---
title: "single cell Mapper (scMappR)"
author: "Dustin Sokolowski"
date: "`r Sys.Date()`"
output: 
  rmarkdown:::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{single cell Mapper (scMappR)}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
---

## Install and load scMappR
 scMappR relies on the following dependencies which should be downloaded/updated with scMappR automatically. Please ensure that these packages are not open when installing scMappR. 

  * ggplot2 - CRAN
  * pheatmap - CRAN
  * graphics - CRAN
  * Seurat - CRAN
  * GSVA - Bioconductor
  * stats - CRAN
  * utils - CRAN
  * downloader - CRAN
  * pcaMethods - Bioconductor
  * grDevices - CRAN
  * gProfileR - CRAN
  * limSolve - CRAN 
  * ADAPTS - CRAN
  * reshape - CRAN
	

Install GSVA and pcaMethods from bioconductor first, as `devtools::install_githb()` will automatically install CRAN. 

1. Github (Development Version)


```{r install_developter, eval=FALSE}

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
if (!requireNamespace("devtools", quietly = TRUE))
    install.packages("devtools")

BiocManager::install("pcaMethods")
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")

BiocManager::install("pcaMethods")
BiocManager::install("GSVA")

install.packages("scMappR")

```

## Downloading Files.

Before using scMappR, we strongly recommend that you download data files from "https://github.com/wilsonlabgroup/scMappR_Data".Tthe `rda_path` parameter will allow users to set the directory where these data are stored. If the correct files are not stored in this directory, then scMappR will download these as temporary files from "https://github.com/wilsonlabgroup/scMappR_Data", use them in the function, and not save them. This parameter is set to "" as a default and thus data are downloaded as a default.

Additionally, the `get_signature_matrices()` function allows the user to get the list of all stored signature matrices at any given time. Input "all", "pVal", or "OR" to get the matrix of p-values, odds-ratios, or both. This function outputs the signature matrices, the top 30 cell-type markers for each cell-type, and the predicted cluster label from the Fisher's exact test and GSVA.


```{r get_signatures, eval=FALSE}

signatures <- get_signature_matrices(type = "all") #return a list of cell-type labels, p-values, and odds-ratios.


```



## Saving results.

Many of the functions in scMappR print files or  generate directories. To return the full output of scMappR, please change the 'toSave' parameters from `FALSE` to `TRUE` in any of the functions being used. Otherwise, the functions in scMappR will only return a small portion of what scMappR has to offer. Due to CRAN packages not allowing for their packages to print files/make directories, toSave is set to `FALSE` as default. Furthermore, a path directory must be set. For example `path="./"` will print files and directories to the working directory. These measures are to prevent any file overwriting. This vignette sets `path` to `tempdir()`. When using scMappR, change `tempdir()` to the directories where files should be saved.

## Introduction of the primary functions highlighted in the vignette.

#### Transforming summary statistics of differentially expressed genes by cell-type specific information
* `scMappR_and_pathway_analysis()`: This function generates cell-weighted fold changes (cellWeighted_Foldchanges), visualizes them in a heatmap, and completes pathway enrichment of cellWeighted_Foldchanges and bulk gene list.
* `two_method_pathway_enrichment()`: This function completes additional pathway enrichment tests from cwFold-changes calculated in `scMappR_and_pathway_analysis`. Specifically, it re-ranks cwFoldChanges based on their absolute ct specificity scores (per-celltype) as well as their rank increase in cell-type specificity before completing an ordered pathway analysis. In the second method, only genes with a rank increase in cell-type specificity were included.
* `cwFoldChange_evaluate()`. At the level of the gene, cwFold-changes are scored so that they sum to 1. For each gene, cell-types whose cwFold-change are greater than the cell-type proportion while accounting for an abnormally high proportion of the fold change (+ 3 median absolute deviations from median cell-type specificity) are considered cell-type specific. At the level of the cell-type, bulk DEGs and cwFold-changes for each cell-type are correlated. The difference in the rank of DEG is also measured.

#### Enriching cell-type markers in a list of genes.
* `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.

#### Processing scRNA-seq count data into a gene list.

* `process_dgTMatrix_lists()`: This function takes a list of count matrices, processes them, calls cell-types, and generates signature matrices.


