DOI: 10.5061/dryad.08kprr55h
title: Data from: Clustering Deviation Index (CDI): A robust and accurate internal measure for evaluating scRNA-seq data clustering

description: The clustering of cells has been widely used to explore the heterogeneity
 of cell populations in single-cell RNA-sequencing (scRNA-seq). We proposed
 a parametric model for monoclonal and polyclonal scRNA-seq data to
 evaluate clustering results. Based on the parametric model, we proposed a
 metric (CDI) to quantify the goodness-of-fit of cell clustering to the
 data. Here we presented CT26.WT and T-CELL as two datasets to examine the
 performance of our model and metric. CT26.WT contains wild-type CT26 cells
 from the murine colorectal carcinoma cell line, and cells in CT26.WT are
 highly homogeneous. T-CELL contains T-cells from tumor tissue of mice
 three weeks after 4T1 tumor injection. From these datasets and public
 datasets, we validated our model and benchmarked our metric.
description: This dataset contains six files. Four of them (matrix.mtx,
 features.tsv, barcodes.tsv, CT26_bulk_30k.txt) are for CT26.WT, and the
 other two are for T-CELL. CT26.WT sample preparation:
 Murine colorectal carcinoma cell line CT26.WT was obtained from the cell
 culture facility of Duke University and cultured in DMEM media (Sigma
 Aldrich). All cells were cultured at 37 degrees. Single-cell clones were
 chosen and cultured for over 220 days. Bulk RNA-seq and single-cell
 RNA-seq samples were prepared on the same day. CT26.WT
 bulk RNA-seq: Total RNA from ~ 1,000,000 cells from each group was
 extracted using the miniprep kit (Zymo Research) according to the
 manufacturer’s instructions. Then, the libraries were sequenced on the
 Illumina sequencing platform by the Novogene Corporation Inc. (CA, USA)
 (HiSeq × Ten) with paired-end 150 bp (PE 150) sequencing
 strategy. CT26.WT scRNA-seq: A total of ~ 10000 cells
 of each clone were selected for single-cell RNA-seq. Single-cell RNA
 sequence libraries using Chromium Single Cell 3’ Reagent kits v3 (10x
 genomics). The libraries were then sequenced on the Illumina sequencing
 platform by the Novogene Corporation Inc. (CA, USA) with PE 150 sequencing
 strategy in a single index mode. T-CELL scRNA-seq: In
 this study, tumors were firstly collected from the female mice after 3
 weeks since the mice were injected by 4T1 tumors. Tissues were then
 disassociated into single cells and homogenized. T cells were separated
 out by flow sorting with a stringent gating threshold and sequenced on the
 10X platform. T-CELL filtering: We filtered out genes
 with less than 2% non-zero cells and removed cells with less than 2%
 non-zero genes. Eventually, 2, 989 cells from five cell types with 7, 893
 genes were retained. T-CELL annotation: The benchmark
 clustering labels of the T-CELL population were generated as a combination
 of protein-marker-based flow sorting labels and bioinformatics labels from
 Seurat v2. For evaluation purposes, we selected 5 distinct cell types:
 Regulatory Trm cells, Classical CD4 Tem cells, CD8 Trm cells, CD8 Tcm
 cells, and Active EM-like Treg cells.
description: CT26.WT: The raw count matrix is in "matrix.mtx", and
 it can be read by readMM() function in R package Matrix. The feature names
 and cell barcodes are in "features.tsv" and
 "barcodes.tsv", respectively, They can be read by read.table()
 function in R package utils. The bulk-seq dataset is in
 "CT26_bulk_30k.txt", and it can be read by read.table() function
 in R package utils.  T-CELL: The filtered count matrix
 is in "Tcell_5type_filtered.rds". The cell annotation is in
 "Tcell_5type_filtered_labels.rds". They can both be opened with
 R function readRDS().