TCGA RNA-seq Survival Analysis

@ASirbescu

Hello, With my background in Data Analysis, Statistical Analysis and Python programming, I can bring your TCGA RNA-seq Survival Analysis project to a successful conclusion. I am no stranger to working with high-volume omics data as well as Python-driven analysis workflows, which are the core of this project. My broad skillset spans from preprocessing and normalizing RNA-seq count data to creating publication-quality plots and reproducible scripts. I fully understand the significance of robustness in scientific analysis. Thus, you can count on me to ensure that all your recovery scripts and metrics can be recreated with a narrow 5% margin of error as per accepted guidelines. This notion guarantees that any future refinements or modifications will be smoothly implemented without sacrificing accuracy or reliability. I am particularly looking forward to employing my expertise in GSEA (Gene Set Enrichment Analysis) for exploring analyzing ranked gene lists derived from Cox statistics. My commitment to pristine code, attention to detail, efficient delivery and prompt communication aligns perfectly with your project's requirements - a perfect fit, isn't it? Don't hesitate to assign me this task; I'll need immediately access the cohort list and file structure so we can hit the ground running on this vital scientific endeavor together. Thanks!

@TheRSTeam

Hi, I see you’re looking for a focused survival analysis linking RNA-seq data to overall and progression-free survival outcomes for multiple cancer types, with a workflow covering normalization, survival modeling, and GSEA integration. With expertise in bioinformatics and survival analysis, I can: Pre-process and normalize large RNA-seq count datasets, ensuring accurate matching to clinical records. Build Kaplan-Meier curves and multivariable Cox proportional-hazards models to identify genes significantly associated with survival outcomes, adjusting for covariates like age, stage, and subtype. Perform GSEA (Gene Set Enrichment Analysis) on Cox-ranked gene lists to uncover pathways predicting patient prognosis. Deliver all scripts (bash, R, Python, and GSEA command-line) with a detailed README for full reproducibility, intermediate and final result files, and publication-quality plots. Provide a concise methods and results report summarizing the approach, key findings, and limitations. I have extensive experience handling large omics datasets and ensuring reproducibility in Linux environments. Could you share more details about the cancer types or specific pathways of interest? Let’s collaborate to deliver actionable survival insights—I’m ready to start!

@KolmanOuassou

Hi, I understand you’re seeking a comprehensive survival analysis for large TCGA RNA-seq datasets, focusing on gene-level and pathway-level associations with overall and progression-free survival outcomes. I specialize in omics data analysis and can assist by: Pre-processing and normalizing RNA-seq count data while matching each sample to its corresponding clinical record accurately. Conducting survival analysis using Kaplan-Meier curves and multivariable Cox proportional-hazards models, identifying genes with significant associations to survival outcomes after adjusting for key clinical covariates (age, stage, subtype, etc.). Running Gene Set Enrichment Analysis (GSEA) on Cox-ranked gene lists to uncover pathways predictive of patient prognosis. Providing reproducible scripts (R, Python, bash, or GSEA CLI) with clear instructions, intermediate results (normalized matrices, survival statistics, enrichment scores), and publication-quality visualizations. Delivering a concise report summarizing the methodology, findings, and any limitations for clarity and academic use. I am experienced in managing high-volume omics data and ensuring all analyses are reproducible on Linux systems. Could you share your cohort list and file structure to get started? Let’s work together to uncover impactful survival insights—I’m ready to begin!

@mykhaylob4

✋ Hi there. I can perform TCGA RNA seq survival analysis with full preprocessing, normalized expression pipelines, Cox regression modeling, and GSEA based pathway discovery with fully reproducible scripts and interpretable reporting. ✔️ I have solid experience analyzing high throughput omics datasets using R, Python, Bioconductor tools, survival packages, and GSEA workflows on Linux systems. In a previous project, I processed TCGA cohorts, matched clinical metadata, built Kaplan Meier curves, ran multivariable Cox proportional hazards models with covariate adjustments, and generated publication ready plots with reproducible notebooks and structured data outputs. ✔️ For your project, I will normalize raw counts using DESeq2 or edgeR, align samples with clinical records, and perform survival analysis using survival and survminer packages. I will compute hazard ratios, rank genes by Cox statistics, run GSEA command line analysis, and generate pathway enrichment reports with clear visualizations and reproducible pipelines. ✔️ I will deliver scripts, intermediate matrices, final statistics, SVG and PNG plots, and a concise methods and results report with clear documentation ensuring full reproducibility within your Linux environment. Let’s chat to review cohort structure and dataset layout. Best regards, Mykhaylo