## cell-weighted Fold Changes (cwFold-changes) Generation

The `scMappR_and_pathway_analysis()` function generates cellWeighted_Foldchanges based on an inputted signature matrix, normalized RNA-seq count matrix (e.g. TPM, RPKM, CPM), and list of differentially-expressed genes (DEGs) before creating cell-weighted Fold Changes (cwFold-changes). cwFold-changes are row-normalized and visualized in a heatmap. Cell-type markers within calculated cwFold-changes are also visualized both as cell-type markers on their own (i.e. just their cell-type specificity), and as cwFold-changes. Then, for each cell-type, this function filters genes expressed in each cell-type (cwFold-change > 1e-5), reorders the genes by cwFold-change, then completes an ordered pathway analysis with g:ProfileR or gprofiler2 package (user chooses). All genes detected in the bulk RNA-seq are used as the background.  \ 
The example below has `toSave = FALSE`, `up_and_downregulated = FALSE` and `internet = FALSE`. When running scMappR yourself, it is strongly recommended to set all to `TRUE`. `toSave = TRUE` allows for the printing of folders, images, and files onto a desktop/cluster. `up_and_downregulated = TRUE` repeats pathway analysis for up and down-regulated genes separately. `internet = TRUE` allows for the completion of pathway analysis altogether. Tissue-types are available in `data(scMappR_tissues)`, and the signature matrices themselves from `get_signature_matrices(type = "OR")`.

#### Example

```{r scMappR_and_pathway_analysis, eval=FALSE}

data(PBMC_scMappR) # load data example of PBMC bulk- and cell-sorted RNA-seq data

bulk_DE_cors <- PBMC_example$bulk_DE_cors # 59 sex-specific DEGs in bulk PBMC (up-regulated = female-biased)

bulk_normalized <- PBMC_example$bulk_normalized # log CPM normalized bulk RNA-seq data

odds_ratio_in <- PBMC_example$odds_ratio_in # signature matrix developed from cell-sorted RNA-seq

case_grep <- "_female" # flag for 'cases' (up-regulated), index is also acceptable

control_grep <- "_male" # flag for 'control' (down-regulated), index is also acceptable

max_proportion_change <- 10 # maximum cell-type proportion change -- this is good for cell-types that are uncomon in population and small absolute changes may yield large relative changes

theSpecies <- "human" # these RNA-seq data have human gene symbols (and are also from human)

# When running scMappR, it is strongly recommended to use scMappR_and_pathway analysis with the parameters below.
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, path = tempdir())


```

#### Output


### Saved outputs

Assuming `toSave = T` and `up_and_downregulated = T`, `scMappR_and_pathway_analysis()` will also generate a folder in your current directory with a considerable amount of data/figures.


Here, we will walk through the output of the above example sorting by name and working downwards.

  * BP_barplot: a directory with pdf's of the top 10 pathways of DEGs that have been re-normlaized by their cwFold-change.
  * Downregulated: a directory with pdf's and Rdata files of pathway and TF enrichment of downregulated DEGs re-normalized by their cwFold-change.
  * TF_barplot: a directory with pdf's of the top 10 TFs of DEGs that have been re-normlaized by their cwFold-change.
  * Upregulated: a directory with pdf's and Rdata files of pathway and TF enrichment of upregulated DEGs re-normalized by their cwFold-change.
  * bulk_pathway_enrichment: png and Rdata file of traditional g:ProfilR analysis of GOBP, REAC, and KEGG databases.
  * bulk_TF_enrichment: png and Rdata file of traditional g:ProfilR analysis of TF database.
  * scMappR_vignette_cell_proportion_changes_summary: t-tests looking at difference in estimated cell-type proporitons between conditions.
  * scMappR_vignette_celltype_proportions: Estimated cell-type proportions of all inputted samples.
  * scMappR_vignette_cellWeighted_Foldchanges: cwFold-changes of all estimated DEGs.
  * scMappR_vignette_cwFC_downregulated_DEGs: heatmap of downregulated cwFold-changes.
  * scMappR_vignette_cwFC_signature_matrix_downregulated_DEGs: heatmap of downregulated cwFold-changes overlapping with signature matrix.
  * scMappR_vignette_DEG_signature_matrix: Heatmap of signature matrix.
  * scMappR_vignette_leaveOneOut_gene_proportions: estimated cell-type proportions of all samples after each DEG is removed.
  * scMappR_vignette_reordered_pathways: g:ProfileR enrichment of DEGs after they're re-ordered by cwFold-changes.   
  * scMappR_vignette_reordered_transcription_factors: g:ProfileR enrichment of DEGs after they're re-ordered by cwFold-changes.   
  * scMappR_vignette_signature_matrix: heatmap of signature matrix.
  * scMappR_vignette_signature_matrix_downregulated_DEGs: heatmap of signature matrix overlapping downregulated DEGs.
  * scMappR_vignette_signature_matrix_upregulated_DEGs: heatmap of signature matrix overlapping upregulated DEGs.
  * scMappR_vignetteabs_cwFC: heatmap of absolute valued cwFold-changes (up- and down-regulated DEGs).
  * scMappR_vignettecwFC_signature_matrix_upregulated_DEGs: heatmap of absolute valued cwFold-changes (up- and down-regulated DEGs) overlapping with the signature matrix.
 
