This dataset contains all the Seurat objects that were used for generating all the figures in Pal et al. 2021 (https://doi.org/10.15252/embj.2020107333). All the Seurat objects were created under R v3.6.1 using the Seurat package v3.1.1. The detailed information of each object is listed in a table in Chen et al. 2021.

Background

RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.

Results

With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.

Conclusion

RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Data repository for the scMappR manuscript:

Abstract from biorXiv (https://www.biorxiv.org/content/10.1101/2020.08.24.265298v1.full).

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq.

Additional file 2 Results_of_intePareto. Full list of the results of integrative analysis using intePareto.

One of the fundamental aspects of genomic research is the identification of differentially expressed (DE) genes between two conditions. In the past decade, numerous DE analysis tools have been developed, employing various normalization methods and statistical modelling approaches. In this article, we introduce DElite, an R package that leverages the capabilities of four state-of-the-art DE tools: edgeR, limma, DESeq2, and dearseq. DElite returns the outputs of the four tools with a single command line, thus providing a simplified way for non-expert users to perform DE analysis. Furthermore, DElite provides a statistically combined output of the four tools, and in vitro validations support the improved performance of these combination approaches for the detection of DE genes in small datasets. Finally, DElite offers comprehensive and well-documented plots and tables at each stage of the analysis, thus facilitating result interpretation. Although DElite has been designed with the intention of being accessible to users without extensive expertise in bioinformatics or statistics, the underlying code is open source and structured in such a way that it can be customized by advanced users to meet their specific requirements. DElite is freely available for download from https://gitlab.com/soc-fogg-cro-aviano/DElite.

Fish under intensive rearing conditions experience various stress conditions, which have negative impacts on survival, growth and fillet quality. Identifying and characterizing the molecular mechanisms underlying stress responses will facilitate the development of strategies that aim to improve animal welfare and production efficiency. In this study, we focused on hepatic response to handling and confinement stress in rainbow trout due to their relevance in aquaculture production. Our objective was to identify differentially expressed transcripts (DETs) in liver in response to handling and confinement stress using RNA-seq. Total RNA was extracted from the livers of individual fish in five tanks having eight fish each, including three tanks of fish subjected to handling and confinement stress and two control tanks. Equal amount of total RNA of six individual fish was pooled by tank to create five RNA-seq libraries which were sequenced in one lane of Illumina HiSeq 2000. Three sequencing runs were conducted for a total of 491,570,566 reads. These reads were mapped onto the previously generated stress reference transcriptome, and 316 DETs were identified. Twenty one DETs were selected for qPCR to validate the RNA-seq approach. The fold changes in gene expression identified by RNA-seq and qPCR were highly correlated (r = 0.94). Several gene ontology terms including transcription factor activity and metabolic process especially carbohydrate metabolism were enriched among these DETs. Pathways involved in response to handling and confinement stress were implicated by mapping the DETs to the reference pathways in KEGG database.

This archive contains supplementary material used in the workshop "Introduction to single cell RNAseq analysis" taught by the Danish National Sandbox for Health Data Science. The course repo can be found on Github. Data.zip contains 6 10x runs on Spermatogonia development. 3 from healthy individuals and 3 from azoospermic individuals. Data has been already preprocessed using cellranger and can be loaded using Seurat (R) or scanpy (python). Slides.zip contains slides explaning theory regarding single cell RNAseq data analysis Notebooks.zip contains Rmarkdown files to follow the course in using R in Rstudio. Latest version of the notebooks