@MQamar123

With my extensive experience in data analysis, particularly with Python, I can confidently offer you a detailed and insightful analysis of your TCGA RNA-seq dataset. Handling high-volume omics data is a welcome challenge for me as it allows me to deploy the skills and knowledge I've acquired across my seven years' career. Using publicly available libraries or the standalone GSEA software, I guarantee code execution that meets your acceptance criteria. Moreover, I would leverage GSEA (Gene Set Enrichment Analysis) on the ranked gene lists derived from the Cox statistics to uncover pathways that collectively predict patient prognosis. Throughout this process, you can expect transparency and clear documentation with script reproducibility of reported hazard ratios and enrichment scores within a 5% margin. In summary, my deep appreciation for detail, proficiency in handling large datasets, and knack for generating clear insights make me an ideal fit for this project. I look forward to discussing your cohort list and file structure to start delivering impactful outputs immediately!

@Bhartifreelancer

Hi there, I have checked your project, which requires TCGA RNA-seq Survival Analysis. I’m a professional academic writer with 7 years’ experience penning different academic research, thesis, essay, and dissertation on various subjects. I am well skilled with numerous citation and referencing styles, including APA, MLA, HARVARD, CHICAGO and Turbain along with skills of MATLAB, Python, SPSS, Machine Learning, Data Science, R Studio, Ansys, Cloud computing etc. Please feel free to connect with me in the chat. Regards Bharti

@lalitdeveloper7

Hi I can perform a fully reproducible survival analysis workflow on your TCGA RNA-seq datasets, including normalization, clinical matching, Kaplan-Meier and multivariable Cox modeling, and integrated GSEA to identify prognostic genes and pathways. The deliverables will include clean scripts/notebooks, publication-quality plots, all intermediate outputs, and a concise methods and results report that can be reproduced end-to-end on a fresh Linux environment. Please let me know further. Thanks

@EngrHamza3409

As a seasoned Full Stack Developer and Data Analyst, I've handled numerous large-scale data projects and have a deep understanding of high-volume omics data - making me a perfect fit for your TCGA RNA-seq Survival Analysis. I am adept at handling the full data lifecycle from cleaning to visualization, analysis, and report generation. Moreover, with 3+ years of experience, I'm well-versed in utilizing Python libraries like Pandas and GSEA command-line tools to deliver insightful survival analyses. One of my areas of expertise is creating reproducible scripts and notebooks - exactly what you require. Over the years, I've honed my skills to ensure that my codes execute perfectly on fresh Linux environments using only publicly available libraries or standalone software like GSEA. My commitment to strong documentation practices guarantees that you'll obtain not just the analytical workflow but also thorough explanations for all dependencies. Given that survival analyses involve critical healthcare data, reporting accurate and interpretable results is paramount. In this regard, my proficiency in generating publication-quality plots (SVG/PNG) and creating comprehensive methods & results document will be extremely valuable to your project. With me on board, you can have the utmost confidence that your project will be completed on time while adhering strictly to acceptance criteria. Excited about the prospect of contributing substantively to your cause.

@Hemantt841

With 7 years of experience in bioinformatics analysis, I am the best fit to complete this TCGA RNA-seq Survival Analysis project. I have the relevant skills to preprocess and normalize RNA-seq count data, build Kaplan-Meier curves, and run GSEA for pathway analysis. How I will complete this project: - Pre-process and normalize RNA-seq count data, linking samples to clinical records. - Build Kaplan-Meier curves and Cox proportional-hazards models for survival analysis. - Run GSEA on ranked gene lists to uncover pathways predicting patient prognosis. - Provide reproducible scripts, result files, and a methods & results document summarizing key findings. Tech stack I will use: - Programming languages: R, Python - Tools: GSEA command-line tool - File formats: bash, Word, PDF, Markdown, SVG, PNG I have worked on similar solutions in the past, ensuring clear and reproducible insights for high-volume omics data. My roadmap includes delivering all required deliverables and meeting acceptance criteria, ensuring that hazard ratios and enrichment scores can be reproduced within a 5% margin. I am confident in my ability to successfully complete this project and provide you with the insightful analysis you are looking for. Let's get started immediately by sharing the cohort list and file structure.

