UNDER CONSTRUCTION
Testis 3 Individual Clustering
Normalize Data
sobj <- FindVariableFeatures(
sobj,
selection.method = "vst",
nfeatures = 2000,
verbose = FALSE
)
top20 <- head(VariableFeatures(sobj), 20)

Scale Data
all_genes <- rownames(sobj)
sobj <- ScaleData(
sobj,
features = all_genes,
vars.to.regress = "nCount_RNA",
verbose = FALSE
)
Dimensionality Reduction
Dimensional Gene Loadings

Top Genes Heatmap

JackStraw

Elbow Plot

Dimension Selection
Clustering
Resolution | Num of Clusters |
---|---|
0.4 | 11 |
0.5 | 13 |
0.6 | 13 |
0.7 | 15 |
0.8 | 16 |
0.9 | 17 |
1 | 19 |
1.1 | 18 |
1.2 | 21 |


Look at marker genes
Spermatogonia


Spermatocytes


CySC


TE


PC


hub


Cluster Markers
cluster | Num Biomarkers per Cluster |
---|---|
0 | 160 |
1 | 786 |
2 | 567 |
3 | 344 |
4 | 65 |
5 | 1,486 |
6 | 453 |
7 | 1,355 |
8 | 844 |
9 | 801 |
10 | 364 |
11 | 1,002 |
12 | 1,390 |