Find research datasets worth reusing

opencc-by-4.0Apr 2024View details →

Primary Breast Tumor Atlas

Integrated scRNA-seq atlas of primary breast tumors containing 236,363 cells from 119 biopsy samples across eight original source datasets. <a title="Persistent link using digital object identifier" href="https://doi-org.foyer.swmed.edu/10.1016/j.xcrm.2024.101511" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.xcrm.2024.101511</a>

opencc-by-4.0Nov 2021View details →

Dataset of "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"

The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings identified TWIST1 as novel therapeutic target in NB.

opencc-by-4.0Nov 2021View details →

Dataset for "TWIST1 expression is associated with high-risk neuroblastoma and promotes primary and metastatic tumor growth"

The embryonic transcription factors TWIST1/2 are frequently overexpressed in cancer, acting as multifunctional oncogenes. Here we investigate their role in neuroblastoma (NB), a heterogeneous childhood malignancy ranging from spontaneous regression to dismal outcomes despite multimodal therapy. We first reveal the association of TWIST1 expression with poor survival and metastasis in primary NB, while TWIST2 correlates with good prognosis. Secondly, suppression of TWIST1 by CRISPR/Cas9 results in a reduction of tumor growth and metastasis in immunocompromised mice. Moreover, TWIST1 knock-out tumors displays a less aggressive cellular morphology and a reduced disruption of the extracellular matrix (ECM) reticulin network. Additionally, we identify a TWIST1-mediated transcriptional program associated with dismal outcome in NB and involved in the control of pathways mainly linked to the signaling, migration, adhesion, the organization of the ECM, and the tumor cells versus tumor stroma crosstalk. Taken together, our findings confirm TWIST1 as promising therapeutic target in NB. This dataset comprise images  (.ndpi files) of anti-F4/80 IHC staining used for the quantification of macrophages in subcutaneous and orthotopic neuroblastoma xenografts derived from SK-N-Be2c cells expressing TWIST1 or knocked out for TWIST1 through CRISR/Cas9.

opencc-by-4.0Mar 2024View details →

Dataset of 'HIV infection is associated with compromised tumor microenvironment adaptive immune reactivity in Hodgkin Lymphoma'

§  :The data were generated using the i) GeoMx Digital Spatial Profiler (DSP) platform developed by Nanostring Technologies. GeoMx analysis utilizes in situ RNA hybridization with Whole Atlas Transcriptome probe (Nanostring) and ii) HTG platform (Immune Response kit) Our dataset comprises samples from donors categorized as HLposHIVnegEBVneg, HLposHIVposEBVpos, or HLposHIVnegEBVpos (HL: Hodgkin Lymphoma). Regions of interest (ROI) were spatially profiled to capture distinct molecular signatures associated with these donor categories.

opencc-by-4.0Jan 2024View details →

Datasets: Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment

The following are necessary data files for the manuscript "Natural killer cells associate with malignant epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment": <ul> <li>.zip files for TMA_1, TMA_2, TMA_3, and TMA_4 are .mcd files acquired from imaging mass cytometry (IMC) for each slide of the pancreas TMA slide series</li> <li>pancreas_TMA_sample_info.xlxs includes info on all samples of the TMA slide series that were imaged by IMC</li> <li>custom_gates_0.zip includes histoCAT-derived single cell data files from all IMC samples in the pancreas TMA to be used for single cell analyses in R</li> <li>PDAC_IMC.RDS is a Seurat object of the IMC-derived PDAC single cell data to use for single cell and spatial analyses</li> <li>PDAC_sce is a SingleCellExperiment object of IMC-derived PDAC single cell data to use for spatial analyses</li> <li>mat.RDS is a distance matrix of PDAC cell types to use in R to generate network graph (Figure 2)</li> </ul>

opencc-by-4.0Nov 2022View details →

Spatial immunophenotyping of the tumor microenvironment in non-small cell lung cancer

A dataset with spatial immune cell information on a lung cancer cohort from Uppsala University Hospital, Sweden, with anonymized clinical data. For more information please refer to the 'readme' file and the original study (https://doi.org/10.1016/j.ejca.2023.02.012).

opencc-by-4.0Dec 2019View details →

Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells

Cell culture and data set Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4LUC+ cells (or LM2-4), a metastatic variant of the  human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7]. <ul> </ul> Tumor injections LM2-4LUC+ cells were orthotopically implanted (106 cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice. Tumor measurements Tumor size was measured regularly with calipers to a maximum volume of  2 cm3, calculated by the formula  \(V = \frac{\pi}{6} w^2 L\) (ellipsoid) where L is the largest and w is the smallest tumor diameter. Please cite: Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>   In the file, the columns correspond to: <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm3)</li> </ul>   References [1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, 10(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800 [2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389. [3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217. [4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989 [5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021 [6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017 [7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>

