RetroSearch Browse

Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://cran.r-project.org/web/packages/rms/../Matrix/../tidyseurat/readme/README.html below:

README

tidyseurat - part of tidytranscriptomics

Brings Seurat to the tidyverse!

website: stemangiola.github.io/tidyseurat/

Please also have a look at

tidyseurat for tidy single-cell RNA sequencing analysis
tidySummarizedExperiment for tidy bulk RNA sequencing analysis
tidybulk for tidy bulk RNA-seq analysis
nanny for tidy high-level data analysis and manipulation
tidygate for adding custom gate information to your tibble
tidyHeatmap for heatmaps produced with tidy principles

visual cue Introduction

tidyseurat provides a bridge between the Seurat single-cell package [@butler2018integrating; @stuart2019comprehensive] and the tidyverse [@wickham2019welcome]. It creates an invisible layer that enables viewing the Seurat object as a tidyverse tibble, and provides Seurat-compatible dplyr, tidyr, ggplot and plotly functions.

Functions/utilities available dplyr All dplyr APIs like for any tibble tidyr All tidyr APIs like for any tibble ggplot2 ggplot like for any tibble plotly plot_ly like for any tibble tidy Add tidyseurat invisible layer over a Seurat object as_tibble Convert cell-wise information to a tbl_df join_features Add feature-wise information, returns a tbl_df aggregate_cells Aggregate cell gene-transcription abundance as pseudobulk tissue Installation

From CRAN

From Github (development)

Create tidyseurat, the best of both worlds!

This is a seurat object but it is evaluated as tibble. So it is fully compatible both with Seurat and tidyverse APIs.

It looks like a tibble

## # A Seurat-tibble abstraction: 80 Ã 15
## # [90mFeatures=230 | Cells=80 | Active assay=RNA | Assays=RNA[0m
##    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
##    <chr> <fct>           <dbl>        <int> <fct>           <fct>         <chr> 
##  1 ATGCâ¦ SeuratProâ¦         70           47 0               A             g2    
##  2 CATGâ¦ SeuratProâ¦         85           52 0               A             g1    
##  3 GAACâ¦ SeuratProâ¦         87           50 1               B             g2    
##  4 TGACâ¦ SeuratProâ¦        127           56 0               A             g2    
##  5 AGTCâ¦ SeuratProâ¦        173           53 0               A             g2    
##  6 TCTGâ¦ SeuratProâ¦         70           48 0               A             g1    
##  7 TGGTâ¦ SeuratProâ¦         64           36 0               A             g1    
##  8 GCAGâ¦ SeuratProâ¦         72           45 0               A             g1    
##  9 GATAâ¦ SeuratProâ¦         52           36 0               A             g1    
## 10 AATGâ¦ SeuratProâ¦        100           41 0               A             g1    
## # â¹ 70 more rows
## # â¹ 8 more variables: RNA_snn_res.1 <fct>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>,
## #   PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>

But it is a Seurat object after all

## $RNA
## Assay data with 230 features for 80 cells
## Top 10 variable features:
##  PPBP, IGLL5, VDAC3, CD1C, AKR1C3, PF4, MYL9, GNLY, TREML1, CA2

Preliminary plots

Set colours and theme for plots.

# Use colourblind-friendly colours
friendly_cols <- c("#88CCEE", "#CC6677", "#DDCC77", "#117733", "#332288", "#AA4499", "#44AA99", "#999933", "#882255", "#661100", "#6699CC")

# Set theme
my_theme <-
  list(
    scale_fill_manual(values = friendly_cols),
    scale_color_manual(values = friendly_cols),
    theme_bw() +
      theme(
        panel.border = element_blank(),
        axis.line = element_line(),
        panel.grid.major = element_line(size = 0.2),
        panel.grid.minor = element_line(size = 0.1),
        text = element_text(size = 12),
        legend.position = "bottom",
        aspect.ratio = 1,
        strip.background = element_blank(),
        axis.title.x = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
        axis.title.y = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10))
      )
  )

We can treat pbmc_small effectively as a normal tibble for plotting.

Here we plot number of features per cell.

Here we plot total features per cell.

Here we plot abundance of two features for each group.

Preprocess the dataset

Also you can treat the object as Seurat object and proceed with data processing.

