Skip to content

Latest commit

 

History

History
118 lines (89 loc) · 4.48 KB

FAQ.md

File metadata and controls

118 lines (89 loc) · 4.48 KB
Raw

FAQ

How can a gene have zero counts for all samples but has a non-zero fold-change?

This can happen when using the backend of Degust. Degust uses voom (or edgeR) to perform the expression analysis. Voom adds a small constant (0.5), to each count, normalizes for library size, then takes the log. This means when you have a count of 0 across all samples, but different library sizes, it is possible to compute a non-zero fold-change.

We recommend setting Min read count on the configuration page to a small value, say 10.

What is the Min read count setting?

This is the minimum number of reads required in at least one sample to keep the gene in the analysis. That is, a given gene is omitted if the number of reads across all samples is below this setting.

I don't see an option to display an MDS plot

The MDS plot is only available when you have included "count" columns

How is the MDS plot calculated

  • First genes that don't pass the "FDR cut-off" or "abs log FC" filters are ignored. Using these filters is "cheating" when doing an MDS plot to look at replicate clustering
  • The remaining genes have the counts for each replicate log-transformed.
  • The genes are then ranked by decreasing variance. That is, the most variable genes are "at the top"
  • Then the top "Skip genes" are ignored.
  • And the next "Num genes" are selected.
  • These selected genes are used to compute an MDS (or PCA) plot

Uploading a counts file from the command line

You will need to be logged in to degust for this functionality. You will also need to create an upload token. Browse to the user page : http://degust.erc.monash.edu/users/me and create a token, then copy this token.

Then use curl to upload your counts file with the token from above:

curl 'http://degust.erc.monash.edu/upload' -F 'upload_token=35758967af4c201aabfd7231a71fad6d' -F 'filename=@/home/dave/degust-counts.tsv'

This will return a link that you can use to browse the new file.

Note this can used to upload AND configure a degust session at the same time. See the next section for the contents of the settings.json file.

curl 'http://degust.erc.monash.edu/upload' -F 'upload_token=35758967af4c201aabfd7231a71fad6d' -F 'filename=@/home/dave/degust-counts.tsv' -F 'settings=<settings.json'

Configuring a Degust session from the command line

First upload your count matrix, and note the special code created in the url. eg. 73fb85e4625f5bdd08cfeb3b9fc7a7f2

You may download the settings in a json format. Note here the use of the jq tool, this isn't necessary but it does make for nicer formatting of the json.

curl 'http://degust.erc.monash.edu/degust/73fb85e4625f5bdd08cfeb3b9fc7a7f2/settings' | jq '.settings' > foo.json

You can now edit the file foo.json to change any settings. To save you changes you POST it back to the server:

curl 'http://degust.erc.monash.edu/degust/73fb85e4625f5bdd08cfeb3b9fc7a7f2/settings' -F 'settings=<foo.json'

Example json settings below. Important parts:

  • csv_format specifies the format of your uploaded file, true for csv, or false for tsv
  • info_columns are the names of columns from your csv file
  • name the name of the degust session
  • replicates An array of conditions. Each condition is an array with the first entry as your chosen name for a condition, and the second entry is an array of the column names of the replicates from your file.
  • init_select the condition names from the replicates array that specify the conditions to select on page log
  • hidden_factor the condition names from the replicates array that specifies linear model terms for batch effects
  • analyze_server_side use true if you upload counts, false if you upload analysis results for visualisation in degust
{
  "min_cpm_samples": 5,
  "min_cpm": 1,
  "hidden_factor": [],
  "init_select": [
    "cdhR",
    "GppX",
    "luxS",
    "wt"
  ],
  "name": "Public Example",
  "analyze_server_side": true,
  "info_columns": [
    "Feature",
    "gene",
    "product"
  ],
  "replicates": [
    [
      "cdhR",
      [
        "cdhR-rep1",
        "cdhR-rep2"
      ]
    ],
    [
      "GppX",
      [
        "GppX-rep1",
        "GppX-rep2"
      ]
    ],
    [
      "luxS",
      [
        "luxS-rep1",
        "luxS-rep2",
        "luxS-rep3"
      ]
    ],
    [
      "wt",
      [
        "wt-rep1",
        "wt-rep2",
        "wt-rep3"
      ]
    ]
  ],
  "csv_format": true,
}