Immunofluorescence staining of a human kidney (#4, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#4). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#3, tumor area) obtained by MELC

19 marker MELC run in a human tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#3, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#3). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#2, tumor area) obtained by MELC

19 marker MELC run in a human tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#2, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#2). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

Immunofluorescence staining of a human kidney (#1, peri-tumor area) obtained by MELC

19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm. All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 216).

opencc-by-4.0Apr 2016View details →

Fluorescent Confocal Laser Scanning Microscopy of White Blood Cells, Cancer Cell Line MCF7, and Mixtures of these Cells: A Model System for Circulating Tumor Cell Biomarker Evaluation V.1

This is a confocal laser scanning microscopy data set of white blood cells (leukocytes), the cancer cell line MCF7, and mixtures of these cells acquired on a Zeiss LSM 780 microscope in the University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core. Cells are fluorescently labeled for DNA with DAPI (Sigma D9542), lipids with Bodipy 495/503 (Thermo Fisher D3922), the filament protein cytokeratin (CK) with pan-cytokertain-alexa555 antibodies (Cell Signaling Technologies 3478S) and the surface membrane antigen CD45 with CD45-alexa647 antibodies (Biolegend 304020). Bodipy was excited with a continuous wave (CW) 488 nm laser, alexa555 was excited with CW 561 nm laser, and alexa647 was excited with a CW 633 nm laser. The acquiring instrument does not have a CW 405 nm source so DAPI was excited by two photon process using a Coherent Cameleon ultrafast pulsed laser tuned to 765 nm. The objective used was a Zeiss Plan-Apochromat 20x, 0.8 NA, air. The data consists of 4 channel 8x8 mosaic z-stacks. The Zeiss software performed stitching of the mosaics. These stitched data images are included and marked with _Stitched at the end. Those interested in performing the stitching themselves can do this with the raw data files (without the _Stitched). The jpeg images are processed from the stitched LSM images. The LSM files contain additional meta data on the experiment including power levels and acquisition settings. The _Stiched .lsm files will load in ImageJ (tested with V.1.49) as 4 channel 3 stack images. This data is a model system for evaluating the DNA/Lipids/CK/CD45 biomarker panel to identify circulating tumor cells (CTCs). The D- population of the model is the WBCs and the D+ population is the MCF7 cancer cell line. The amount of separation the biomarker panel plus analysis algorithm can produce between these populations (D+/D-) is an estimate the sensitivity and specificity of the biomarker panel plus algorithm to CTCs. Experiments generating the data were performed over the course of 15 days. Peripheral blood samples were collected from the Gynecological Tissue and Fluid Bank (COMIRB 07-0935 / COMIRB 05-1081) from consenting patients undergoing surgery at the University of Colorado Hospital. Blood samples were used the same day they were collected. Blood samples were collected from 3 patients with benign conditions, labeled WBBN#, and 3 patients with ovarian cancer, labeled WBCA#. We do not expect there to be any difference in the isolated white blood cells samples prepared from the cancer and benign patients. Samples were stored at room temperature until white blood cells were isolated. Mixed samples were prepared by passaging a MCF7 flask and mixing it with isolated white blood cells before fixation. A schedule showing the time duration between collection, processing and imaging is included as “experimental schedule.gif”. The MCF7 cancer cell line was a kind gift from Dr. Heide Ford. Genomic DNA was isolated from the MCF7 cell line after the experiment and sent for cell line authentication. The gDNA was a match to MCF7. The authentication report and data are included in this submission. CD45 antibodies were exhausted on day 7. New antibody was purchased and received on day 8. The day 7 images only has labels for DAPI and Bodipy. The samples prepared with the old antibodies on days 4 and 7 were relabeled and imaged with the new antibodies on days 14 and 15. This labeling was also done to confirm the pan-CK antibodies remained good since they are dim in the MCF7 cells imaged on days 12 and 13. The pan-CK on days 14 and 15 looks the same as it did on days 5 and 7 confirming the antibodies are good. Four of the filters containing cells were not sufficiently flat to be acquired with a 3 slice z-stack so a 5 slice z-stack was used. These files have been zipped to compress them under the 2 GB limit permitted by zenodo.org Further information on how these samples were prepared, processed, and analyzed can be found in our associated 2016 SPIE Photonics West BIOS conference proceeding titled, “Quantitative image cytometry measurements of lipids, DNA, CD45 and cytokeratin for circulating tumor cell identification in a model system”, http://dx.doi.org/10.1117/12.2222317. This work was supported by funding provided to the University of Colorado Cancer Center by the American Cancer Society and awarded as Institutional Research Grant Number 57-001-53, by funding provided by the Defense Advanced Research Projects Agency under grant number N66001-10-4035, and by funding provided by NIH/NCATS Colorado CTSI Grant Number TL1 TR001081. The University of Colorado Anschutz Medical Campus Advanced Light Microscopy Core is also supported in part by NIH/NCATS Colorado CTSI Grant Number UL1 TR001082. The funders had no role in the study design, data collection, analysis, or decision to publish.