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

This is the GitHub repository for the single cell RNA sequencing data analysis for the human manuscript. The following essential libraries are required for script execution: Seurat scReportoire ggplot2 dplyr ggridges ggrepel ComplexHeatmap Linked File: -------------------------------------- This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. Provided below are descriptions of the linked datasets: 1. Gene Expression Omnibus (GEO) ID: GSE229626 - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the matrix.mtx, barcodes.tsv, and genes.tsv files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token"(https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). 2. Sequence read archive (SRA) repository - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the "raw sequencing" or .fastq.gz files, which are tab delimited text files. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token" (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). Please note that since the GSE submission is private, the raw data deposited at SRA may not be accessible until the embargo on GSE229626 has been lifted. Installation and Instructions -------------------------------------- The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation: > Ensure you have R version 4.1.2 or higher for compatibility. > Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code. The following code can be used to set working directory in R: > setwd(directory) Steps: 1. Download the "Human_code_April2023.R" and "Install_Packages.R" R scripts, and the processed data from GSE229626. 2. Open "R-Studios"(https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R. 3. Set your working directory to where the following files are located: - Human_code_April2023.R - Install_Packages.R 4. Open the file titled Install_Packages.R and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies. 5. Open the Human_code_April2023.R R script and execute commands as necessary.

This lesson plan was created to teach RNAseq analysis as a part of GCAT-SEEK network. It is provided here, both in finished form and with the modifiable source code, to allow flexible adaptation to various classroom settings, published in...

This page includes the data and code necessary to reproduce the results of the following paper: Yang Liao, Dinesh Raghu, Bhupinder Pal, Lisa Mielke and Wei Shi. cellCounts: fast and accurate quantification of 10x Chromium single-cell RNA sequencing data. Under review. A Linux computer running an operating system of CentOS 7 (or later) or Ubuntu 20.04 (or later) is recommended for running this analysis. The computer should have >2 TB of disk space and >64 GB of RAM. The following software packages need to be installed before running the analysis. Software executables generated after installation should be included in the $PATH environment variable.

R (v4.0.0 or newer) https://www.r-project.org/ Rsubread (v2.12.2 or newer) http://bioconductor.org/packages/3.16/bioc/html/Rsubread.html CellRanger (v6.0.1) https://support.10xgenomics.com/single-cell-gene-expression/software/overview/welcome STARsolo (v2.7.10a) https://github.com/alexdobin/STAR sra-tools (v2.10.0 or newer) https://github.com/ncbi/sra-tools Seurat (v3.0.0 or newer) https://satijalab.org/seurat/ edgeR (v3.30.0 or newer) https://bioconductor.org/packages/edgeR/ limma (v3.44.0 or newer) https://bioconductor.org/packages/limma/ mltools (v0.3.5 or newer) https://cran.r-project.org/web/packages/mltools/index.html

Reference packages generated by 10x Genomics are also required for this analysis and they can be downloaded from the following link (2020-A version for individual human and mouse reference packages should be selected): https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest After all these are done, you can simply run the shell script ‘test-all-new.bash’ to perform all the analyses carried out in the paper. This script will automatically download the mixture scRNA-seq data from the SRA database, and it will output a text file called ‘test-all.log’ that contains all the screen outputs and speed/accuracy results of CellRanger, STARsolo and cellCounts.

An example of customized R code generated by iDEP. This code is generated for the analysis of the Hoxa1 dataset. (R 11 kb)

Background Individuals with cystic fibrosis have an elevated lifetime risk of colonization, infection, and disease caused by nontuberculous mycobacteria. A prior study involving non-cystic fibrosis individuals reported a gene expression signature associated with susceptibility to nontuberculous mycobacteria pulmonary disease (NTM-PD). In this study, we determined whether people living with cystic fibrosis who progress to NTM-PD have a gene expression pattern similar to the one seen in the non-cystic fibrosis population.
Methods We evaluated whole blood transcriptomics using bulk RNA-seq in a cohort of cystic fibrosis patients with samples collected closest in timing to the first isolation of nontuberculous mycobacteria. The study population included patients who did (n = 12) and did not (n = 30) develop NTM-PD following the first mycobacterial growth. Progression to NTM-PD was defined by a consensus of two expert clinicians based on reviewing clinical, microbiological, and radiologic..., Study population and clinical data This study is a secondary data analysis using blood samples and data from the “CF Biomarker†cohort approved by the University of British Columbia-Providence Health Care ethics review board (H12-00835). The local ethics board also reviewed and approved the secondary analysis (H20-00117). Patients in the parent cohort were recruited following informed consent at the St. Paul’s Hospital Adult CF Clinic (Vancouver, Canada) between January 2012 and December 2019. In the current analysis, we included participants who consented to the future use of their samples and data, had at least one positive respiratory culture for NTM, and had a whole blood RNA sample available (PAXgene® stored at -70°C). Lung transplant recipients and subjects without a definite diagnosis of CF were excluded. We preferentially selected blood samples taken during clinically stable periods and closest to the first positive growth of NTM. We did not limit blood sampling to within a spec..., The data sets are provided as comma-separated values and can be opened with standard statistical software or explored with a spreadsheet program. In our analyses, we employed R and the GUI R studio (v 4.1.1) for analysis. Raw sequencing processing and transcript counting was done in a CentOS high performance cluster, dependencies and commands ran are described inside markdown scripts.Â

