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

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

For current up to date scATAC-seq analysis https://github.com/GreenleafLab/ArchR

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

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.

LSI-Projection

scATAC-seq saved UMAP embedding :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/LSI-Projection/scATAC-Projection-UMAP.zip

scRNA-seq saved UMAP embedding :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/LSI-Projection/scRNA-Projection-UMAP.zip

Note 1. To project into the reference hematopoiesis we used in this paper you need to use the uwot.tar file for either modality.

Integration

Peak-To-Gene Linkages :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Integration/MPAL-Significant-Peak2Gene-Links.tsv.gz

scRNA to scATAC mappings :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/Integration/scATAC-scRNA-mappings.rds

Other

MPAL Clinical FACS Data :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data_Revision_MPAL_FACS_FCS.zip

Differential Results MPAL (scRNA + scATAC) :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/LSI-Projection/MPAL-Differential-Results.zip

Differential Results AML (scRNA) :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/LSI-Projection/scRNA-AML-Analyses.zip

Differential Results Bulk Leuekemias (bulk RNA) :
https://jeffgranja.s3.amazonaws.com/MPAL-10x/Supplementary_Data/LSI-Projection/Bulk-Leukemias-RNA-Differential-Results.zip

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