Single-cell (from 10X Genomics) analysis of 8 liver tumoral samples.
Single-cell (from 10X Genomics) analysis of 8 liver tumoral samples.
Samples were merged into a single Seurat dataset, which was pre-processed to remove low-quality cells, that is, those with a feature count below 1000 and above 6000, as well as cells with a fraction of mitochondrial genes higher than 10%.
Samples were merged into a single Seurat dataset, which was pre-processed to remove low-quality cells, that is, those with a feature count below 1000 and above 6000, as well as cells with a fraction of mitochondrial genes higher than 10%.
Afterwards, cells annotated as doublets with DoubletFinder were excluded as well from the analysis with Seurat.
Afterwards, cells annotated as doublets with DoubletFinder were excluded as well from the analysis with Seurat.</br>
RNA UMI-counts were normalized using a global-scaling normalization method and the Variance Stabilizing Transformations (SCTransform) was performed to scale based on the percentage of mitochondrial genes, the absolute count of RNAs in each cell, and the difference between S and G2/M cell cycle scores computed for each cell.
RNA UMI-counts were normalized using a global-scaling normalization method and the Variance Stabilizing Transformations (SCTransform) was performed to scale based on the percentage of mitochondrial genes, the absolute count of RNAs in each cell, and the difference between S and G2/M cell cycle scores computed for each cell.
A principal component analysis with 50 principal components (PCs) was performed for dimensional reduction, and a UMAP-representation as well as clusterswere computed on those reductions.
A principal component analysis with 50 principal components (PCs) was performed for dimensional reduction, and a UMAP-representation as well as clusterswere computed on those reductions.</br>
Marker genes for each cluster were obtained using the FindAllMarkers Seurat function and, consequently, clusters were manually annotated (removing a small population of undefined cells).</br>
Marker genes for each cluster were obtained using the FindAllMarkers Seurat function and, consequently, clusters were manually annotated (removing a small population of undefined cells).
Analysis of the subclusters "T and NK cells" and "APCs" was performed accordingly.</br>
Analysis of the subclusters "T and NK cells" and "APCs" was performed accordingly.
Top upregulated markers of each population were calculated based on the FindAllMarkers function and a heatmap was generated based on the top 20 upregulated genes in each cluster to represent them.
Top upregulated markers of each population were calculated based on the FindAllMarkers function and a heatmap was generated based on the top 20 upregulated genes in each cluster to represent them.
For the calculation of differentially expressed genes within individual clusters comparing the different treatment cohorts, namely control, partial responders and resistant, the FindMarker function was utilized.
For the calculation of differentially expressed genes within individual clusters comparing the different treatment cohorts, namely control, partial responders and resistant, the FindMarker function was utilized.
For GSEA the gene sets from [MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/genesets.jsp) were used.
For GSEA the gene sets from [MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/genesets.jsp) were used.
