ScRNAseq

To not miss a post like this, sign up for my newsletter to learn computational biology and bioinformatics. I recently was interviewed by the SEQanswers forum on single-cell RNAseq analysis. In your opinion, what is the most challenging aspect of single-cell analysis? Every single-cell dataset is unique in terms of data quality and QC has to be carried out in a dataset specific manner. Cell annotation is still one of the most challenging steps.

Spatial transcriptome cellular niche analysis using 10x xenium data go to https://www.10xgenomics.com/resources/datasets There is a lung cancer and a breast cancer dataset. Let’s work on the lung cancer one. https://www.10xgenomics.com/resources/datasets/xenium-human-lung-preview-data-1-standard 37G zipped file! wget https://s3-us-west-2.amazonaws.com/10x.files/samples/xenium/1.3.0/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE/Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip unzip Xenium_Preview_Human_Lung_Cancer_With_Add_on_2_FFPE_outs.zip sudo tar xvzf cell_feature_matrix.tar.gz cell_feature_matrix/ cell_feature_matrix/barcodes.tsv.gz cell_feature_matrix/features.tsv.gz cell_feature_matrix/matrix.mtx.gz read in the data with Seurat We really only care about the cell by gene count matrix which is inside the cell_feature_matrix folder, and the cell location x,y coordinates: cells.

Single cell matrix is often represented as gene x cell in R/Seurat, but it is represented as cell x gene in python/scanpy. Let’s use a real example to show how to transpose between the two formats. The GEO accession page is at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE154763 Download the data We can use command line to download the count matrix at ftp: https://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154763/suppl/ wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154763/suppl/GSE154763_ESCA_normalized_expression.csv.gz -O ~/blog_data/GSE154763_ESCA_normalized_expression.csv.gz # decompress the file gunzip GSE154763_ESCA_normalized_expression.csv.gz # this GEO matrix is cell x gene # take a look by https://www.

This is a blog post for a series of posts on marker gene identification using machine learning methods. Read the previous posts: logistic regression and partial least square regression. This blog post will explore the tree based method: random forest and boost trees (gradient boost tree/XGboost). I highly recommend going through https://app.learney.me/maps/StatQuest for related sections by Josh Starmer. Note, all the tree based methods can be used to do both classification and regression.

I want to curate a public scRNAseq dataset from this paper Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer ffq I first tried ffq, but it gave me errors. ffq fetches metadata information from the following databases: GEO: Gene Expression Omnibus, SRA: Sequence Read Archive, EMBL-EBI: European Molecular BIology Laboratory’s European BIoinformatics Institute, DDBJ: DNA Data Bank of Japan, NIH Biosample: Biological source materials used in experimental assays, ENCODE: The Encyclopedia of DNA Elements.

I am interested in learning more on matrix factorization and its application in scRNAseq data. I want to shout out to this paper: Enter the Matrix: Factorization Uncovers Knowledge from Omics by Elana J. Fertig group. A matrix is decomposed to two matrices: the amplitude matrix and the pattern matrix. You can then do all sorts of things with the decomposed matrices. Single cell matrix is no special, one can use the matrix factorization techniques to derive interesting biological insights.

Dotplot is a nice way to visualize scRNAseq expression data across clusters. It gives information (by color) for the average expression level across cells within the cluster and the percentage (by size of the dot) of the cells express that gene within the cluster. Seurat has a nice function for that. However, it can not do the clustering for the rows and columns. David McGaughey has written a blog post using ggplot2 and ggtree from Guangchuang Yu.

When it comes to make a heatmap, ComplexHeatmap by Zuguang Gu is my favorite. Check it out! You will be amazed on how flexible it is and the documentation is in top niche. For Single-cell RNAseq, Seurat provides a DoHeatmap function using ggplot2. There are two limitations: when your genes are not in the top variable gene list, the scale.data will not have that gene and DoHeatmap will drop those genes.

It is very common to see in the scRNAseq papers that the authors compare cell type abundance across groups (e.g., treatment vs control, responder vs non-responder). Let’s create some dummy data. library(tidyverse) set.seed(23) # we have 6 treatment samples and 6 control samples, 3 clusters A,B,C # but in the treatment samples, cluster C is absent (0 cells) in sample7 sample_id<- c(paste0("sample", 1:6, "_control", rep(c("_A","_B","_C"),each = 6)), paste0("sample", 8:12, "_treatment", rep(c("_A","_B", "_C"), each = 5))) sample_id<- c(sample_id, "sample7_treatment_A", "sample7_treatment_B") cell_id<- paste0("cell", 1:20000) cell_df<- tibble::tibble(sample_id = sample(sample_id, size = length(cell_id), replace = TRUE), cell_id = cell_id) %>% tidyr::separate(sample_id, into = c("sample_id", "group", "clusterid"), sep= "") cell_num<- cell_df %>% group_by(group, cluster_id, sample_id)%>% summarize(n=n()) cell_num ## # A tibble: 35 x 4 ## # Groups: group, cluster_id [6] ## group cluster_id sample_id n ## <chr> <chr> <chr> <int> ## 1 control A sample1 551 ## 2 control A sample2 546 ## 3 control A sample3 544 ## 4 control A sample4 585 ## 5 control A sample5 588 ## 6 control A sample6 542 ## 7 control B sample1 550 ## 8 control B sample2 562 ## 9 control B sample3 574 ## 10 control B sample4 563 ## # … with 25 more rows total_cells<- cell_df %>% group_by(sample_id) %>% summarise(total = n()) total_cells ## # A tibble: 12 x 2 ## sample_id total ## <chr> <int> ## 1 sample1 1673 ## 2 sample10 1713 ## 3 sample11 1691 ## 4 sample12 1696 ## 5 sample2 1633 ## 6 sample3 1700 ## 7 sample4 1711 ## 8 sample5 1768 ## 9 sample6 1727 ## 10 sample7 1225 ## 11 sample8 1720 ## 12 sample9 1743 join the two dataframe to get percentage of cells per cluster per sample

In scanpy, there is a function to create a stacked violin plot. There is no such function in Seurat, and many people were asking for this feature. e.g. https://github.com/satijalab/seurat/issues/300 https://github.com/satijalab/seurat/issues/463 The developers have not implemented this feature yet. In this post, I am trying to make a stacked violin plot in Seurat. The idea is to create a violin plot per gene using the VlnPlot in Seurat, then customize the axis text/tick and reduce the margin for each plot and finally concatenate by cowplot::plot_grid or patchwork::wrap_plots.