Package 'AnanseSeurat'

Title: Construct ANANSE GRN-Analysis Seurat
Description: Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software 'ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from 'Seurat' objects, for GRN analysis by 'ANANSE' implemented in 'snakemake'. Finally, incorporate results for visualization and interpretation.
Authors: Jos Smits [aut, cre, cph] , Huiqing Jo Zhou [aut, fnd, cph]
Maintainer: Jos Smits <[email protected]>
License: Apache License (>= 2)
Version: 1.2.0
Built: 2025-03-05 04:35:18 UTC
Source: https://github.com/jgasmits/ananseseurat

Help Index

config_scANANSE

Description

This functions generates a sample file and config file for running Anansnake based on the seurat object

Usage

config_scANANSE(
  seurat_object,
  output_dir,
  min_cells = 50,
  cluster_id = "seurat_clusters",
  genome = "./scANANSE/data/hg38",
  additional_contrasts = c()
)

Arguments

seurat_object

seurat object
output_dir

directory where the files are outputted
min_cells

minimum of cells a cluster needs to be exported
cluster_id

ID used for finding clusters of cells
genome

genomepy name or location of the genome fastq file
additional_contrasts

additional contrasts to add between clusters within cluster_ID

Value

None, outputs snakemake config file in the output directory

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
config_scANANSE(sce_small, min_cells = 2, output_dir = tempdir())

DEGS_scANANSE

Description

Calculate the differential genes needed for ananse influence

Usage

DEGS_scANANSE(
  seurat_object,
  output_dir,
  min_cells = 50,
  cluster_id = "seurat_clusters",
  genome = "./scANANSE/data/hg38",
  RNA_count_assay = "RNA",
  additional_contrasts = "None"
)

Arguments

seurat_object

seurat object
output_dir

directory where the files are outputted
min_cells

minimum of cells a cluster needs to be exported
cluster_id

ID used for finding clusters of cells
genome

path to the genome folder used for the anansnake config file
RNA_count_assay

assay containing the RNA data
additional_contrasts

additional contrasts to add between clusters within cluster_ID

Value

None, outputs DEG files in the output directory

Examples

sce_small <- readRDS(system.file("extdata","sce_obj_tiny.Rds",package = 'AnanseSeurat'))
DEGS_scANANSE(sce_small, min_cells = 2, output_dir = tempdir())

export_seurat_Maelstrom

Description

normalize and export the peak table of a seurat object based on clusters

Usage

export_ATAC_maelstrom(
  seurat_object,
  output_dir,
  min_cells = 50,
  ATAC_peak_assay = "peaks",
  cluster_id = "seurat_clusters",
  select_top_rows = TRUE,
  n_top_rows = 1e+05
)

Arguments

seurat_object

object
output_dir

directory where the files are outputted
min_cells

minimum of cells a cluster needs to be exported
ATAC_peak_assay

assay of the seurat object containing the peaks and peakcounts
cluster_id

ID used for finding clusters of cells
select_top_rows

only output the top variable rows, or all rows if false
n_top_rows

amount of variable rows to export

Value

None, outputs maelstrom peak counts table in the output directory

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
config_scANANSE(sce_small, min_cells = 2, output_dir = tempdir())

export_ATAC_scANANSE

Description

This functions exports ATAC values from a seurat object

Usage

export_ATAC_scANANSE(
  seurat_object,
  output_dir,
  min_cells = 50,
  ATAC_peak_assay = "peaks",
  cluster_id = "seurat_clusters"
)

Arguments

seurat_object

object
output_dir

directory where the files are outputted
min_cells

minimum of cells a cluster needs to be exported
ATAC_peak_assay

assay of the seurat object containing the peaks and peakcounts
cluster_id

ID used for finding clusters of cells

Value

None, outputs ATAC peak count file in the output directory

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
export_ATAC_scANANSE(sce_small, min_cells = 2, output_dir = tempdir())

export_CPM_scANANSE

Description

This functions exports CPM values from a seurat object

Usage

export_CPM_scANANSE(
  seurat_object,
  output_dir,
  min_cells = 50,
  RNA_count_assay = "RNA",
  cluster_id = "seurat_clusters"
)

Arguments

seurat_object

the seurat object used to export the CPM values from
output_dir

directory where the files are outputted
min_cells

minimum of cells a cluster needs to be exported
RNA_count_assay

assay of the seurat object containing the RNA count data
cluster_id

ID used for finding clusters of cells

Value

None, outputs CPM and counts files in the output directory

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
export_CPM_scANANSE(sce_small, min_cells = 2, output_dir = tempdir())

Factor_Motif_Plot

Description

plot both expression of a TF, and the motif accessibility of the associated motif. Finally, fetch the motif logo from the Maelstrom directory.

