KMT2C deficiency promotes small cell lung cancer metastasis through DNMT3A-mediated epigenetic reprogramming

This page recorded the codes and data used and mentioned in Nature Cancer. And you could downloaded this paper by clicking here

​ A prevalent bottleneck for metastasis researches is the lack of resected samples because patients with metastases won’t be treated with surgery, which is the case for the majority of SCLC patients. Therefore, mouse models of SCLC which can recapitulate the clinical features of the human disease are especially of value.

​ As we known, there were little omics data from SCLC patients and even from murine SCLC. Therefore, we accumulated all attainable omics data and stored them here. To make it easier to use, we pre-processed them by standard pipeline, and the detail codes of processing also recorded in this page.

​ We hope this page could be real-time maintained. If you have generated and submitted SCLC data we haven’t recorded or mentioned in list, you could contact us anytime and we would like to integrate your resource here.

​ To increase the reproducibility of omics analysis mentioned in our paper, we will record and submit all scripts, raw data and processed data for sharing our bioinformatics tools and academic principles. We have submitted all processed data on Figshare and you could download them followed according agreements. Raw data also had been submitted on GEO database, and you could be accessible by clicking GSE161570. Besides, we also recorded our figures-making codes in Markdown files, which could be regarded as tutorials for beginner of omics analysis. More detail descriptions of the submitted data you could get in following pages.

​ In this project, to better analyze and visualize our data, we have modified a lot of function formulas from previous perfect packages. And we also be happy to share them to help you improve your work efficiency in omics data analysis and visualizations.

All SCLC patients’ data files

This page recorded all omics data about SCLC patients that were mentioned and analyzed in our paper. And the detail list was displayed as following:

Source_of_Data	Data_type	File_type	Species	Disease	Doi_number	Clinical_info	Normal_samples	ID
Nature Genetics	42WES	.csv	HUMAN	SCLC	10.1038/ng.2405	No	No	SCLC_DATA_1
Nature Genetics	29 WES/ 2WGS/ 15RNA-seq	.csv	HUMAN	SCLC	10.1038/ng.2396	Yes	No	SCLC_DATA_2
Nature	110 WGS/ 81 RNA-seq	.csv	HUMAN	SCLC	10.1038/nature14664	Yes	No	SCLC_DATA_3
PLOS Genetics	86 RNA-seq	.csv	HUMAN	SCLC	10.1371/journal.pgen.1005895	Yes (but only part of patients matches to omics data)	Yes	SCLC_DATA_4
Nature Cancer	8 scRNA-seq (but we only get the 7 scRNA-seq data)	.rds	HUMAN	SCLC	10.5281/zenodo.3574846	No	No	SCLC_DATA_5
Nature Cancer	90RNA-seq	.csv	HUMAN	SCLC	10.1038/s43018-019-0020-z	Yes	No	SCLC_DATA_6

The tree map showed the files detail as following:

.
├── SCLC_DATA_1
│   ├── SCLC_NG_SOX2_Clinical_Normal.csv
│   ├── SCLC_NG_SOX2_Clinical_Tumor.csv
│   └── SCLC_NG_SOX2_Mut.csv
├── SCLC_DATA_2
│   ├── SCLC_NG1_Clnical.csv
│   ├── SCLC_NG1_Mut.csv
│   └── SCLC_NG1_RNA.csv
├── SCLC_DATA_3
│   ├── SCLC_Nature_Clinical.csv
│   ├── SCLC_Nature_Mut.csv
│   └── SCLC_Nature_RNA.csv
├── SCLC_DATA_4
│   └── ALL_tumor_vs_normal_tpm.csv
├── SCLC_DATA_5
│   └── scRNA_SCLC_CTC.rds
└── SCLC_DATA_6
    ├── CDX_data.csv
    └── CDX_META_INFO.csv
6 directories, 13 files

And if you want to use these SCLC patients data, you could follow our pipelines by clicking here.

And if you want to use modified function formulas mentioned in our paper, you could see the source codes by clicking here.