opencc-by-4.0Oct 2023View details →

2 million histological images of breast cancer tumors with her2 labels

Data Description This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue. The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded. A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high. The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches. A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+. The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil

opencc-by-4.0Nov 2024View details →

Single-cell RNA-seq profiles of tumor-bearing mice treated with PAGln with or without anti-PD-1

single-cell RNA sequencing (scRNA-seq) profiles of  tumor-bearing mice treated using Phenylacetylglutamine (PAGln) with or without anti-PD-1 were performed to compare the alterations of immune microenvironment affected by PAGln under the condition of anti-PD-1 treatment.

opencc-by-4.0Dec 2024View details →

Additional data: Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring

This repository provides additional data for the manuscript titled "Longitudinal single-cell multiomic atlas of high-risk neuroblastoma reveals chemotherapy-induced tumor microenvironment rewiring", currently under revision at Nature Genetics. The primary data cohort has been deposited in the HTAN data portal. This repository includes processed 10x Xenium spatial transcriptomic data for six TH-MYCN mice (three chemotherapy-treated and three treatment-naive) as well as processed scRNA-seq data for CHLA15 and CHLA20 neuroblastoma (NBL) cells. The scRNA-seq data includes mono-cultured, co-cultured cells with THP-1 macrophages, and co-culture cells treated with Afatinib/CRM197.

opencc-by-4.0Jan 2024View details →

Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome

Supplementary dataset for manuscript: Cell-free DNA Fragmentomics and Second Malignant Neoplasm Risk in Patients with PTEN Hamartoma Tumor Syndrome Affiliations: 1Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA 2Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, OH 44195, USA 3Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center, Cleveland Clinic, Cleveland, OH 44195, USA 4Center for Personalized Genetic Healthcare, Medical Specialties Institute, Cleveland Clinic, Cleveland, OH 44195, USA 5Center for Immunotherapy and Precision Immuno-oncology, Cleveland Clinic, Cleveland, OH 44195, USA 6Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA 7Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA 8Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH 44106, USA Correspondence: Darren Liu, MD, MS (<a href="https://orcid.org/0000-0002-6703-1843">https://orcid.org/0000-0002-3693-5145)</a> Cleveland Clinic Genomic Medicine Institute 9500 Euclid Avenue, Cleveland, OH 44195, USA E-mail: liud5@ccf.org

opencc-by-4.0Sep 2020View details →

A Single-Cell Tumor Immune Atlas for Precision Oncology

Publication version of the Single-Cell Tumor Immune Atlas This upload contains: <ul> <li>TICAtlas.rds: an rds file containing a Seurat object with the whole Atlas</li> <li>TICAtlas.h5ad: an h5ad file with the whole Atlas</li> <li>TICAtlas_downsampled.rds: an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li>TICAtlas_downsampled.h5ad: an rds file containing a downsampled version of the Seurat object of the whole Atlas</li> <li>TICAtlas_metadata.csv: a comma-separated text file with the metadata for each of the cells</li> </ul> All the files contain the following patient/sample metadata variables: <ul> <li>patient: assigned patient identifiers</li> <li>nCountRNA and nFeatureRNA: number of UMIs and genes per cell</li> <li>percent.mt: percentage of mitochondrial genes</li> <li>gender: the patient's gender (male/female/unknown)</li> <li>source: dataset of origin</li> <li>subtype: cancer type (abbreviations as indicated in the preprint)</li> <li>kmeans_cluster: patients clusters, NA if filtered out before clustering</li> <li>lv1 and lv2: annotated cell type for each of the cells, two level annotation (lv2 has more cell types)</li> </ul> <pre> </pre> If you have any issues with the metadata (i.e. unexpected factors, NA values...) you can use the TICAtlas_metadata.csv file. For more information, <a href="https://genome.cshlp.org/content/early/2021/09/21/gr.273300.120.">read our paper</a>, <a href="https://github.com/Single-Cell-Genomics-Group-CNAG-CRG/Tumor-Immune-Cell-Atlas">check our GitHub</a> and our <a href="https://singlecellgenomics-cnag-crg.shinyapps.io/TICA/">ShinyApp</a>. h5ad files can be read with Python using <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>, rds files can be read in R using <a href="https://satijalab.org/seurat/">Seurat</a>. For format conversion between AnnData and Seurat we recommend <a href="https://mojaveazure.github.io/seurat-disk/">SeuratDisk</a>. For other single-cell data formats you can use <a href="https://github.com/cellgeni/sceasy">sceasy</a>.