Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.
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HCA BM and cord blood data were downloaded from the HCA portal (https://preview.data.humancellatlas.org/). The dataset represents a relatively uniform collection of data on well-studied tissues, making it particularly suitable for benchmarking purposes. To reduce calculation times in benchmark evaluations, we took a random subset of the cells from lane 1 of each dataset. By default, 3,000 cells per sample were used (HCA BM + CB 3k datasets). A smaller, 1,000-cell dataset (HCA BM + CB 1k) was used for the more extensive sensitivity analysis (Supplementary Fig. 1f). For Fig. 1i, we combined HCA BM samples with two samples (‘Frozen BMMCs Healthy Donors 1 and 2’) downloaded this from 10x Genomics (https://www.10xgenomics.com/resources/datasets/). This was done to extend the number of samples (x axis in Fig. 1i). The data on breast cancer from Azizi et al.9 were downloaded from GEO (GSE114725) as a count matrix, together with the provided annotations. As shown in the plots (Fig. 2 and Supplementary Fig. 4), the annotations were simplified to collapse patient-specific populations and omit smaller subpopulation distinctions. To demonstrate applicability to different levels of data fragmentation, we reanalyzed the dataset by combining eight individual subjects, 15 subject + tissue combinations or 53 subject + tissue + replicate combinations. The dataset provides a good example of a clinically oriented panel with both tissue- and individual-level heterogeneity. The molecular count data and annotations on lung cancer from Lambrechts et al.12 were downloaded from ArrayExpress (E-MTAB-6149, E-MTAB-6653). The dataset provides an example of a more typical case-control design of a clinically oriented panel. The molecular count data and annotations on non-small-cell lung cancer from Guo et al.11 were downloaded from GEO (GSE99254). The dataset serves as an example of a heterogeneous clinically oriented panel, with limited complexity and numbers of cells in some of the samples. The molecular count data and annotations on head-and-neck cancer from Puram et al.10 were downloaded from GEO (GSE103322). Similar to the data from Guo et al.11, the dataset provides an example of a collection with challenging complexity and cell-number variation in a clinically oriented panel. For the human cortex comparison, the datasets were included as an example of integration of distinct nuclei-based protocols. The count matrix for Hodge et al. (bioRxiv; https://doi.org/10.1101/384826) was downloaded from http://celltypes.brain-map.org/rnaseq. The count matrix from Lake et al. (Nat. Biotechnol. 36, 70–80; 2018) was downloaded from GEO (GSE97930). Tabula Muris mouse data were downloaded from https://tabula-muris.ds.czbiohub.org/. Only cells with at least 1,000 molecules were analyzed. A total of 48 datasets were combined. The mouse cell atlas by Han et al.16 and the relevant annotations were downloaded from http://bis.zju.edu.cn/MCA/. Cell line datasets were excluded. Human pancreas islet data from different platforms, used to demonstrate alignment between different platforms and illustrate mixing controls (Supplementary Fig. 9), were taken from the following sources: 10x Chromium platform data were taken from a publication by Xin et al.19 and downloaded from GEO (GSE114297). Normalized count matrices were used. inDrops platform data were taken from a publication by Baron et al.20 and downloaded from GEO (GSE84133). Only human data (four samples) were used. Normalized count matrices were used. Smart-seq2 platform data were taken from a publication by Segerstolpe et al.21 with count matrices downloaded from ArrayExpress (E-MTAB-5061). Only data from healthy individuals (six samples) were used. For the demonstration of ATAC-seq alignment and alignment between ATAC-seq and RNA-seq (Supplementary Note 2), the following datasets were used: sci-ATAC data from Cusanovich et al.17 were downloaded from the authors’ website (http://atlas.gs.washington.edu/mouse-atac/). Author-provided accessibility scores were used as gene-level input to Conos. sci-CAR data from Cao et al.18 were downloaded from GEO (GSE117089). To increase coverage, the cells were aggregated into groups of ten on the basis of transcriptional similarity (see Supplementary Note 2 for details).
Conos is implemented as an R package with C++ optimizations, and is available on GitHub (https://github.com/hms-dbmi/conos) under the GPL-3 open source license. Analysis scripts and intermediate data representations used for the preparation of the manuscript can be found on the author’s website (http://pklab.med.harvard.edu/peterk/conos/).
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N.B. and P.V.K. were supported by the NIH R01HL131768 and NSF-14-532 CAREER awards. D.N. and Y.Z. were supported by the SMTB Alumni Summer Research Program from Zimin Foundation. We would like to thank the HMS Research Computing team for facilitating benchmarking calculations using the O2 cluster.
P.V.K. serves on the Scientific Advisory Board to Celsius Therapeutics Inc.
Peer Review Information: Nicole Rusk was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Barkas, N., Petukhov, V., Nikolaeva, D. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat Methods 16, 695–698 (2019). https://doi.org/10.1038/s41592-019-0466-z
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