Gene expression profiling experiments are extensively used to reveal biological mechanisms underlying complex processes. A primary output from global gene expression experiments, such as those enabled by RNA sequencing (RNA-seq), is a list of differentially expressed (DE) genes. In most cases, DE lists are obtained from heterogeneous samples, and do not necessarily indicate the cell type(s) where the DE occurred. While single cell RNA-seq (scRNA-seq) experiments reveal cell-type specific DE, bulk RNA-seq data is by far the most feasible to collect. Here we present single-cell Mapper (scMappR), a method that assigns cell type specificity to DE results obtained from bulk RNA-seq experiments. scMappR utilizes existing cell type proportion information from user selected scRNA-seq studies or a curated catalogue of re-processed scRNA-seq information. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned differentially expressed genes to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. This immune cell response was not readily observed in pathway analyses of the original, un-partitioned, DE list. Overall, scMappR complements traditional differential expression analysis of bulk RNA-seq.
Currently, there is only a development version. scMappR relies on the following dependencies which should be downlaoded/updated with scMappR automatically. Please ensure that these packages are not open when installing scMappR.
Install GSVA and pcaMethods from bioconductor first, as devtools::install_github() will automatically install CRAN dependencies.
```{r install_developter, eval=FALSE}
if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) if (!requireNamespace(“devtools”, quietly = TRUE)) install.packages(“devtools”)
if (!requireNamespace(“pcaMethods”, quietly = TRUE)) BiocManager::install(“pcaMethods”)
if (!requireNamespace(“GSVA”, quietly = TRUE)) BiocManager::install(“GSVA”)
devtools::install_github(“wilsonlabgroup/scMappR”)
2. CRAN (Stable Release)
```{r install_cran, eval=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
if (!requireNamespace("pcaMethods", quietly = TRUE))
BiocManager::install("pcaMethods")
if (!requireNamespace("GSVA", quietly = TRUE))
BiocManager::install("GSVA")
install.packages("scMappR")
Link to data used in scMappR: https://github.com/wilsonlabgroup/scMappR_Data
To run scMappR locally, please download all .rda files in this data download repository. In many of the functions, the “rda_path” argument can be changed to wherever you would like to download these files to. It however assumes “~/scMappR/data”.
If these functions do not detect these rda files, they will temporarily download them with the downloader R package; however, these rda files must already be downloaded to use the examples that are not automatically run.
Below describes the primary ways scMappR can contextualize gene lists and process data. It is strongly reccomended to set toSave = TRUE in functions and, when appropraite, internet = TRUE. Otherwise scMappR will not print files/directories and many of the results will not be printed.
tissue_scMappR_custom(): This function visualizes signature matrix, clusters subsetted genes, completes enrichment of individual cell-types and co-enrichment.tissue_scMappR_internal(): This function loops through every signature matrix in a particular tissue and generates heatmaps, cell-type preferences, and co-enrichment.tissue_by_celltype_enrichment(): This function completes a fishers exact test of an input list of genes against one of the two curated tissue by cell-type marker datasets from scMappR.scMappR_and_pathway_analysis(): This function generates scMappR Transformed Variables (STVs), visualies them in a heatmap, and completes pathway enrichment of STVs and bulk gene list.process_dgTMatrix_lists(): This function takes a list of count matrices, processes them, calls cell-types, and genreates signature matrices.Input a list of human or mouse gene symbols as well as a tissue of interest to intentify and visualize enriched cell-types and cell-types co-enriched by the same genes. This process is repeated for every signature matrix (i.e. scRNA-seq study) present for the inputted tissue.
