Using CisTopic object generated through R for SCENIC+ analysis about scenicplus HOT 1 CLOSED

Hi @debbie28!

This is a great question! I will divide it in two parts:

• 'I have a cisTopic analysis in R, can I still use it for SCENIC+?'

  Yes, you can! There is no need to repeat the topic modelling in Python :).
  
  For this, you will need to do the following in R first:
  
  ```
  # Export data to python
  library(arrow)
  path <- OUTDIR
  cisTopic_obj <- readRDS(PATH_TO_YOUR_CISTOPICOBJECT)
  modelMat <- modelMatSelection(cisTopic_obj, 'cell', 'Probability')
  modelMat <- as.data.frame(modelMat)
  write_feather(modelMat, sink=paste0(path, 'model_to_pycisTopic/cell_topic.feather'))
  modelMat <- modelMatSelection(cisTopic_obj, 'region', 'Probability', all.regions=TRUE)
  modelMat <- as.data.frame(modelMat)
  write_feather(modelMat, sink=paste0(path, 'model_to_pycisTopic/topic_region.feather'))
  # You can also save the count.matrix
  ct <- [email protected]
  ct <- as.data.frame(ct)
  write_feather(ct, sink=paste0(path, 'model_to_pycisTopic/count_matrix.feather'))
  ```
  
  And then in Python:
  
  ```
  import pycisTopic
  ## 1. Initialize cisTopic object
  from pycisTopic.cistopic_class import *
  # Load count matrix
  matrix_path=PATH_TO_FRAGMENTS_MATRIX
  fragment_matrix = pd.read_feather(matrix_path)
  cisTopic_obj = create_cistopic_object(fragment_matrix)
  # Also add the cell annotation, cell_data should be a pandas df with cells as rows (cell names as index) and variables as columns
  cisTopic_obj.add_cell_data(cell_data)
  # 2. Add model
  from pycisTopic.utils import *
  model = load_cisTopic_model(PATH_TO_THE_FOLDER_WHERE_YOU_SAVED_DATA_FROM_R)
  cistopic_obj.add_LDA_model(model)
  # 3. Get imputed accessibility matrix
  from pycisTopic.diff_features import *
  imputed_acc_obj = impute_accessibility(cistopic_obj, selected_cells=None, selected_regions=None, scale_factor=10**6)
  # 4. DARs
  normalized_imputed_acc_obj = normalize_scores(imputed_acc_obj, scale_factor=10**4)
  variable_regions = find_highly_variable_features(normalized_imputed_acc_obj,
                                             min_disp = 0.05,
                                             min_mean = 0.0125, 
                                             max_mean = 3,
                                             max_disp = np.inf,
                                             n_bins=20, 
                                             n_top_features=None,
                                             plot=True)
  markers_dict= find_diff_features(cistopic_obj, 
                        imputed_acc_obj,
                        variable='cell_type', # Here put the column of cell_data that you want to have DARs for
                        var_features=variable_regions,
                        contrasts=None,
                        adjpval_thr=0.05,
                        log2fc_thr=np.log2(1.5),
                        n_cpu=5) 
  # You can continue with pycistarget.
  ```
  
  Feel free to change the function if you do not want to work in feather format.
  
  ```
  def load_cisTopic_model(path_to_cisTopic_model_matrices):
      metrics = None
      coherence = None
      marg_topic = None
      topic_ass = None
      cell_topic = pd.read_feather(path_to_cisTopic_model_matrices + "cell_topic.feather")
      cell_topic.index = ["Topic" + str(x) for x in range(1, cell_topic.shape[0] + 1)]
      topic_region = pd.read_feather(
          path_to_cisTopic_model_matrices + "topic_region.feather"
      )
      topic_region.index = ["Topic" + str(x) for x in range(1, topic_region.shape[0] + 1)]
      topic_region = topic_region.T
      parameters = None
      model = cisTopicLDAModel(
          metrics, coherence, marg_topic, topic_ass, cell_topic, topic_region, parameters
      )
      return model
  ```
  

• 'I have an analysis with another tool (e.g. Signac/ArchR), can I still use it for SCENIC+?'

Here you have two options:

1. Start from the count matrix: If you have performed QC steps and generated you fragment count matrix with another tool, you can directly start with the creation of the cisTopic object. In this tutorial you have an example on how to do this.
2. You want to skip the topic modelling altogether: I would not recommend this option because we have not tested it, but technically it is possible. You would need to get 1) a denoised fragment matrix and 2) region sets. The region sets will be used as input for cisTarget. The region sets could be Differentially Accessible Regions (DARs); however we do recommend to use topics as well as they can capture regions with accessibility patterns without the need of specifying contrasts. If only using DARs you will have to make sure to make the right contrats (for example, all cell types versus the rest; higher cell type groups versus rest, etc). For example, in the cortex data set the topics reveal a topic of regions shared across all neurons; this pattern would be lost if you only do contrasts per cell type and not higher groups. In conclusion, it is possible to plug it in code-wise; but we can't assure results will be as good as the workflow we propose.

I will add these responses in a FAQs tab in the readthedocs page as I find them very relevant, thanks for the heads up!

C

from scenicplus.