### Additional pathway enrichments of cwFold-changes

In addition to completing pathway enrichment of DEGs re-ranked by their cwFold-change, it may be informative to interrogate which DEGs were the most influenced by their cell-type specificity in the context of each cell-type.

#### Example

In this example, we are extending the output from the `scMappR_and_pathway_analysis` function above.

```{r two_method_pathway, eval=FALSE}

twoOutFiles <- two_method_pathway_enrichment(bulk_DE_cors, "human",
scMappR_vals = toOut$cellWeighted_Foldchange, background_genes = rownames(bulk_normalized), 
output_directory = "newfun_test",plot_names = "nonreranked_", toSave = FALSE)



```



#### Output

  * rank_increase: a list containing the rank change between DEGs and cwFold-changes. Additionally, it contains the output of g:ProfileR with genes re-ranked by eac cwFold-change.
  * non_rank_increase: the same output of pathway enrichment found in `scMappR_and_pathway_analysis`. Please refer to previous section for output.

##### Saved outputs

  * BP_barplot: Plots of the top 10 most enriched pathways by re-ranking genes by cwFold-change using g:ProfileR.
  * TF_barplot: Plots of the top 10 most enriched TFs by re-ranking genes by cwFold-change using g:ProfileR.
  * bp_reranked: Plots of the top 10 most enriched pathways by re-ordering genes based on how much the gene increases in rank between Fold-change and cwFold-change.
  * tf_reranked: Plots of the top 10 most enriched TFs by re-ordering genes based on how much the gene increases in rank between Fold-change and cwFold-change.
  * scatter_reranked: Scatter plots of the rank of cwFold-changes and regulated fold-changes of DEGs for each cell-type.
  * bulk_X_enrichment.Rdata: gProfiler results of pathway/TF enrichment of DEGs.
  * rank_increase_genes.Rdata: list showing the rank difference between Fold-changes and cwFold-changes for each cell-type.
  * reweighted_bulk_X.Rdata: List of each cell-type containing Pathway/TF enrichment of genes re-ordered by cwFold-change.
  * reweighted_reordered_X.Rdata: List of each cell-type containing Pathway/TF enrichment of genes ordered by the change in rank between fold-change and cwFold-change.
  
## Testing the cell-type specificity of cwFold-changes at the level of the gene and cell-type.

An important aspect of scMappR is to calculate which DEGs from bulk differential analysis may be differentially expressed due to a change in expression of specific cell-types. We complete this task my measuring if the cell-type specificity of each DEG contains outliers for a specific cell-type, and if the distribution of DEGs within a cell-type (measured through cwFold-changes) is different from the distribution of DEGs measured in bulk.

