Introductioon In scRNA-seq data analysis, one of the most crucial and demanding tasks is determining the optimal resolution and cluster number. Achieving an appropriate balance between over-clustering and under-clustering is often intricate, as it directly impacts the identification of distinct cell populations and biological insights.
The clustering algorithms have many parameters to tune and it can generate more clusters if e.g., you increase the resolution parameter. However, whether the newly generated clusters are meaningful or not is a question.
In my last blog post, I showed that pearson gene correlation for single-cell data has flaws because of the sparsity of the count matrix.
One way to get around it is to use the so called meta-cell. One can use KNN to find the K nearest neighbors and collapse them into a meta-cell. You can implement it from scratch, but one should not re-invent the wheel. For example, you can use metacells.
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.
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
Seurat is great for scRNAseq analysis and it provides many easy-to-use ggplot2 wrappers for visualization. However, this brings the cost of flexibility. For example, In FeaturePlot, one can specify multiple genes and also split.by to further split to multiple the conditions in the meta.data. If split.by is not NULL, the ncol is ignored so you can not arrange the grid.
This is best to understand with an example.
library(dplyr) library(Seurat) library(patchwork) library(ggplot2) # Load the PBMC dataset pbmc.