Usage

Factor_Motif_Plot(
  seurat_object,
  TF_list,
  assay_RNA = "RNA",
  assay_maelstrom = "MotifTFanticor",
  logo_dir = "~/maelstrom/logos",
  col = c("darkred", "white", "darkgrey"),
  dim_reduction = "umap"
)

Arguments

seurat_object

seurat object
TF_list

list of TFs to plot the expression and linked motif Z-score for
assay_RNA

RNA_count_assay assay containing the RNA data
assay_maelstrom

maelstrom assay used for zscore vizualization, often either TFcor or TFanticor
logo_dir

directory containing motif logos generated by gimme maelstrom
col

colours used for zscore vizualization
dim_reduction

dimensionality reduction method to use

Value

patchwork plot containing a expression dimreduction plot, a maelstrom motif score dimreduction plot, and a png image of the motif

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
logos_dir_path <- system.file("extdata","maelstrom","logos",package = 'AnanseSeurat')
sce_small <- Factor_Motif_Plot(sce_small,
  c('gene1', 'gene2'),
  dim_reduction = 'pca',
  logo_dir = logos_dir_path)

import_seurat_Maelstrom

Description

load Maelstrom enriched motifs

Usage

import_seurat_maelstrom(
  seurat_object,
  cluster_id = "seurat_clusters",
  maelstrom_file = "~/final.out.txt",
  return_df = FALSE
)

Arguments

seurat_object

object
cluster_id

ID used for finding clusters of cells
maelstrom_file

maelstrom final.out.txt file
return_df

return both the seurat object and a dataframe with maelstrom scores as a list

Value

seurat object with the maelstrom motif scores addes as an assay

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
maelstromfile_path <- system.file("extdata","maelstrom","final.out.txt",package = 'AnanseSeurat')
sce_small <- import_seurat_maelstrom(sce_small, maelstrom_file = maelstromfile_path)

import_seurat_scANANSE

Description

import the influences from a anansnake directory into a seurat object

Usage

import_seurat_scANANSE(
  seurat_object,
  cluster_id = "seurat_clusters",
  anansnake_inf_dir = "None",
  return_df = FALSE
)

Arguments

seurat_object

seurat object
cluster_id

ID used for finding clusters of cells
anansnake_inf_dir

influence directory generated by anansnake
return_df

return both the seurat object and a dataframe with influence scores as a list

Value

seurat object with the influence scores addes as an assay

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
infdir <- system.file("extdata","influence",package = 'AnanseSeurat')
sce_small <- import_seurat_scANANSE(sce_small, anansnake_inf_dir = infdir)

Maelstrom_Motif2TF

Description

create motif-factor links & export tables for printing motif score alongside its binding factor

Usage

Maelstrom_Motif2TF(
  seurat_object,
  mot_mat = NULL,
  m2f_df = NULL,
  cluster_id = "seurat_clusters",
  maelstrom_dir = "./maelstrom/",
  combine_motifs = "means",
  RNA_expression_assay = "RNA",
  RNA_expression_slot = "data",
  expr_tresh = 10,
  cor_tresh = 0.3,
  curated_motifs = FALSE,
  cor_method = "pearson",
  return_df = FALSE
)

Arguments

seurat_object

object
mot_mat

motif_matrix, if not provided extracts one from the single cell object from the maelstrom assay
m2f_df

motif to factor dataframe, if not provided extracts from the maelstrom directory
cluster_id

ID used for finding clusters of cells
maelstrom_dir

directory where the GimmeMotifs m2f table is stored
combine_motifs

means (take mean multiple motifscores), max_var (take motif with highest variance), or max_cor (take motif with best correlation to gene expression)
RNA_expression_assay

Seurat assay containing factor expression info
RNA_expression_slot

slot within assay used for calculating mean factor expression per cluster
expr_tresh

minimum sum of gene counts over all cells in RNA_expression_assay to filter genes by
cor_tresh

minimum value of to filter the cor() output by
curated_motifs

use only curated motifs (T), or all motifs in the database (F)
cor_method

specify one of the cor() methods
return_df

return both the seurat object and two dataframes with maelstrom scores and expression values as a list

Value

seurat object with two assays added, MotifTFcor for TFs with positive correlation to the linked motif, and MotifTFanticor for TFs with positive correlation to the linked motif

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
maelstrom_dir_path <- system.file("extdata","maelstrom",package = 'AnanseSeurat')
sce_small <- Maelstrom_Motif2TF(sce_small, maelstrom_dir = maelstrom_dir_path)

per_cluster_df

Description

generate a table of the assay score averages per cluster identifier cell

Usage

per_cluster_df(
  seurat_object,
  assay = "influence",
  cluster_id = "seurat_clusters"
)

Arguments

seurat_object

seurat object
assay

assay containing influence or motif scores generated from cluster pseudobulk
cluster_id

ID used for finding clusters of cells

Value

dataframe with assay scores, concatinating cells from each per cluster

Examples

sce_small <- readRDS(system.file("extdata","sce_small.Rds",package = 'AnanseSeurat'))
df <- per_cluster_df(sce_small)