UNDER CONSTRUCTION
Testis 1 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 | 10 |
| 0.6 | 11 |
| 0.7 | 12 |
| 0.8 | 13 |
| 0.9 | 14 |
| 1 | 14 |
| 1.1 | 16 |
| 1.2 | 16 |

Look at marker genes
Spermatogonia


Spermatocytes


CySC


TE


PC


hub


Cluster Markers
| cluster | Num Biomarkers per Cluster |
|---|---|
| 0 | 130 |
| 1 | 617 |
| 2 | 263 |
| 3 | 2,043 |
| 4 | 495 |
| 5 | 143 |
| 6 | 455 |
| 7 | 1,175 |
| 8 | 1,266 |
| 9 | 9 |
| 10 | 1,029 |
| 11 | 1,031 |
| 12 | 633 |