Skip to content

Latest commit

 

History

History
49 lines (30 loc) · 2.76 KB

README.md

File metadata and controls

49 lines (30 loc) · 2.76 KB
Raw

BMN characterization

This repository is a summary of codes used in Deconvolution of the hematopoietic stem cell microenvironment reveals a high degree of specialization and conservation.

Preprocessing, Integration and Clustering

The cells were filtered and integrated using Seurat. A divide-and-conqur strategy was applied using IKAP[REF1]. Core script is Clustering.R

Bootstrapping

To measure the robustness of the clusters, we customized an existing bootstrapping-based approach based on random forest [REF2]. See details below.

Summary of the bootstrapping strategy:

Bootstrapping strategy outline



  • Dominant cluster identification: A dominant cluster is the cluster to which the cell is assigned for more than half the runs.
  • Number of Correct Dominance Assignment (#Correct): The sum of times the dominant cluster matches the original cluster (true positive) for a cell across all cluster pair comparisons.
  • Recall Per Cell: The proportion of correct assignment (positive result) to its original cluster in all runs from all comparisons.
Usage of bootstrapping methods

see Using_Bootstrapping_functions.R

Compare mouse and human BMN

Human samples were filtered in the same way with mouse, except

  • the mt.percent threshold is 10% instead of 5%
  • cells with more than 10% of Immunoglobin protein were removed.

singleR.R shows the comparison of mouse and human EC/MSCs using singleR.

Added values

To show that the integration method provide more information than a single dataset.

  • Added value 1: Comparing the DEGs defined by individual dataset and integrated dataset. See Added_value1.R

  • Added value 2: Cluster stability evaluation for individual dataset.
    Apply Bootstrapping function to each individual dataset while maintaining the cluster label to see if the cluster defined by one dataset is robust enough.

  • Added value 3: Comparing cluster identified by a single dataset.
    To further understand if the clusters can be identified by one dataset only, Baryawno dataset was used as an example considering its large cell populations. Clustering+bootstrapping functions were applied to Scadden(Baryawno) dataset.

Reference

1. Chen, Y. C. et al. IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis. Gigascience 8, (2019).
2. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).