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10X Genomics Single Cell RNA Sequencing report

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Document location：

summary/1_Cellranger_result/sample_sequence_stat.xlsx

Document location：

summary/1_Cellranger_result/sample_align_stat.xlsx

4.2 Cell filter & Cell clustering

4.2.1 Cell filter

Seurat allows to easily explore QC metrics and filter cells, we can visualize gene and molecule counts, plot their relationship, and exclude cells with a clear outlier number of genes detected as potential multiplets. Followed criteria were used to filter cells:

1.gene counts 500-Inf per cell.

2.UMI counts<-Inf.

3. the percentage of mitochondrial genes < 25% 25 (customized).

4.DoubletFinder for detecting doublets.

Document location：

src/summary_part/3_Cluster_result/sample_cells_filt_stat.xlsx

Document location：

summary/3_Cluster_result/Basicinfo_nGene_nUMI_pMito.png

summary/3_Cluster_result/Filter_Basicinfo_nGene_nUMI_pMito.png

4.2.2 Cell clustering

Cell clustering contains the following steps:

1)Normalizing the data

After removing unwanted cells from the dataset, the next step is to normalize the data. By default, we employ a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression.

2)PCA(Principal component analysis)

To overcome the extensive technical noise in any single gene for scRNA-seq data, Seurat clusters cells based on their PCA scores, with each PC essentially representing a ‘metagene’ that combines information across a correlated gene set.

3)Clustering cells

Seurat implements an graph-based clustering approach. Distances between the cells are calculated based on previously identified PCs. Seurat approach was heavily inspired by recent manuscripts which applied graph-based clustering approaches to scRNA-seq data - SNN-Cliq and CyTOF data – PhenoGraph. Briefly, these methods embed cells in a graph structure - for example a K-nearest neighbor (KNN) graph, with edges drawn between cells with similar gene expression patterns, and then attempt to partition this graph into highly interconnected quasi-cliques or communities. As in PhenoGraph, we first construct a KNN graph based on the euclidean distance in PCA space, and refine the edge weights between any two cells based on the shared overlap in their local neighborhoods (Jaccard distance). To cluster the cells, we apply modularity optimization techniques – SLM, to iteratively group cells together, with the goal of optimizing the standard modularity function.

4)t-SNE (t-distributed Stochastic Neighbor Embedding) visualization

Seurat continues to use t-SNE as a powerful tool to visualize and explore these datasets. The tSNE aims to place cells with similar local neighborhoods in high-dimensional space together in low-dimensional space.

Document location：

summary/3_Cluster_result/sample_cell_cluster_stat.xlsx

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summary/3_Cluster_result/sample_cell_cluster_stat.png

summary/3_Cluster_result/cluster_sample_stat.png

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summary/3_Cluster_result/tsne.png

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summary/3_Cluster_result/tsne.sample_split.png

4.3 Differentially expressed genes (up-regulation) analysis per cluster

4.3.1 Differentially expressed genes analysis

We used likelihood-ratio test to find differential expression for a single cluster, compared to all other cells. We identified differentially expressed genes as following criteria:

1) p_value ≤ 0.01

2) log2FC ≥ 0.26,log2FC means log2 fold-chage of the average expression between the two groups.

3) The percentage of cells where the gene is detected in specific cluster > 10%.

Document location：

summary/4_Markergene_result/all_cluster_gene_avg_exp.xlsx

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summary/4_Markergene_result/Markergene_list.xlsx

Document location:

summary/4_Markergene_result/Markergene_stat.xlsx

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summary/4_Markergene_result/Markergene_stat.png

Then the expression distribution of the top 10 marker genes were demonstrated by using heat map and bubble diagram et al.