## # A Seurat-tibble abstraction: 80 Ã 17
## # [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[0m
##    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
##    <chr> <fct>           <dbl>        <int> <fct>           <fct>         <chr> 
##  1 ATGCâ¦ SeuratProâ¦         70           47 0               A             g2    
##  2 CATGâ¦ SeuratProâ¦         85           52 0               A             g1    
##  3 GAACâ¦ SeuratProâ¦         87           50 1               B             g2    
##  4 TGACâ¦ SeuratProâ¦        127           56 0               A             g2    
##  5 AGTCâ¦ SeuratProâ¦        173           53 0               A             g2    
##  6 TCTGâ¦ SeuratProâ¦         70           48 0               A             g1    
##  7 TGGTâ¦ SeuratProâ¦         64           36 0               A             g1    
##  8 GCAGâ¦ SeuratProâ¦         72           45 0               A             g1    
##  9 GATAâ¦ SeuratProâ¦         52           36 0               A             g1    
## 10 AATGâ¦ SeuratProâ¦        100           41 0               A             g1    
## # â¹ 70 more rows
## # â¹ 10 more variables: RNA_snn_res.1 <fct>, nCount_SCT <dbl>,
## #   nFeature_SCT <int>, PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>,
## #   PC_5 <dbl>, tSNE_1 <dbl>, tSNE_2 <dbl>

If a tool is not included in the tidyseurat collection, we can use as_tibble to permanently convert tidyseurat into tibble.

Identify clusters

We proceed with cluster identification with Seurat.

## # A Seurat-tibble abstraction: 80 Ã 19
## # [90mFeatures=220 | Cells=80 | Active assay=SCT | Assays=RNA, SCT[0m
##    .cell orig.ident nCount_RNA nFeature_RNA RNA_snn_res.0.8 letter.idents groups
##    <chr> <fct>           <dbl>        <int> <fct>           <fct>         <chr> 
##  1 ATGCâ¦ SeuratProâ¦         70           47 0               A             g2    
##  2 CATGâ¦ SeuratProâ¦         85           52 0               A             g1    
##  3 GAACâ¦ SeuratProâ¦         87           50 1               B             g2    
##  4 TGACâ¦ SeuratProâ¦        127           56 0               A             g2    
##  5 AGTCâ¦ SeuratProâ¦        173           53 0               A             g2    
##  6 TCTGâ¦ SeuratProâ¦         70           48 0               A             g1    
##  7 TGGTâ¦ SeuratProâ¦         64           36 0               A             g1    
##  8 GCAGâ¦ SeuratProâ¦         72           45 0               A             g1    
##  9 GATAâ¦ SeuratProâ¦         52           36 0               A             g1    
## 10 AATGâ¦ SeuratProâ¦        100           41 0               A             g1    
## # â¹ 70 more rows
## # â¹ 12 more variables: RNA_snn_res.1 <fct>, nCount_SCT <dbl>,
## #   nFeature_SCT <int>, SCT_snn_res.0.8 <fct>, seurat_clusters <fct>,
## #   PC_1 <dbl>, PC_2 <dbl>, PC_3 <dbl>, PC_4 <dbl>, PC_5 <dbl>, tSNE_1 <dbl>,
## #   tSNE_2 <dbl>

Now we can interrogate the object as if it was a regular tibble data frame.

## # A tibble: 6 Ã 3
##   groups seurat_clusters     n
##   <chr>  <fct>           <int>
## 1 g1     0                  23
## 2 g1     1                  17
## 3 g1     2                   4
## 4 g2     0                  17
## 5 g2     1                  13
## 6 g2     2                   6

We can identify cluster markers using Seurat.

Reduce dimensions

We can calculate the first 3 UMAP dimensions using the Seurat framework.

And we can plot them using 3D plot using plotly.

screenshot plotly Cell type prediction

We can infer cell type identities using SingleR [@aran2019reference] and manipulate the output using tidyverse.

We can easily summarise the results. For example, we can see how cell type classification overlaps with cluster classification.

We can easily reshape the data for building information-rich faceted plots.

We can easily plot gene correlation per cell category, adding multi-layer annotations.

Nested analyses

A powerful tool we can use with tidyseurat is nest. We can easily perform independent analyses on subsets of the dataset. First we classify cell types in lymphoid and myeloid; then, nest based on the new classification

Now we can independently for the lymphoid and myeloid subsets (i) find variable features, (ii) reduce dimensions, and (iii) cluster using both tidyverse and Seurat seamlessly.

Now we can unnest and plot the new classification.

Aggregating cells

Sometimes, it is necessary to aggregate the gene-transcript abundance from a group of cells into a single value. For example, when comparing groups of cells across different samples with fixed-effect models.

In tidyseurat, cell aggregation can be achieved using the aggregate_cells function.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4