Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nature Biotechnology (Granja JM, Klemm SK, McGinnis LM*, et al. 2019)

Link : https://www.nature.com/articles/s41587-019-0332-7

Please cite : Granja JM et al., Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nature Biotechnology (2019)

Brief Descriptions of Analysis Scripts

scATAC Analyses

scATAC_01 - Script for reading in 10x scATAC-seq fragments identify cells using number of fragments and TSS enrichment scores and saving fitlered fragments.

scATAC_02 - Script for pre-clustering using large windows genome-wide and then calling peaks on putative clusters and create a master peak set

scATAC_03 - LSI-Clustering + UMAP of scATAC-seq data with visualization and demonstration of how to properly save umap for projection.

scATAC_04 - Computing Gene Activity Scores using an adapted form of Cicero (Pliner et al 2018).

scATAC_05 - Identifying potential disease cells by clustering disease w/ healthy reference, and then projecting these cells onto healthy hematopoiesis.

scRNA Analyses

scRNA_01 - LSI-Clustering + UMAP of scRNA-seq data with visualization and demonstration of how to properly save umap for projection.

scRNA_02 - Identifying potential disease cells by clustering disease w/ healthy reference, and then projecting these cells onto healthy hematopoiesis.

Integration (scATAC + scRNA) Analyses

scRNA_scATAC_Integration_01 - Alignment of scRNA and scATAC-seq data using Seurat CCA and identifcation of nearest neighbors across modalities.

scRNA_scATAC_Integration_02 - Aggregate scRNA + scATAC-seq data for correlation focused analysis.

scRNA_scATAC_Integration_03 - Identify putative Peak-To-Gene Links with aligned scATAC and scRNA-seq data aggregates.

scRNA_scATAC_Integration_04 - Link TFs to putative target genes that are differential in both mRNA and nearby accessibility peaks containing motifs of the TFs.

Raw Data Download

GEO Accession : GSE139369 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE139369

You will be able to download raw 10x Bam Files which can be converted back to fastq using bamtofastq (https://support.10xgenomics.com/docs/bamtofastq) if you have issues with this please reach out to support@10xgenomics

scATAC-seq fragment files which contain fragment coordinates

scRNA-seq/scADT-seq individual matrices (recommend looking below for summarized experiments)

Additional Data Download Links

These links may be moved if we can find a better host for better download speed

Notes

.rds file is an R binarized object to read into R use readRDS(filename)

SummarizedExperiment is a class in R see :
https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html

deviations (TF chromVAR) is a class in R see :
https://bioconductor.org/packages/release/bioc/html/chromVAR.html

Healthy Hematopoiesis

scATAC-seq Hematopoeisis cell x peak Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Data/scATAC-Healthy-Hematopoiesis-191120.rds

scATAC-seq Hematopoeisis cell x gene activity Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Data/scATAC-Cicero-GA-Hematopoiesis-191120.rds

scATAC-seq Hematopoeisis cell x TF chromVAR Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Data/scATAC-chromVAR-Hematopoiesis-191120.rds

scRNA-seq Hematopoeisis cell x gene Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Data/scRNA-Healthy-Hematopoiesis-191120.rds

scADT-seq Hematopoeisis cell x antibody Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Data/scADT-Healthy-Hematopoiesis-191120.rds

Note 1. If you want to get the biological classifications for each cell use colData(se)$BioClassification.

Healthy + MPAL Data Sets

scATAC-seq Hematopoeisis + MPAL cell x peak Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Disease-Data/scATAC-All-Hematopoiesis-MPAL-191120.rds

scATAC-seq Hematopoeisis + MPAL cell x gene activity Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Disease-Data/scATAC-Cicero-GA-Hematopoiesis-MPAL-191120.rds

scATAC-seq Hematopoeisis + MPAL cell x TF chromVAR Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Disease-Data/scATAC-chromVAR-All-Hematopoiesis-MPAL-191120.rds

scRNA-seq Hematopoeisis + MPAL cell x gene Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Disease-Data/scRNA-All-Hematopoiesis-MPAL-191120.rds

scADT-seq Hematopoeisis + MPAL cell x antibody Summarized Experiment :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Healthy-Disease-Data/scADT-All-Hematopoiesis-MPAL-191120.rds

Note 1. The peakset for Hematopoiesis + MPAL is different than that for Hematopoiesis because we used the same peak calling pipeline where pre-clustering was done using all the cells (ie Hematopoiesis + MPAL) then peaks called so that we could easily include malignant peaks. This did not result in many ~10-20% additional peaks, but they may not be the exact coordinates as in the previous file.

Note 2. If you want to get projected positions/classifications for MPALs onto hematopoiesis use colData(se)$ProjectedUMAP1, colData(se)$ProjectedUMAP2, and colData(se)$ProjectedClassification.