```{r tissue_scMappR_internal, eval=FALSE }
data(Preoptic_Area) Signature <- POA_example\(POA_Rank_signature rowname <- get_gene_symbol(Signature) rownames(Signature) <- rowname\)rowname genes <- rownames(Signature)[1:60] rda_path1 = “~/Documents/scMappR/data” internal <- tissue_scMappR_internal(genes,“mouse”,output_directory = “scMappR_Test”, tissue = “hypothalamus”,rda_path = rda_path1, toSave = TRUE)
#### Provided signature matrix.
Complete the same process but with a signature matrix and gene list provided by the user. Here, gene symbols do not have to be human or mouse, the symbols in the list must match the signature.
```{r tissue_scMappR_custom, eval=FALSE}
data(POA_example)
Signature <- POA_example$POA_Rank_signature
rowname <- get_gene_symbol(Signature)
rownames(Signature) <- rowname$rowname
genes <- rownames(Signature)[1:200]
internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = TRUE)
While computing custom signature matrices, there are cell-type markers across tissues and studies. These markers are stored in a gmt file format and can be used for cell-type enrichment. This may be useful if there is not a particular tissue in mind. This function inputs a list of human or mouse gene symbols.
```{r tissue_by_celltype_enrichment, eval=FALSE }
data(POA_example) POA_generes <- POA_example\(POA_generes POA_OR_signature <- POA_example\)POA_OR_signature POA_Rank_signature <- POA_example\(POA_Rank_signature Signature <- POA_Rank_signature rowname <- get_gene_symbol(Signature) rownames(Signature) <- rowname\)rowname genes <- rownames(Signature)[1:100]
enriched <- tissue_by_celltype_enrichment(gene_list = genes, species = “mouse”,p_thresh = 0.05, isect_size = 3)
### Scaling and visualizing Differentially Expressed Genes from bulk RNA-seq data
This function requires a normalized count matrix from RNA-seq, a signature matrix such that there are fewer cell-types than there are samples, and a list of differentially expressed genes (with log2Fold-Change and adjusted P-value).
Then, it will calculate scMappR Transformed Values (STVs) before estimating if there are changes in cell-type proportion between samples. If `toSave = TRUE` then STV's will be visualized. Additionally, if `internet = TRUE`, scMappR will iteratively re-order DEGs based on their STV's and complete pathway analysis with g:ProfileR or gprofiler2.
```{r scMappR_and_pathway_analysis, eval=FALSE}
data(PBMC_scMappR)
bulk_DE_cors <- PBMC_example$bulk_DE_cors
bulk_normalized <- PBMC_example$bulk_normalized
odds_ratio_in <- PBMC_example$odds_ratio_in
case_grep <- "_female"
control_grep <- "_male"
max_proportion_change <- 10
theSpecies <- "human"
toOut <- scMappR_and_pathway_analysis(bulk_normalized, odds_ratio_in,
bulk_DE_cors, case_grep = case_grep,
control_grep = control_grep, rda_path = "",
max_proportion_change = 10, print_plots = TRUE,
plot_names = "scMappR_vignette_", theSpecies = "human",
output_directory = "scMappR_vignette_",
sig_matrix_size = 3000, up_and_downregulated = TRUE,
internet = TRUE, toSave = TRUE)
A matrix, dgTMatrix, or list of these matrices are inputted where the rows are genes and the columns are indiviudal cells. The gene names must be human or mouse gene symbols. If the dataset is being processed from PanglaoDB, then the rownames are GeneSymbol-ENSMBL, here, set species_name = -9.
This function returns the signature matrix and cell-type labels. If toSave = TRUE signature matrices, all cell-type markers, the average expression of genes from each cell-type, and cell-type labels from gsva are stored as files in the working directory. Additionally, if saveSCObject = TRUE, then the Seurat object is also saved in the working directory.
```{r process_dgTMatrix_lists, eval=FALSE}
data(sm) toProcess <- list(example = sm) tst1 <- process_dgTMatrix_lists(toProcess, name = “testPropcess”, species_name = -9, naming_preference = “eye”, rda_path = “~/scMappR/data”, panglao_set = “TRUE”, toSave = TRUE, saveSCObject = TRUE)
```