#### Example

In this example, we are extending the output from the `scMappR_and_pathway_analysis` function above.

```{r cwFoldChange_evaluate, eval=FALSE}


evaluated <- cwFoldChange_evaluate(toOut$cellWeighted_Foldchange, toOut$cellType_Proportions, bulk_DE_cors)


```

#### Output
  * cwFoldChange_normalized: A gene by cell-type data.frame of cwFold-changes. Here, the cwFold-change of each gene is divided by it's sum such that the cwFold-change can be represented as how much that gene is accounting for the fold-change of the DEG.
  * gene_level_investigation: A data.frame where each row is a gene. The first column is that gene's normalized cwFold-change's (see previous point) euclidean distance from 1. The following columns test if that gene's normalized cwFold-change fits a normal distribution using a Shapiro test.
  * cwFoldchange_gene_assigned: A list of cell-types. Each cell-type contains genes where scMappR would predict that the cell-type is driving the gene's expression in the researcher's bulk sample. For each gene, outliers are measured using mean + 3sd or median + 3mad depending on if the cwFold-change of the gene fit the normal distribution. As a second threshold, the cwFold-change of the DEG in the outlier cell-type needed needed to be greater than the cell-type proportion of that cell-type. The normalized cwFold-change for that gene in the assigned cell-type is also stored.
    * cwFoldchange_gene_flagged_FP: In our cell-type proportion scaling step, we multiply the fold-change of each DEG by the reciprical of the cell-type proporitons between case and controls. In the condition when there are many DEGs that do not overlap with the signature matrix and there are one or more cell-types with a very large difference in cell-type proportion. The scaling step can overpredit and assign non-celltype specific DEGs to a cell-type based on the change in cell-type proportion. These DEGs have a very predictable pattern and are stored in the `cwFoldchange_gene_flagged_FP` list. There is a possibility that these genes are not false positives, but should be inspected further by the researcher if they are to be included in downstream analysis. By default we suggest that these genes are not considered cell-type specific.
  * celltype_level_investigation: data frame where the columns are cell-type and the rows are the Pearson and Spearman correlation coefficients (and p-values) between bulk DEGs and cwFold-changes.
  * cwFoldchange_vs_bulk_rank_change: A gene by cell-type matrix populated with what rank the gene is differentially expressed given that cell-type.
  
  

```{r library_scMappR, warning=FALSE, echo = FALSE}

library(scMappR)

```

## Cell-type markers in a list of genes.
Given a tissue and an unranked list of genes (i.e. without a count matrix or summary statistics), the `tissue_scMappR_custom()` and `tissue_scMappR_internal()` functions visualizes cell-type markers contained within the gene-list. Then, they test for the enrichment of cell-types within that tissue. When there is no custom signature matrix, providing a tissue present in `get_signature_matrices(type = "pVal")` will complete cell-type specific gene visualization and enrichment for every signature matrix in the tissue.

### No custom signature matrix: 
  
#### Example

``` {r  scMappR_internal_example, eval = FALSE} 

data(POA_example) # region to preoptic area

Signature <- POA_example$POA_Rank_signature # signature matrix 

rowname <- get_gene_symbol(Signature) # get signature

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:60]

rda_path1 = "" # data directory (if it exists)

# Identify tissues available for tissue_scMappR_internal
data(scMappR_tissues)

"Hypothalamus" %in% toupper(scMappR_tissues)

internal <- tissue_scMappR_internal(genes, "mouse", output_directory = "scMappR_Test_Internal", 
tissue = "hypothalamus", rda_path = rda_path1, toSave = TRUE, path = tempdir())


```

#### Output

This function returns a list "internal". Using the example of `internal[[4]]`, the pre-optic area of the hypothalamus, you can see four objects. 

##### background_heatmap
  * genesIn: genes in the signature matrix is tested. \
  * genesNoIn: genes not in the signature matrix (empty). \
  * geneHeat: Gene x cell-type matrix containing the cell-type rank of each gene in the signature matrix. \
  * preferences: Cell-type marker genes sorted into each cell-type. 
   

##### gene_list_heatmap
  * genesIn: Inputted genes that are also in the signature matrix. 
  * genesNoIn: Inputted genes not in the signature matrix. 
  * geneHeat: Gene x cell-type matrix containing the cell-type rank for inputted genes overlaying with genes in the signature matrix. 
  * preferences: Inputted genes overlaying with signature matrix sorted into the cell-types where they're preferentially expressed.
  
##### single_celltype_preferences
  * Output of the Fisher's-exact test for cell-type enrichment of inputted genes with every cell-type. 