Low RNA yield and quality limit use of formalin-fixed paraffin-embedded (FFPE) tissue samples for genomic analyses. In this study, we evaluated methods to demodify RNA highly fragmented and crosslinked by formalin fixation. Primary endpoints were RNA recovery, RNA-sequencing quality metrics, and target gene responses to a reference chemical (phenobarbital, PB). Frozen mouse liver samples from control and PB groups (n=6/group) were divided and preserved for 3 months as follows: frozen (FR); 70% ethanol (OH); 10% buffered formalin for 18 hours followed by ethanol (18F); and 10% buffered formalin (3F). Samples from OH, 18F, and 3F groups were processed to FFPE blocks and sectioned for RNA isolation. The latter group received no additional treatment (3F) or the following demodification protocols: short heated incubation with TAE buffer; overnight heated incubation with an organocatalyst using two different isolation kits; or overnight heated incubation without organocatalyst. TruSeq Stranded Total RNA libraries with Ribo-Zero were built and sequenced using the Illumina HiSeq platform. Extended incubation with or without organocatalyst increased RNA yield >3-fold and enhanced quality compared to 3F, as indicated by higher RNA integrity number (>1.5-fold) and fragment analysis values (>3.0-fold). Post-sequencing metrics showed reduced bias in gene coverage and deletion rates for all extended incubation groups. Following PB-induced differential gene expression analysis, all demodification groups showed increased overlap with FR in genes (73-83%) and pathways (91-94%) compared to 3F overlap with FR (60% and 63%, respectively). These results demonstrate simple changes in RNA isolation methods that can enhance genomic analyses of FFPE samples. This dataset is associated with the following publication: Wehmas, L., C. Wood, R. Gagne, A. Williams, C. Yauk, M. Gosink, D. Dalmas, R. Hao, R. O'Lone, and S. Hester. Demodifying RNA for Transcriptomic Analyses of Archival Formalin-Fixed Paraffin-Embedded Samples. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 162(2): 535-547, (2018).

1. The growth and development of root systems, essential for plant performance, is influenced by mechanical properties of the substrate in which the plants grow. Mechanical impedance, such as by compacted soil, can reduce root elongation and limit crop productivity.

2. To understand better the mechanisms involved in plant root responses to mechanical impedance stress, we investigated changes in the root transcriptome and hormone signalling responses of Arabidopsis to artificial root barrier systems in vitro.

3. We demonstrate that upon encountering a barrier, reduced Arabidopsis root growth and the characteristic 'step-like' growth pattern is due to a reduction in cell elongation associated with changes in signalling gene expression. Data from RNA-sequencing combined with reporter line and mutant studies identified essential roles for reactive oxygen species, ethylene and auxin signalling during the barrier response.

4. We propose a model in which early responses to mechanical impedance include reactive oxygen signalling integrated with ethylene and auxin responses to mediate root growth changes. Inhibition of ethylene responses allows improved growth in response to root impedance, an observation that may inform future crop breeding programmes.

