGGTTGGC-1  5.883220436323025      -20.80497990643471
AACCACTGCCATAGCC-1  -8.471808255953594     -12.06184466119581
AACCAGAATCAGACGT-1  11.670881660483042     4.385137546311761


## UMAP

For gene expression, after running PCA, Uniform Manifold Approximation and Projection (UMAP) is run to visualize spots in a 2-D space.

$column -s, -t < analysis/umap/gene_expression_2_components/projection.csv | less -S Barcode UMAP-1 UMAP-2 AACACTTGGCAAGGAA-1 10.310660096919259 7.813228392659608 AACAGGATTCATAGTT-1 9.20511225151223 5.568023946107357 AACAGGTTATTGCACC-1 12.291062284438889 6.940462987013961 AACAGGTTCACCGAAG-1 9.032031636927861 7.064727855092599 AACAGTCAGGCTCCGC-1 13.326524472133555 4.742776277209383 AACAGTCCACGCGGTG-1 9.27174981223149 0.7703902647873845 AACATAGTCTATCTAC-1 11.73081758571688 4.510761419587083 AACATCTTAAGGCTCA-1 11.816231548622202 3.0618744238318683 AACCAATCTGGTTGGC-1 8.917922202723922 1.723589437141921 AACCACTGCCATAGCC-1 8.090373623491763 3.0793685741491017 AACCAGAATCAGACGT-1 12.501168063179637 5.342741923918538 AACCGCCAGACTACTT-1 8.042049094337923 2.931341403074622  ## Clustering Clustering is then run to group spots that have similar expression profiles together, based on their projection into PCA space for gene expression features. 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, and N=10 by default. The corresponding results for each K is separated into its own directory. clustering ├── gene_expression_graphclust ├── gene_expression_kmeans_10_clusters ├── gene_expression_kmeans_2_clusters ├── gene_expression_kmeans_3_clusters ├── gene_expression_kmeans_4_clusters ├── gene_expression_kmeans_5_clusters ├── gene_expression_kmeans_6_clusters ├── gene_expression_kmeans_7_clusters ├── gene_expression_kmeans_8_clusters └── gene_expression_kmeans_9_clusters  For each clustering, spaceranger produces cluster assignments for each spot. $ column -s, -t < analysis/clustering/gene_expression_kmeans_6_clusters/clusters.csv | less -S

Barcode             Cluster
AACACTTGGCAAGGAA-1  2
AACAGGATTCATAGTT-1  5
AACAGGTTATTGCACC-1  3
AACAGGTTCACCGAAG-1  2
AACAGTCAGGCTCCGC-1  6
AACAGTCCACGCGGTG-1  4
AACATAGTCTATCTAC-1  6
AACATCTTAAGGCTCA-1  4
AACCAATCTGGTTGGC-1  4
AACCACTGCCATAGCC-1  2
AACCAGAATCAGACGT-1  3
AACCGCCAGACTACTT-1  2


## Differential expression

spaceranger also produces a table indicating which features are differentially expressed in each cluster relative to all other clusters. For each feature we compute three values per cluster:

• The mean UMI counts per spot of this feature in cluster i
• The $log_{2}$ fold-change of this feature's expression in cluster i relative to all other clusters
• The p-value denoting significance of this feature's expression in cluster i relative to other clusters, adjusted to account for the number of hypotheses (i.e. number of features) being tested.

For details on the mean expression normalization and statistical test, see algorithms.

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

\$ column -s, -t < analysis/diffexp/gene_expression_kmeans_6_clusters/differential_expression.csv | less -S

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  Cluster 4 Mean Counts  Cluster 4 Log2 fold change  Cluster 4 Adjusted p value                            Cluster 5 Mean Counts  Cluster 5 Log2 fold change  Cluster 5 Adjusted p value  Cluster 6 Mean Counts  Cluster 6 Log2 fold change  Cluster 6 Adjusted p value
ENSG00000187634  SAMD11        0.029518663633325348   -0.6095322929009148         0.15858746256099554                 0.03872574484664482    0.22461461249795533         0.9833486026864531                          0.05434796766793789    0.7717892513698335          0.5539781031134247          0.04715646443259539    0.5514286743311745          0.5353925121590467                                    0.06172687981624832    1.220250418944373           0.993462403034926           0                      4.571576338266795           1
ENSG00000188976  NOC2L         0.1467335512646852     -0.0075967135760453         1                                   0.14740767393239       0.014213063751402633        1                                           0.1698373989623059     0.24460101767787146         0.7911469739812328          0.1365911383564832     -0.10554919914683936        0.9108210732531846                                    0.14402938623791275    0.13278757769403393         1                           0                      2.494819139282757           1
ENSG00000187961  KLHL17        0.04556764580290029    -0.39403627583048184        0.32049989245048643                 0.03872574484664482    -0.39782259364186334        0.8812215004145008                          0.08831544746039907    0.9220513967762671          0.33577683887500076         0.0682955691782416     0.5366894741993495          0.45292552912400735                                   0.12345375963249663    1.5026501496450981          0.9360648785130347          0                      4.037427627100276           1
ENSG00000187583  PLEKHN1       0.05617143759351231    -0.12087959886173749        0.796151857954772                   0.04247339757373948    -0.47499045416432306        0.7185491283353644                          0.06114146362643013    0.15608687925874287         0.9615028067775023          0.08618250396301916    0.6946685676302531          0.1601888957255227                                    0.04115125321083221    0.05866127667012577         1                           0                      3.8393486631072515          1
ENSG00000187642  PERM1         0.014902626300319593   -0.5455256804536939         0.3788718406454424                  0.022485916362567963   0.53064508050511            0.8934787587812238                          0.01698373989623059    0.22672844861260533         1                           0.02113910474564621    0.43327732261291274         0.8209540486632679                                    0.04115125321083221    1.8213325850288067          0.9735819926883299          0                      5.579481536352142           1
ENSG00000188290  HES4          0.18055104940771813    -0.030843629087912827       0.9373501986428783                  0.1486568915080882     -0.3279264105308233         0.6634182075223123                          0.24116910652647439    0.450053144644428           0.4526318219460056          0.2065127925151591     0.21783766191823117         0.6562367163059909                                    0.14402938623791275    -0.17663973362421137        1                           0                      2.1879192536277263          1
ENSG00000187608  ISG15         0.17969128250577662    -0.03771907517883433        0.916923410853115                   0.16614593756786328    -0.139088325241695          0.9612349344804666                          0.18002764290004428    0.010740899242568158        1                           0.2113910474564621     0.2617530372733241          0.5640702024367799                                    0.16460501284332885    -0.0006145326454807254      1                           0                      2.192458413242894           1
ENSG00000188157  AGRN          0.9044747808424738     0.12651962483140314         0.4707216413731767                  0.8494679514747897     -0.05772812832491778        1                                           0.8389967508737912     -0.07019980279983279        0.9809786650957779          0.8162946601780305     -0.11988533997079429        0.8073796843234022                                    0.576117544951651      -0.5929361291791735         1                           0                      -0.08452611213214536        1
ENSG00000237330  RNF223        0.03267114227377757    -0.5519448013479709         0.20914145009837526                 0.036227309695248386   -0.029958214587642473       1                                           0.06793495958492236    0.9765323481080515          0.3717962630568738          0.05040863439346404    0.519007196638797           0.5830747542768697                                    0.08230250642166442    1.4267012964117995          0.9579008199206683          0                      4.450651556007188           1


## Downstream analysis in R

Data structures produced by Visium can be analyzed and visualized in R. See Secondary Analysis in R for instructions.