@Nadirzaman01

Dear Client, I am writing to express my strong interest in executing the survival analysis workflow for your TCGA datasets. With a professional background in bioinformatics and genomics, including a recent publication in Scientific Reports (DOI: 10.1038/s41598-024-62876-5), I have extensive experience managing high-volume omics data and translating raw sequences into clinically relevant insights. My technical expertise aligns perfectly with your required workflow: TCGA Data Handling: I am highly proficient in the Linux/Ubuntu environment and experienced in the precise preprocessing of RNA-seq counts and FPKM tables, ensuring seamless integration with complex clinical metadata. GSEA & Pathway Analysis: I have utilized GSEA (Gene Set Enrichment Analysis) extensively, ranking gene lists based on differential expression or Cox statistics to uncover the biological pathways (Hallmark, KEGG, or GO) driving patient prognosis. Reproducibility: I prioritize clean, modular code. I can deliver reproducible R or Python notebooks that are ready to run in any standard Linux environment, accompanied by detailed README files and publication-quality SVG/PNG plots. I am well-versed in the "publish-ready" standards of high-impact journals and am committed to delivering a methods-and-results document that is as insightful as it is accurate. I am ready to review your cohort list and file structure to begin the normalization and analysis immediately. Best regards, [Your Name]

@saliktechpro

Hey , I just finished reading the job description and I see you are looking for someone experienced in Statistics, Statistical Analysis, Data Science, Data Visualization, Bioinformatics, Data Analysis, Python and R Programming Language. This is something I can do. Please review my profile to confirm that I have great experience working with these tech stacks. While I have few questions: 1. These are all the requirements? If not, Please share more detailed requirements. 2. Do you currently have anything done for the job or it has to be done from scratch? 3. What is the timeline to get this done? Why Choose Me? 1. I have done more than 250 major projects. 2. I have not received a single bad feedback since the last 5-6 years. 3. You will find 5 star feedback on the last 100+ major projects which shows my clients are happy with my work. Timings: 9am - 9pm Eastern Time (I work as a full time freelancer) I will share with you my recent work in the private chat due to privacy concerns! Please start the chat to discuss it further. Regards, Salik.

@blazenicholas

I’ve worked extensively with TCGA RNA-seq data and survival analysis, so I understand your need for a clear, reproducible workflow linking gene expression to survival outcomes. Your focus on integrating gene-level and pathway analyses alongside clinical covariates is well noted. I may be new to Freelancer, but I bring solid experience to the table working with complex omics datasets. I can deliver clean code, comprehensive visualizations, and a concise report that meets your reproducibility criteria on a fresh Linux setup. Let’s arrange a quick chat to go over the cohort details and ensure I fully capture your expectations. Regards, Blaze Nicholas

@namn795

Hi, there I'd love to help you build your project. As a Staff Software Engineer with 15 years of experience across multiple industries, I've focused heavily on Statistical Analysis, R Programming Language, Bioinformatics, Data Analysis, Python, Data Science, Statistics and Data Visualization. I already check your job description more details. I'd like to discuss about your project and my previous experience more details via chat or meeting. Let's chat! Best regards, Nam X.

@therons4

✨Hello, Client!✨ I read your TCGA RNA-seq Survival Analysis brief and it’s a compelling challenge: linking gene-level and gene-set activity to overall and progression-free survival across multiple cancer types with a clean, reproducible workflow. I’m Theron, a data scientist specialized in omics, survival modeling, and reproducible research pipelines. I’ll deliver a tightly integrated workflow that pre-processes and normalises RNA-seq counts, accurately matches samples to clinical records, builds Kaplan-Meier curves, and fits multivariable Cox models adjusting for age, stage, subtype, and other covariates. Then I’ll run GSEA on ranked Cox statistics to reveal prognostic pathways, plus publication-ready plots and a concise methods/results report. What you’ll get: • Reproducible scripts/notebooks (bash, R, Python) plus a README with dependencies and environment setup. • All intermediate/final results: normalised matrices, survival stats, GSEA scores, and high-quality SVG/PNG plots. • A brief methods & results document in Markdown/Word/PDF. Deliverables will run end-to-end on a fresh Linux environment using only publicly available libraries or the standalone GSEA tool. I’ll ensure hazard ratios and enrichment scores reproduce within 5% using the supplied scripts. I’ll tailor the workflow to your cohort list and file structure and provide a concise interpretation guide to support publication-quality reporting. For reference, see related resources on robust RNA-seq analysis a