All raw data files

The tree map showed the files detail as following:

.
├── ATAC-seq
│   ├── PRM1_R1.fq.gz
│   ├── PRM1_R2.fq.gz
│   ├── PRM2_R1.fq.gz
│   ├── PRM2_R2.fq.gz
│   ├── PRMK1_R1.fq.gz
│   ├── PRMK1_R2.fq.gz
│   ├── PRMK2_R1.fq.gz
│   ├── PRMK2_R2.fq.gz
│   ├── PRM_Liver1_R1.fq.gz
│   ├── PRM_Liver1_R2.fq.gz
│   ├── PRM_Liver2_R1.fq.gz
│   └── PRM_Liver2_R2.fq.gz
├── RNA-seq
│   ├── RNAseq1_Lung1_R1.fq.gz
│   ├── RNAseq1_Lung1_R2.fq.gz
│   ├── RNAseq1_Lung2_R1.fq.gz
│   ├── RNAseq1_Lung2_R2.fq.gz
│   ├── RNAseq1_Lung3_R1.fq.gz
│   ├── RNAseq1_Lung3_R2.fq.gz
│   ├── RNAseq1_PRM_1_R1.fq.gz
│   ├── RNAseq1_PRM_1_R2.fq.gz
│   ├── RNAseq1_PRM_2_R1.fq.gz
│   ├── RNAseq1_PRM_2_R2.fq.gz
│   ├── RNAseq1_PRM_3_R1.fq.gz
│   ├── RNAseq1_PRM_3_R2.fq.gz
│   ├── RNAseq2_PRM_2_R1.fq.gz
│   ├── RNAseq2_PRM_2_R2.fq.gz
│   ├── RNAseq2_PRM_3_R1.fq.gz
│   ├── RNAseq2_PRM_3_R2.fq.gz
│   ├── RNAseq2_PRMK_1_R1.fq.gz
│   ├── RNAseq2_PRMK_1_R2.fq.gz
│   ├── RNAseq2_PRMK_2_R1.fq.gz
│   ├── RNAseq2_PRMK_2_R2.fq.gz
│   ├── RNAseq2_PRMK_3_R1.fq.gz
│   ├── RNAseq2_PRMK_3_R2.fq.gz
│   ├── RNAseq3_sgMeis2_1_R1.fq.gz
│   ├── RNAseq3_sgMeis2_1_R2.fq.gz
│   ├── RNAseq3_sgMeis2_2_R1.fq.gz
│   ├── RNAseq3_sgMeis2_2_R2.fq.gz
│   ├── RNAseq3_sgMeis2_3_R1.fq.gz
│   ├── RNAseq3_sgMeis2_3_R2.fq.gz
│   ├── RNAseq3_sgScr_1_R1.fq.gz
│   ├── RNAseq3_sgScr_1_R2.fq.gz
│   ├── RNAseq3_sgScr_2_R1.fq.gz
│   ├── RNAseq3_sgScr_2_R2.fq.gz
│   ├── RNAseq3_sgScr_3_R1.fq.gz
│   └── RNAseq3_sgScr_3_R2.fq.gz
├── scRNA-seq
│   ├── Abdominal_LN_R1.fastq.gz
│   ├── Abdominal_LN_R2.fastq.gz
│   ├── Left_lung_R1.fastq.gz
│   ├── Left_lung_R2.fastq.gz
│   ├── Liver_R1.fastq.gz
│   ├── Liver_R2.fastq.gz
│   ├── Thoracic_LN_R1.fastq.gz
│   └── Thoracic_LN_R2.fastq.gz
└── WGBS
    ├── PRM_1_clean_R1.fq.gz
    ├── PRM_1_clean_R2.fq.gz
    ├── PRM_2_clean_R1.fq.gz
    ├── PRM_2_clean_R2.fq.gz
    ├── PRMK_1_clean_R1.fq.gz
    ├── PRMK_1_clean_R2.fq.gz
    ├── PRMK_2_clean_R1.fq.gz
    └── PRMK_2_clean_R2.fq.gz
4 directories, 62 files

And if you want to use our data, you could download them by clicking GSE161570.

All processed data files

In our paper, we used bulk omics data to deeply illustrate academic principles about SCLC metastasis, including bulk RNA-seq, bulk ATAC-seq and bulk WGBS-seq. To increase the reproducibility, we would like to share all processed files that you could download from GEO and figshare.The detail information of each processed file were displayed by tree map. And analysis codes of bulk omics data were recorded and collected that would help you understand how’s the processed files meaning and how’s them could be used in your study.

And you could get the access of these processed data files by clicking here.