##### group_celltype_preference
  * Identification and statistical enrichment of groups of cell-types containing the same cell-type marker genes.
  
#### Saved directory

When `toSave == TRUE`, a directory is generated with visualization of each signature matrix, the signature matrix subsetted by input genes, and statistical enrichment.


### Custom signature matrix
`tissue_scMappR_custom()` assumes a custom signature matrix. We suggest a gene x cell-type matrix populated with the `-log10(Padj)*sign(FC)` (i.e. Rank) of the cell-type marker. We identify the cell-type marker with the `FindMarker` function from the Seurat package. The statistical test within that function is based on the biological question that the user interested in. Pre-computed signature matrices use the Wilcoxon test. If the value filling the matrix is not rank, users will need to change the `gene_cutoff` parameter to what they deem significant. If `is_pvalue == TRUE`, `tissue_scMappR_custom()` will transform the p-values into Ranks.

#### Example

```{r scMappR_custom_example, eval = FALSE} 

# Acquiring the gene list
data(POA_example)

Signature <- POA_example$POA_Rank_signature

rowname <- get_gene_symbol(Signature)

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:200]
 
#running tisue_scMappR_custom
internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F)


```

#### Output

The output is identical to that found in `tissue_scMappR_internal()` but with only one study, or the example shown above. \

If you choose to set `toSave = TRUE`. This function will return a directory with a number of relevant files. 

### Saved Outputs
  
  * _celltype_preferences.tsv: Enrichment of inputted genes on each cell-type.
  * _cell_co_preferences.tsv: Identification and enrichment of cell-types being enriched by the same genes (cell-type markers in common).
  * _custom_background_heatmap.pdf: Heatmap of all cell-type markers in each cell-type in the entire signature matrix.
  * _custom_background_preferences.pdf: All cell-type markers sorted into each cell-type.
  * _custom_genelist_heatmap.pdf: Heatmap of cell-type markers intersected with inputted gene list.
  * _custom_genelist_preferences.pdf: Cell-type markers intersected with inputted gene list sorted into each cell-type.
  
#### Saved directory

When `toSave == TRUE`, a directory is generated with visualization of each signature matrix, the signature matrix subsetted by input genes, and statistical enrichment.

  
## Tissue by cell-type enrichment.

scMappR allows for gene-set enrichment of every cell-type marker, sorted by cell-type, tissue, and study, curated while preprocessing all of the signature matrices for the functions within scMappR. The dataset may be downloaded from https://github.com/DustinSokolowski/scMappR_Data, processed into a gmt file using a number of packages (qusage, activepathways etc.) and then used with traditional gene-set-enrichment tools (GSEA, GSVA, gprofiler). Additionally, scMappR contains the `tissue_by_celltype_enrichment` function, that enriches a gene list against all cell-type markers using a Fisher's-exact test. Users can also download all cell-type markers by setting `return_gmt = TRUE` when using the function.


#### Example

Here, we will investigate the tissue x cell-type enrichment of the top 100 genes in the Preoptic area signature matrix. 


```{r tissue_ct_enrichment_example, fig.show='hide', 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)




```

### Three types of outputs.

  * tissue x CT enrichment: Always returned when using the `tissue_by_celltype_enrichment()` function. This function returns gene set enrichment compatible with `plotBP()` for all cell-type markers significantly enriched via the function.
  * gmt_file: Since the bank of signature matrices is updated monthly, it is recommended to periodically download a new human_tissue_celltype_scMappR.rda and mouse_tissue_celltype_scMappR.rda from https://github.com/DustinSokolowski/scMappR_Data. When internet is available, setting "rda_path" to "" will download the most updated pathway files directly using the downloader package. Here, if `return_gmt == TRUE`, then this downloaded gmt will be returned with the enrichment.
  * if `toSave == TRUE`, then the -log10(P_adj) of the top 10 tissue/cell-types that are enriched are plotted.
  
