UNDER CONSTRUCTION
Testis 2 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 | 10 |
0.5 | 12 |
0.6 | 14 |
0.7 | 15 |
0.8 | 16 |
0.9 | 17 |
1 | 18 |
1.1 | 20 |
1.2 | 20 |

Look at marker genes
Spermatogonia


Spermatocytes


CySC


TE


PC


hub


Cluster Markers
cluster | Num Biomarkers per Cluster |
---|---|
0 | 359 |
1 | 392 |
2 | 683 |
3 | 1,375 |
4 | 556 |
5 | 377 |
6 | 1,615 |
7 | 501 |
8 | 1,088 |
9 | 1,918 |
10 | 286 |
11 | 570 |
12 | 1,933 |
13 | 74 |