Methods 20 mg of tissue was ground in liquid nitrogen using TissueLyser II (QIAGEN, Manchester, UK) and RNA extracted using the Qiagen ReliaPrepTM RNA Tissue Miniprep System. RNA quality was determined using the NanoDrop ND-1000 spectrophotometer (ThermoFisher Scientific) and Agilent 2200 TapeStation. Libraries were constructed from 100 ng and 1 μg total RNA using the NEBNext UltraTM Directional RNA Library Prep Kit for Illumina for use with the NEBNext Poly(A) mRNA Magnetic Isolation Module (NEB, Hitchin, UK). mRNA was isolated, fragmented and primed, cDNA was synthesised and end prep was performed. NEBNext Adaptor was ligated and the ligation reaction was purified using AMPure XP Beads. PCR enrichment of adaptor ligated DNA was conducted using NEBNext Multiplex Oligos for Illumina (Set 1, NEB#E7335). The PCR reaction was purified using Agencourt AMPure XP Beads. Library quality was then assessed using a DNA analysis ScreenTape on the Agilent Technologies 2200 TapeStation. qPCR was used for sample quantification using NEBNext® Library Quant Kit Quick Protocol Quant kit for Illumina. Samples were diluted to 10 nM. 7 μl of each 10 nM sample was pooled together and all were run on two lanes using an Illumina HiSeq2500 (DBS Genomics facility, Durham University). Approximately 30M unique paired-end 125bp reads were carried per sample. Primers were designed using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/primer-blast/) and synthesised by MWG Eurofins (http://www.eurofinsdna.com/). FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was used to assess read quality and Trimmomatic (Bolger et al., 2014) was used to cut down and remove low quality reads. Salmon (Patro et al., 2017) was used for quasi-mapping of reads against the AtRTD2-QUASI (Brown et al., 2017; Zhang et al., 2017) transcriptome and to estimate transcript-level abundances. The tximport R package (Soneson et al., 2016) was used to import transcript-level abundance, estimate counts and transcript lengths, and summarise into matrices for downstream analysis in R. Before differential expression analysis, low quality reads were filtered out of the data set. Only genes with a count per million of 0.744 in 6 or more samples were retained. The DESeq2 (Love et al., 2014) R package was used to estimate variance-mean dependence in count data and test for differential expression (using the negative binomial distribution model). A padj-value of ≤0.05 and a log2fold change of ≥0.5 were selected to identify differentially expressed genes (DEGs). The 3D RNA-Seq online App (Guo et al., 2019; Calixto et al., 2018) was used for independent verification of estimated DEGs and for differential alternative splicing analysis.

This archive contains data of scRNAseq and CyTOF in form of Seurat objects, txt and csv files as well as R scripts for data analysis and Figure generation.

A summary of the content is provided in the following.

R scripts

Script to run Machine learning models predicting group specific marker genes: CML_Find_Markers_Zenodo.R
Script to reproduce the majority of Main and Supplementary Figures shown in the manuscript: CML_Paper_Figures_Zenodo.R
Script to run inferCNV analysis: inferCNV_Zenodo.R Script to plot NATMI analysis results:NATMI_CvsA_FC0.32_Updown_Column_plot_Zenodo.R Script to conduct sub-clustering and filtering of NK cells NK_Marker_Detection_Zenodo.R

Helper scripts for plotting and DEG calculation:ComputePairWiseDE_v2.R, Seurat_DE_Heatmap_RCA_Style.R

RDS files

General scRNA-seq Seurat objects:

• scRNA-seq seurat object after QC, and cell type annotation used for most analysis in the manuscript: DUKE_DataSet_Doublets_Removed_Relabeled.RDS
• scRNA-seq including findings e.g. from NK analysis used in the shiny app: DUKE_final_for_Shiny_App.rds
• Neighborhood enrichment score computed for group A across all HSPCs: Enrichment_score_global_groupA.RDS
• UMAP coordinates used in the article: Layout_2D_nNeighbours_25_Metric_cosine_TCU_removed.RDS

SCENIC files:

• Regulon set used in SCENIC: 2.6_regulons_asGeneSet.Rds
• AUC values computed for regulons: 3.4_regulonAUC.Rds
• MetaData used in SCENIC cellInfo.Rds
• Group specific regulons for LCS: groupSpecificRegulonsBCRAblP.RDS
• Patient specific regulons for LSC: patientSpecificRegulonsBCRAblP.RDS
• Patient specificity score for LSC: PatientSpecificRegulonSpecificityScoreBCRAblP.RDS
• Regulon specificty score for LSC: RegulonSpecificityScoreBCRAblP.RDS

BCR-ABL1 inference:

• HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label.RDS
• UMAP for HSC with inferred BCR-ABL1 label: HSCs_CML_with_BCR-Abl_label_UMAP.RDS
• HSPCs with BCR-ABL1 module scores: HSPC_metacluster_74K_with_modscore_27thmay.RDS

NK sub-clustering and filtering:

• NK object with module scores: NK_8617cells_with_modscore_1stjune.RDS
• Feature genes for NK cells computed with DubStepR: NK_Cells_DubStepR
• NK cells Seurat object excluding contaminating T and B cells: NK_cells_T_B_17_removed.RDS
• NK Seurat object including neighbourhood enrichment score calculations: NK_seurat_object_with_enrichment_labels_V2.RDS

txt and csv files:

• Proportions per cluster calculated from CyTOF: CyTOF_Proportions.txt
• Correlation between scRNAseq and CyTOF cell type abundance: scRNAseq_Cor_Cytof.txt
• Correlation between manual gating and FlowSOM clustering: Manual_vs_FlowSOM.txt
• GSEA results:
  • HSPC, HSC and LSC results: FINAL_GSEA_DATA_For_GGPLOT.txt
  • NK: NK_For_Plotting.txt
• TFRC and HLA expression: TFRC_and_HLA_Values.txt
• NATMI result files:
  • UP-regulated_mean.csv
  • DOWN-regulated_mean.csv
• Gene position file used in inferCNV: inferCNV_gene_positions_hg38.txt
• Module scores for NK subclusters per cell: NK_Supplementary_Module_Scores.csv

Compressed folders:

• All CyTOF raw data files: CyTOF_Data_raw.zip
• Results of the patient-based classifier: PatientwiseClassifier.zip
• Results of the single-cell based classifier: SingleCellClassifierResults.zip

For general new data analysis approaches, we recommend the readers to use the Seruat object stored in DUKE_final_for_Shiny_App.rds or to use the shiny app(http://scdbm.ddnetbio.com/) and perform further analysis from there.

RAW data is available at EGA upon request using Study ID: EGAS00001005509

Datasets and Code accompanying the new release of RCA, RCA2. The R-package for RCA2 is available at GitHub: https://github.com/prabhakarlab/RCAv2/

The datasets included here are:

• Datasets required for a characterization of batch effects:
  • merged_rna_seurat.rds
  • de_list.rds
  • mergedRCAObj.rds
  • merged_rna_integrated.rds
• 10X_PBMCs.RDS: Processed 10X PBMC data RCA2 object (10X PBMC example data sets )
• NBM_RDS_Files.zip: Several RDS files containing RCA2 object of Normal Bone Marrow (NBM) data, umap coordinates, doublet finder results and metadata information (Normal Bone Marrow use case)
• Dataset used for the Covid19 example:
  • blish_covid.seu.rds
  • rownames_of_glocal_projection_immune_cells.txt
• Data sets used to outline the ability of supervised clustering to detect disease states:
  • 809653.seurat.rds
  • blish_covid.seu.rds
• Performance benchmarking results:
  • Memory_consumption.txt
  • rca_time_list.rds

The R script provides R code to regenerate the main paper Figures 2 to 7 modulo some visual modifications performed in Inkscape.

Provided R scripts are:

• ComputePairWiseDE_v2.R (Required code for pairwise DE computation)
• RCA_Figure_Reproduction.R