Spatial_Permutation_and_Normalization/example.R at main · rickert-lab/Spatial_Permutation_and_Normalization · GitHub

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## These packages need to be installed in R (OS-independent):
# install.packages(c('dplyr', 'ggplot2', 'spdep', 'tidyr'))
## package dependencies might require installation of system libraries (OS-dependent):
# brew install pkg-config udunits gdal

# message from 'spdep': "To access larger datasets in this package, install the spDataLarge package with:"
# install.packages('spDataLarge', repos='https://nowosad.github.io/drat/', type='source')

# load custom library with imports
source("./Spatial_Perm_and_Normalization.R")


### Samples are first processed here to generate original and permutated CLQs for each cell to cell pair in a given sample.

### Input file name example: “TAFs1_cell_type_assignment.csv”

files <- (Sys.glob("./input/*cell_type_assignment.csv"))
if (length(files) == 0) {
  stop("No files found at path. Check file path and pattern!")
}

for (file in files) {
  print(file)

  count_file <- write_counts(sample_path = file, out_dir = "./output")


  ### CLQ_permutated_matrix_gen function is using iteration number, filename and the count_file generated in the previous step.
  ### This function is dependent on multiple functions [get_CLQ(), KNN_neighbors(), find_cell_type_neighbors()]
  ### iternum is the iteration number for permutation analysis, which is set to 500.


  CLQ_permutated <- CLQ_permutated_matrix_gen1(
    iternum = 500,
    sample_path = file,
    counts_path = count_file,
    out_dir = "./output"
  )
}

###
### Then, significance of CLQs are calculated based on their percentile.
### The original CLQs and permutated CLQs are retrieved for each sample through the functions [CLQ_matrix_gen(),
### CLQ_permutated_matrix_gen_caller(),get_counts_caller() ]

files <- (Sys.glob("./input/*cell_type_assignment.csv"))
if (length(files) == 0) {
  stop("No files found at path. Check file path and pattern!")
}

for (file in files) {
  print(file)

  CLQ_matrix <- CLQ_matrix_gen(sample_path = file, out_dir = "./output")

  CLQ_permutated <- CLQ_permutated_matrix_gen2(sample_path = file, out_dir = "./output")

  counts_data <- read_counts(sample_path = file, out_dir = "./output")

  ###  “list_of_matrices” is the 500 different CLQ sets for each iteration.

  significance_matrices <- significance_matrix_gen(
    iternum = 500,
    sample_path = file,
    list_of_matrices = CLQ_permutated,
    CLQ_matrix_original = CLQ_matrix,
    counts_data = counts_data,
    out_dir = "./output"
  )

  ### plot_gen generates plot for each CLQ for a pair of cell type A to cell type B. It retrieves the permutated CLQ values
  ### from CLQ_permutated, and the original CLQ values from CLQ_matrix.

  plot_gen(
    iternum = 500,
    sample_path = file,
    list_of_matrices = CLQ_permutated,
    CLQ_matrix_original = CLQ_matrix,
    out_dir = "./output"
  )
}

#
# #calling the function
# #Example 1 : assembloid sample
# filename = "TAFs1_cell_type_assignment.csv"
# righttail = 0.05
# lefttail = 0
# call_normalization_fnc(filename , righttail , lefttail)
# #
#
# calling the function
# Example 2 : clinical specimen
# filename = "lepidic_58.6_cell_type_assignment.csv"
# righttail = 0.05
# lefttail = 0
# call_normalization_fnc(filename , righttail , lefttail)

# filename = "lepidic_58.6_cell_type_assignment.csv"
# righttail = 0.15
# lefttail = 0
# call_normalization_fnc(filename , righttail , lefttail)
#

files <- (Sys.glob("./input/*cell_type_assignment.csv"))
if (length(files) == 0) {
  stop("No files found at path. Check file path and pattern!")
}

for (file in files) {
  print(file)
  call_normalization(sample_path = file, righttail = 0.05, lefttail = 0.00, out_dir = "./output")
}