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summary/4_Markergene_result/all_cluster_markers_heatmap.png

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summary/4_Markergene_result/top10_marker_plot/cluster0_top10_marker.png

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summary/4_Markergene_result/top10_marker_plot/cluster0_top10_marker_tsne.png

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summary/4_Markergene_result/top10_marker_plot/cluster0_top10_marker_dotplot.png

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summary/4_Markergene_result/top10_marker_plot/cluster0_top10_marker_RidgePlot.png

4.3.2 Differentially expressed genes GO enrichment analysis

Gene Ontology (GO) is an international standardized gene functional classification system which offers a dynamic-updated controlled vocabulary and a strictly defined concept to comprehensively describe properties of genes and their products in any organism. GO has three ontologies: molecular function, cellular component and biological process. The basic unit of GO is GO-term. Each GO term belongs to a type of ontology.

GO enrichment analysis provides all GO terms that significantly enriched in differentially expressed genes comparing to the genome background, and filter the differentially expressed genes that correspond to biological functions. Firstly all peak related genes were mapped to GO terms in the Gene Ontology database (http://www.geneontology.org/), gene numbers were calculated for every term, significantly enriched GO terms in differentially expressed genes comparing to the genome background were defined by hypergeometric test. The calculating formula of P-value is:

Here N is the number of all genes with GO annotation; n is the number of differentially expressed genes in N; M is the number of all genes that are annotated to the certain GO terms; m is the number of differentially expressed genes in M. The calculated p-value ≤ 0.05 was set as a threshold. GO terms meeting this condition were defined as significantly enriched GO terms in differentially expressed genes. This analysis was able to recognize the main biological functions that differentially expressed genes exercise.

Document location：

summary/5_Enrichment_result/1_GO_Enrichment

4.3.3 Differentially expressed genes pathway enrichment analysis

Genes usually interact with each other to play roles in certain biological functions. Pathway-based analysis helps to further understand genes biological functions. KEGG is the major public pathway-related database. Pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways in differentially expressed genes comparing with the whole genome background. The calculating formula is the same as that in GO analysis:

Here N is the number of all transcripts that with KEGG annotation, n is the number of differentially expressed genes in N, M is the number of all transcripts annotated to specific pathways, and m is number of differentially expressed genes in M. The calculated p-value ≤ 0.05 was set as a threshold. Pathways meeting this condition were defined as significantly enriched pathways in differentially expressed genes.

Document location：

summary/5_Enrichment_result/2_KEGG_Enrichment

4.4 Cell annotation (auto by SingleR)

SingleR performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

Document location：

summary/6_SingleR_result/cell_annot_HPCA_label.main_cell_pie.png

Document location：

summary/6_SingleR_result/cell_annot_HPCA_label.main_cell_tsne.png

Notice:

The cell types which included in the HPCA database of SingleR were limited. Some other tools/methods (such as marker gene database-based, correlation-based, supervised classification-based) were suggested to employ in order to identify the tissue specific- or subcellular- cell types.

5.References

[1] CellRanger：http://support.10xgenomics.com/single-cell/software/overview/welcome.

[2] Seurat：Butler A , Hoffman P , Smibert P , et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species[J]. Nature Biotechnology, 2018.

[3] bimod：Mcdavid A , Finak G , Chattopadyay P K , et al. Data Exploration, Quality Control and Testing in Single-Cell qPCR-Based Gene Expression Experiments[J]. Bioinformatics, 2013, 29(4):461-467.

[4] t-SNE：Maaten L V D, Hinton G. Visualizing Data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9(2605):2579-2605.

[5] Kanehisa, M., M. Araki, et al. (2008). KEGG for linking genomes to life and the environment. Nucleic acids research.(KEGG)

[6] Zheng G X, Terry J M, Belgrader P, et al. Massively parallel digital transcriptional profiling of single cells[J]. Nature Communications, 2017, 8:14049.

[7] Cannoodt R, Saelens W, Saeys Y. Computational methods for trajectory inference from single‐cell transcriptomics[J]. European Journal of Immunology, 2016, 46(11):2496-2506.

6.Contact us

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Website:http://www.lcsciences.com/

Email: support@lcsciences.com

Tel: (713) 664-7087