Overview of All processed files and modified codes

.
├── ATAC-seq
│   ├── gappedPeak_files
│   └── RData_files
├── modified_codes
│   └── MyBestFunction_scRNA.R
├── RNA-seq
│   ├── csv_files
│   └── RData_files
├── SCLC_Patient_data
│   ├── SCLC_DATA_1
│   ├── SCLC_DATA_2
│   ├── SCLC_DATA_3
│   ├── SCLC_DATA_4
│   ├── SCLC_DATA_5
│   └── SCLC_DATA_6
├── sc-RNAseq
│   ├── Chapter1
│   └── Chapter2
└── WGBS
    ├── DMR_files
    ├── DMS_files
    ├── for_plot_files
    └── viewBS_files
22 directories, 1 file

Bulk RNA-seq processed data files

All the RNA-seq processed data files were listed in the tree map as following:

.
├── csv_files
│   ├── 1_RNAseq1_PRM_VS_Lung_result_all.csv
│   ├── 1_RNAseq1_PRM_VS_Lung_result_DEGs.csv
│   ├── 1_RNAseq2_PRMK_VS_PRM_result_all.csv
│   ├── 1_RNAseq2_PRMK_VS_PRM_result_DEGs.csv
│   ├── 1_RNAseq3_sgMeis2_VS_sgScr_result_all.csv
│   ├── 1_RNAseq3_sgMeis2_VS_sgScr_result_DEGs.csv
│   ├── RNAseq1_sampleTable.csv
│   ├── RNAseq2_sampleTable.csv
│   └── RNAseq3_sampleTable.csv
└── RData_files
    ├── ebg_GRCm38.RData
    ├── RNAseq1_se.RData
    ├── RNAseq2_se.RData
    ├── RNAseq3_se.RData
    └── txdb_GRCm38.RData
2 directories, 14 files

Bulk ATAC-seq processed data files

All the ATAC-seq processed data files were listed in the tree map as following:

.
├── gappedPeak_files
│   ├── all_PRM1_peaks.gappedPeak
│   ├── all_PRM2_peaks.gappedPeak
│   ├── all_PRMK1_peaks.gappedPeak
│   ├── all_PRMK2_peaks.gappedPeak
│   ├── all_PRM_liver1_peaks.gappedPeak
│   └── all_PRM_liver2_peaks.gappedPeak
└── RData_files
    ├── 1_meta_PRM_vs_PRM.rds
    ├── 1_met_PRM_VS_pri_PRM_res_symbol.rds
    ├── 1_pri_PRMK_VS_pri_PRM_res_symbol.rds
    ├── 1_PRMK_vs_PRM.rds
    ├── atacDDS.rds
    ├── countsFromATAC.RData
    ├── met_PRM_VS_pri_PRM_res_symbol.rds
    └── pri_PRMK_VS_pri_PRM_res_symbol.rds
2 directories, 14 files

Bulk WGBS-seq processed data files

All the WGBS-seq processed data files were listed in the tree map as following:

.
├── DMR_files
│   ├── 1_PRMK_vs_PRM_DMR_all_cpgs_anno.csv
│   ├── 1_PRMK_vs_PRM_DMR_all_gene_anno.csv
│   └── 1_result_all_matrix_CG.txt
├── DMS_files
│   └── 1_PRMK_vs_PRM_DMS_all.csv
├── for_plot_files
│   └── all_matrix.tar.gz
└── viewBS_files
    └── annots_mm10_cpgs_ViewBS.bed
4 directories, 6 files

Single cell RNA-seq processed data files

Besides, we also used single cell RNA-seq(scRNA-seq) data to revel our main logic conclusions and academic principles. As we known, analysis of scRNA-seq sometimes were really complicated which integrated many steps, such as normalization, batch effect reducing, cells clustering, cluster identification, cluster-specific signatures identification, trajectory constructing, dynamics expressing genes detecting, biology pathways enrichments and so on. As new technologies were into explosive era, there were thousands of algorithms and methods to deal with one part of them mentioned above, which caused benchmark report of these methods were not comprehensive. Hence, it’s really difficult to conclude our analysis pipeline and methods were perfect, but our analysis workflow sufficiently supported our main logic conclusions and academic principles definitely. For increasing our data reproducibility, we recorded and collected all processed data and codes files which also had been submitted on github, figshare and GEO.

And the detail lists were displayed as following:

.
├── Chapter1
│   ├── cellranger_out_files
│   │   ├── Abdominal_LN
│   │   │   └── filtered_feature_bc_matrix
│   │   │       ├── barcodes.tsv.gz
│   │   │       ├── features.tsv.gz
│   │   │       └── matrix.mtx.gz
│   │   ├── Left_lung
│   │   │   └── filtered_feature_bc_matrix
│   │   │       ├── barcodes.tsv.gz
│   │   │       ├── features.tsv.gz
│   │   │       └── matrix.mtx.gz
│   │   ├── Liver
│   │   │   └── filtered_feature_bc_matrix
│   │   │       ├── barcodes.tsv.gz
│   │   │       ├── features.tsv.gz
│   │   │       └── matrix.mtx.gz
│   │   └── Thoracic_LN
│   │       └── filtered_feature_bc_matrix
│   │           ├── barcodes.tsv.gz
│   │           ├── features.tsv.gz
│   │           └── matrix.mtx.gz
│   ├── Others_processed
│   │   ├── ASCL1_Lineage1_sigma_20_peudotime_with_anno.pdf
│   │   ├── ASCL1_Lineage1_sigma_20_SCLC_classification.csv
│   │   ├── CCLE_Metastasis_UP_genes.csv
│   │   ├── pri_sig_with_Nature_Patients_survival_clu1.svg
│   │   ├── sample_info.csv
│   │   ├── SCLC_Nature_Clinical.csv
│   │   ├── SCLC_Nature_RNA.csv
│   │   ├── v2_batch_effect_all_cells.png
│   │   ├── v2_dynamics_trend.svg
│   │   ├── v2_Neurod1_Lineage_tranje_GO_BP.svg
│   │   ├── v2_percentage_summary.svg
│   │   ├── v2_SCLC_ASCL1_pseudotime_with_Nature_Patients_survival_clu1.svg
│   │   ├── v2_SCLC_map.png
│   │   ├── v3_ASCL1_Lineage1_sigma_20_peudotime_with_anno.pdf
│   │   └── v3_batch_effect_all_cells.png
│   └── rds_files
│       ├── all_merge_by_nothing_Only_LL_LT.rds
│       ├── all_merge_by_nothing.rds
│       ├── ASCL1_Lineage1_sigma_20_col_bin.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_annotation_row1_all_v2.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_annotation_row1_GO_v2.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_genes.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_heatmap_All_genes_m_normalised.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_heatmap_All_genes_m.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_heatmap_anno.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_heatmap_matrix.rds
│       ├── ASCL1_Lineage1_sigma_20_peudotime_heatmap_m.rds
│       ├── ASCL1_Lineage1_sigma_20.rds
│       ├── ASCL1_Lineage1_sigma_20_remind.rds
│       ├── CTX_Metastasis_UP.rds
│       └── Only_LL_LT.markers.rds
└── Chapter2
    ├── cellranger_out_files
    │   ├── KMT2C_KO
    │   │   └── filtered_feature_bc_matrix
    │   │       ├── barcodes.tsv.gz
    │   │       ├── features.tsv.gz
    │   │       └── matrix.mtx.gz
    │   └── scramble
    │       └── filtered_feature_bc_matrix
    │           ├── barcodes.tsv.gz
    │           ├── features.tsv.gz
    │           └── matrix.mtx.gz
    ├── Others_processed
    │   └── only_sub_sc_both_pseudotime_all1.pdf
    └── rds_files
        ├── CTX_Metastasis_UP.rds
        ├── only_sub_sc_0123_30_sub.rds
        ├── only_sub_sc1_both_lineage.rds
        ├── only_sub_sc_both_pseudotime_heatmap_anno_all.rds
        └── only_sub_sc_both_pseudotime_heatmap_matrix_all.rds

20 directories, 54 files

.bam files were so large to submit on database, if you need further information requests for resources and reagents, you should connect the lead contact.

Codes of analyzing and visualization

Codes of Single cell RNA-seq analysis

​ To show how we analysis scRNA-seq steps by steps, we collected detail processed information and stored in Markdown files, recorded the quality controling, batch effect reducing, dimension reducing, cells clustering, pseudo time constructing, dynamics expression genes identifying and pathways enriching.

​ Our single cell RNA-seq data were generated from two individual biological experiments, including SCLC tissues from primary sites and metastatic lesions, and normal lung organoids with sgscramble and sgKmt2c. Hence, we divided our analysis code into two chapters.

Chapter1 is the code for SCLC data with primary sites and metastatic lesions. And you could access this code by click here.

Chapter2 is the code for normal lung organoids with sgscramble and sgKmt2c. And you could access this code by click here.

Codes of Bulk RNA-seq analysis

Bulk_RNA is the code for Bulk RNA-seq analysis in SCLC data. And you could access this code by click here.

Codes of Bulk ATAC-seq analysis

Bulk_ATAC is the code for Bulk RNA-seq analysis in SCLC data. And you could access this code by click here.

Codes of Bulk WGBS-seq analysis

Bulk_WGBS is the code for Bulk RNA-seq analysis in SCLC data. And you could access this code by click here.

Citation

Our paper has been published on Nature Cancer

You could downloaded raw data from GEO Database GSE161570

You could downloaded processed data from Figshare Database