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.
This is an extension of my last blog post marker gene selection using logistic regression and regularization for scRNAseq.
Let’s use the same PBMC single-cell RNAseq data as an example.
library(Seurat) library(tidyverse) library(tidymodels) library(scCustomize) # for plotting library(patchwork) Preprocess the data
Load the PBMC dataset pbmc.data <- Read10X(data.dir = "~/blog_data/filtered_gene_bc_matrices/hg19/") # Initialize the Seurat object with the raw (non-normalized data). pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.
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.
why this blog post? I saw a biorxiv paper titled A comparison of marker gene selection methods for single-cell RNA sequencing data
Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test and logistic regression
I am interested in using logistic regression to find marker genes and want to try fitting the model in the tidymodel ecosystem and using different regularization methods.
There are so many public datasets there waiting for us to mine! It is the blessing and cursing as a computational biologist!
Metadata, or the data describing (e.g., responder or non-responder for the treatment) the data are critical in interpreting the analysis. Without metadata, your data are useless.
People usually go to GEO or ENA to download public data. I asked this question on twitter, and I will show you how to get the metadata as suggested by all the awesome tweeps.
This is going to be a really short blog post. I recently found that if I join two tables with one of the tables having duplicated rows, the final joined table also contains the duplicated rows. It could be the expected behavior for others but I want to make a note here for myself.
library(tidyverse) df1<- tibble(key = c("A", "B", "C", "D", "E"), value = 1:5) df1 ## # A tibble: 5 x 2 ## key value ## <chr> <int> ## 1 A 1 ## 2 B 2 ## 3 C 3 ## 4 D 4 ## 5 E 5 dataframe 2 has two identical rows for B.
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.
I asked this question on Twitter:
what test to test if two distributions are different? I am aware of KS test. When n is large (which is common in genomic studies), the p-value is always significant. better to test against an effect size? how to do it in this context?
In genomics studies, it is very common to have large N (e.g., the number of introns, promoters in the genome, number of cells in the single-cell studies).
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.
It is the end of the year again and it is a good time to review my 2020. I wrote one for 2019. It has been a tough year for many of us. It is the same for me. I switched my job from Harvard FAS informatics to Department of Data Science at Dana-Farber Cancer Institute during the shutdown because of COVID19 at the end of March and has been working from home since then.