@borisdeveloper71

Hi, there. I’m confident that I can deliver this project to a high standard. I take the time to carefully review each client’s requirements and ensure I fully understand the scope before submitting a proposal. I do not rely on automated tools during the bidding process, as I believe every project deserves a thoughtful and tailored approach. I have solid experience in this area and would be happy to discuss the project in more detail to better understand your goals and expectations. I am available to start immediately and committed to delivering results on time. I have rich experience in Python, Statistics, R Programming Language, Statistical Analysis, Data Science, Data Visualization, Data Analysis, Bioinformatics and I will work until you are 100% satisfied with the final product. I look forward to the opportunity to work with you. Thank you for your time. Boris

@mariamz24

Hello , We would like to grab this opportunity and will work till you get 100% satisfied with our work. We are an expert team which have many years of experience on Python, Statistics, R Programming Language, Statistical Analysis, Data Science, Data Visualization, Data Analysis, Bioinformatics Please come over chat and discuss your requirement in a detailed way. Regards

@billybryan98

Hello, I hope you are doing well. I am a data scientist with deep experience in large-scale RNA-seq analysis, survival modelling, and reproducible bioinformatics workflows. I specialise in turning raw counts and clinical data into robust, interpretable insights, with clear documentation and shareable code. I have built end-to-end pipelines that normalise RNA-seq data, link samples to clinical records, and generate Kaplan-Meier plots and multivariable Cox models with covariate adjustment. I also implement gene-set enrichment analysis on ranked Cox statistics and deliver publication-ready plots, summaries, and a concise methods report. Everything is packaged in reproducible scripts (Python/R/Bash) and a reader-friendly README, ready to run on a fresh Linux environment with publicly available tools. I can handle this project using my experience with TCGA-like datasets, ensuring transparent reporting, reproducibility, and deliverables aligned with your acceptance criteria. Please feel free to contact me so we can discuss more details. I am looking forward to the chance of working together. Best regards, Billy Bryan

@KeyanRibberts

Hello, I am confident in my ability to provide a robust analytical workflow for your TCGA RNA-seq survival analysis. With extensive experience in bioinformatics and statistical analysis, I have successfully processed large-scale omics datasets while integrating GSEA for meaningful biological insights. I will pre-process and normalize the RNA-seq count data, ensuring each sample aligns perfectly with its clinical record. Using Kaplan-Meier curves and Cox models, I will identify genes significantly associated with survival outcomes, considering age, stage, and subtype. GSEA will then reveal key pathways affecting prognosis based on the derived gene lists. I will deliver reproducible scripts, all necessary result files, and a concise report summarizing our findings. Given your needs, we can initiate this project promptly upon your confirmation. Best regards, Keyan

@Ebendewater

New freelancer. Experienced developer. Fully committed to your success. I’m building my reputation, which means I care more about quality and satisfaction than the paycheck, giving you dedicated effort, clear communication, and excellent results at a competitive rate. With years of experience in data visualization and a strong command over Python, I'm well-equipped to handle high-volume omics data like your TCGA RNA-seq datasets. My proficiency extends to utilizing publicly available libraries, ensuring that the code executes seamlessly on a fresh Linux environment. Additionally, my repertoire includes utilizing the GSEA command-line tool, which will be especially beneficial for your project. I recognize that as important as extensive experience is, communication is equally vital. So through our collaboration, you can expect the provision of reproducible scripts or notebooks (bash, R, Python, or GSEA), along with a comprehensive README explaining all dependencies. I guarantee set of high-quality intermediates and final result files including normalised matrices, survival statistics, GSEA enrichment scores.