Data input requirement: please have these data and information ready

• During the first initial meeting, we are going to discuss :
  • the biological question(s) you want to answer
  • the experimental design
  • the platform you used to generate your data (e.g Affymetrix or Illumina, the chip model,...)
  • the quality controls and the input data format
• Your data should have been statistically analyzed (you should provide us with one file containing this information):
  • The data should have been normalized.
  • Some control quality plots should have been done:
    • Box-plot of intensity (before and after normalization)
    • Principal Component Analysis (PCA)
    • Hierarchical clustering of samples (performed on all the data)
    • Please provide a powerpoint presentation with a figure for each analysis
  • An appropriate statistical test testing your hypothesis (your biological question) should have been performed
    • for example : moderated t-test, paired t-test, ANOVA,...

  • If you need support for your statistical analyses, please contact Shaheena Bashir (Ph.D. in Statistics) at sbashir@uhnres.utoronto.ca. Located at MaRS TMDT 15th floor, she offers free consultation for statistical analyses for Cancer Stem Cell program. She will analyze your data and output the results in the right format for subsequent enrichment analyses. You are encouraged to contact Shaheena as soon as you plan your experiment: these genomics technologies are very sensitive to noise and a well designed experiment is very important for best results. It will ensure better quality data.

• You need to provide us with 1 file (.txt) for the enrichment analysis :
  • Name your file as follow: yourname_date.txt (example: veronique_March21.txt)
  • Please rename your file with a new date if you resubmit your file
  • Please follow the format description:
    • the first column corresponds to Entrez ID.
      • An Entrez ID is a numerical value that uniquely identifies genes.

      • For example the Entrez ID for Myc (myelocytomatosis oncogene [ Mus musculus ]) is 17869: http://www.ncbi.nlm.nih.gov/gene/17869.

    • the second column corresponds to a unique array identifier (ProbesetID for Affymetrix and sampleID for Illumina).
    • the third column corresponds to gene name (official gene symbol).
    • the fourth column corresponds to the gene description (full gene name).
    • the fifth and sixth columns contain the statistical values :
      • the statistical values are the ones that enable you to tell if a gene is significantly differentially expressed or not, it could be for example the t value and the p-value if you applied a t-test.

      • the whole table is ordered by the absolute value of the fifth column ( t value in this example) in a decreasing order.

    • the additional columns contain the transformed (log2 for example) and normalized (RMA or quantile normalization for example) values for each sample (= each chip if gene expression data).
  • Example:

• Note:
  • Each row of the table should correspond to a different gene. If several rows correspond to the same gene (same Entrez ID), there are 2 possibilities to remove the redundancy:
    • for a same gene, only the row corresponding to the best t-value is conserved
    • for a same gene, the average of the different normalized values is calculated before the t-test is applied
    • the choice has to be made before the statistical data are performed. We can discuss it during the initial meeting.

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Service SOP

• Consulting Meeting

  • Goal: to help plan an experiment before it is run. We can recommend case studies that user can learn from.

    • Genomics technologies are very sensitive: they can detect small amounts of variation. A good experimental design takes into account all possible variables (or factors) and ensure better quality data. You are encouraged to come to talk with us (and/or with the biostatistician Shaheena Bashir)about your planned experiment. Will your design enable to answer your questions? Are there variables you did not think of? Do you have enough replicates? What is your control?
    • These consulting meetings can also generate a follow up plan, where additional meetings can be scheduled during and after an experiment is run to answer questions and check that the experimental design is good: it is up to you to decide if you need it or not. We are always available to speak with you about your data.

• Initial Meeting (when a dataset is ready to be analyzed)

  • Goal: We will discuss your project, the biological question(s) you want to answer, the experimental design, the enrichment analysis, statistical data data input formats, and create a project name.

  • Once correct input data are received and the quality controls are good, we will issue an initial pathway analysis plan (see below).

  • time estimate: 30min to 1 hour

• Training session

  • Goal: You can book a training session if you wish to do your enrichment analysis on your own of if you want to explore the map once we have performed the analysis for you. We will explain you how to install Cytoscape and the different plugins (Enrichment Map, WordCloud and GeneMANIA) on your computer and how to play with your data.

  • time estimate: 30min to 1 hour

• Initial Pathway Analysis Plan

  • Goal: A pathway analysis plan is a document that state the different analyses that are going to be performed and a time estimate. We write the pathway analysis plan once correct input data are received. It needs to be sign off by researchers and P.I. We send it to you as a Google document.

  • A meeting can be scheduled if requested to explain the Pathway Analysis Plan.

• Run analysis, interpret the map and produce a report

  • Status : the analysis status will be visible on the website page; We will communicate with you very regularly during the process to ensure effective interpretation of results.

  • Analysis Report: A report including a global figure of the map and a detailed focus analysis of several pathways as examples will be written at the end of the analysis.

  • Analysis Report Meeting

    • Goal: discuss the analysis and report.
      • Examples of questions we can discuss: Do the results meet your expectations?
        • Is there anything unexpected in the results? If you had the resource, which experiments would you conduct based on the results of this analysis?
    • Time estimate: 30min to 1hour
    • Two options are available after this meeting:
      • We need to perform additional bioinformatics analyses : customized analyses
      • You are satisfied with the map and we let you play with the data and perform some validation experiments before a follow-up meeting

• Customized analyses

  • Meeting with Researcher to explain the results of the customized analyses

• Follow-up

  • Goal: you may have performed validation experiments or generated new research hypotheses based on your genomics study. You may need to go back and focus on a different aspect of your data. We can help you to re-analyse your data, provide with additional bioinformatics tools or help planned a next genomics experiment.

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