## Processing scRNA-seq count data into a signature matrix.

scMappR inputs a list of count matrices (of class list, dCGMatrix, or matrix) and re-processes it using the standard Seurat V4 vignette (+ removal of cells with > 2 standard deviations of mt contamination than mean). Then, it finds cell-type markers and identifies potential cell-type names using the GSVA and Fisher's exact methods on the CellMarker and Panglao databases. Finally, it creates a signature matrix of odds ratios and ranks. There are options to save the Seurat object, GSVA cell-type identities and list of cell-type markers. To identify what naming-preferences options described here: "brain", "epithelial", "endothelial", "blood", "connective","eye", "epidermis", "Digestive", "Immune", "pancreas", "liver", "reproductive", "kidney", "respiratory"

```{r process_scRNAseq_count, eval = FALSE}

data(sm)

toProcess <- list(example = sm)

tst1 <- process_dgTMatrix_lists(toProcess, name = "testProcess", species_name = "mouse",
naming_preference = "eye", rda_path = "", 
toSave = TRUE, saveSCObject = TRUE, path = tempdir())



```

It is recommended to set `toSave == TRUE`, allowing for important data objects to be saved. Here, the above function is repeated with `toSave == TRUE` and `saveSCObject == TRUE`, and the outputted files will be briefly discussed.

Here, the following objects are saved.

  * testProcess_generes.Rdata: list of cell-type markers for every cluster.
  * testProcess_or_heatmap.Rdata: signature matrix of odds ratios named from Fisher's exact test.
  * testProcess_pval_heatmap.Rdata: signature matrix of ranks named from Fisher's exact test.
  * testProcess_custom.Rdata: Processed (and integrated if necessary) Seurat object.
  * testProcess_gsva_cellname_avg_expression.Rdata: list of cell-type markers from CelllMarker and Panglao using the GSVA method as well as the average expression of each gene in each cell-type.
  * testProcess_cellLabelInfo.Rdata: matrix where each row is a cluster and each column provides information on the cell-type. Columns provide info on the cluster from seurat, the cell-type label from CellMarker and Panglao using the fisher's exact test and GSVA, and the top 30 markers per cluser.
  
#### Interpreting the signature matrix.
* Each row is a cell-type marker.
* Each column is a different cell-type. Assuming that `process_dgTMatrix_lists` was used and cell-types were predicted within scMappR, Each cell-type is labelled as followed. 
  * cluster X. "cell-type predicted by Cellmarker" and_or "cell-type predicted by panglao".
  
### Processing scRNA-seq data with multiple scRNA-seq runs.

When there are multiple scRNA-seq runs, we use the integration anchors feature to combine runs. Here, each run is a different element of the `dgTMatrix_list` parameter.

Making multiple run scRNA-seq data. As seen in many cases, the first dataset is identified with ".1" and the second with ".2"

```{r make_multi_scRNAseq, eval = FALSE}

# generating scRNA-seq data with multiple runs.
data(sm)

sm1 <- sm2 <- sm
colnames(sm1) <- paste0(colnames(sm1), ".1")
colnames(sm2) <- paste0(colnames(sm2),".2")
combined_counts <- cbind(sm1,sm2)

```

Combining datasets with the integration anchors batch correction

```{r combine_int_anchors, eval=FALSE}
toProcess <- list()
for(i in 1:2) {
  toProcess[[paste0("example",i)]] <- combined_counts[,grep(paste0(".",i), colnames(combined_counts))]
}
tst1 <- process_dgTMatrix_lists(toProcess, name = "testProcess", species_name = "mouse",
naming_preference = "eye", rda_path = "", 
toSave = TRUE, saveSCObject = TRUE, path = tempdir())


```

If you do not want to use the integration anchors feature and process the scRNA-seq data as if it were one run, then keep all of these data in the same dgCMatrix matrix.


```{r combine_nobatch, eval=FALSE}

tst1 <- process_dgTMatrix_lists(combined_counts, name = "testProcess", species_name = "mouse",
naming_preference = "eye", rda_path = "", 
toSave = TRUE, saveSCObject = TRUE, path = tempdir())


```




  
### Processing scRNA-seq count data from a different species.

Please continue to use the `process_from_count` function. The only species specific information required in this function is the mitochondrial gene symbol to regress out mitochondria. In each of your count matrices, make sure that your michondial genes have a "MT-" preface. Then select "human" as the species. This can also be done with an "mt-" preface and selecting mouse. 
  
### Processing scRNA-seq count data when cell-types are already named.

It may be common to generate a signature matrix when clusters and cell-types have already been given for every cell. These examples follow how to make this signature matrix from:

1) A Seurat object with named cell-types
2) A Count matrix with named cell-types.


### Signature matrix from Seurat object with named cell-type




Generating the Seurat Object for example and making up cell-types. This example will be used from 1-2.


```{r Seurat_Object_Generation, eval = FALSE}


data(sm)

toProcess <- list(sm = sm)

seurat_example <- process_from_count(toProcess, "test_vignette",theSpecies  = "mouse")

levels(seurat_example@active.ident) <- c("Myoblast", "Neutrophil", "cardiomyoblast", "Mesothelial")

```



1) A Seurat object with named cell-types. Markers for each cell-type are stored in the `generes` object and each signature matrix is in `gene_out`.

```{r from_seurat_object, eval = FALSE}

    generes <- seurat_to_generes(pbmc = seurat_example, test = "wilcox")

    gene_out <- generes_to_heatmap(generes, make_names = FALSE)

```

2) A count matrix with named cell-types.

```{r from_count_and_genes, eval = FALSE}

#Create the cell-type ids and matrix
Cell_type_id <- seurat_example@active.ident

count_file <- sm

rownames_example <- get_gene_symbol(count_file)

rownames(count_file) <- rownames_example$rowname

# make seurat object
seurat_example <- process_from_count(count_file, "test_vignette",theSpecies  = "mouse")

# Intersect column names (cell-types) with labelled CTs

inters <- intersect(colnames(seurat_example), names(Cell_type_id))

seurat_example_inter <- seurat_example[,inters]

Cell_type_id_inter <- Cell_type_id[inters]

seurat_example_inter@active.ident <- Cell_type_id_inter

# Making signature matrices

    generes <- seurat_to_generes(pbmc = seurat_example_inter, test = "wilcox")

    gene_out <- generes_to_heatmap(generes, make_names = FALSE)

```



## Manually making graphics.

scMappR generates heatmaps and barplots. The barplots are generated with `plotBP` and `make_TF_barplot`. The plotting code for `plotBP` is provided. Inputs are a matrix called `ordered_back_all` of -log10(padj) and term names with the column names log10 and term_name respectively.

### Barplots 



```{r plot_barplot, eval=FALSE}

# making an example matrix
term_name <- c("one", "two", "three")
log10 <- c(1.5, 4, 2.1)

ordered_back_all <- as.data.frame(cbind(term_name,log10))

#plotting
 g <- ggplot2::ggplot(ordered_back_all, ggplot2::aes(x = stats::reorder(term_name, 
        log10), y = log10)) + ggplot2::geom_bar(stat = "identity", 
        fill = "turquoise") + ggplot2::coord_flip() + ggplot2::labs(y = "-log10(Padj)", 
        x = "Gene Ontology")
    y <- g + ggplot2::theme(axis.text.x = ggplot2::element_text(face = NULL, 
        color = "black", size = 12, angle = 35), axis.text.y = ggplot2::element_text(face = NULL, 
        color = "black", size = 12, angle = 35), axis.title = ggplot2::element_text(size = 16, 
        color = "black"))
    
print(y)

```

### Heatmaps

Here, the heatmaps are for plotting cwFold-changes and cell-type proportions. The same heatmap is used so just an example of one is given.

```{r heatmap_identification, eval=FALSE}

# Generating a heatmap

# Acquiring the gene list
data(POA_example)

Signature <- POA_example$POA_Rank_signature

rowname <- get_gene_symbol(Signature)

rownames(Signature) <- rowname$rowname

genes <- rownames(Signature)[1:200]
 
#running tisue_scMappR_custom
internal <- tissue_scMappR_custom(genes,Signature,output_directory = "scMappR_Test_custom", toSave = F)

toPlot <- internal$gene_list_heatmap$geneHeat


#Plotting the heatmap

cex = 0.2 # size of genes

myheatcol <- grDevices::colorRampPalette(c("lightblue", "white", "orange"))(256)
    pheatmap::pheatmap(as.matrix(toPlot), color = myheatcol, scale = "row", fontsize_row = cex, fontsize_col = 10)
    


```