Creating Enter Recordsdata

Whenever you run an evaluation module, visualization module, or pipeline, GenePattern shows the parameters for the chosen modules. Usually, a number of of those parameters are enter information, which should have a specific format; for instance, you may want to produce a gct or res file. For extra details about a specific file format, choose it from the listing on the proper.

This part offers basic data that can assist you create correctly formatted information for GenePattern.

  • Remodeling Plain Textual content Recordsdata
  • Creating GCT/RES Recordsdata
  • Changing and Processing Recordsdata
  • Changing CDT to GCT Recordsdata

A number of video tutorials are additionally accessible:

  • Changing Expression Knowledge right into a GenePattern Enter File Format
  • Changing Between MAGE-TAB and GenePattern Expression File Codecs
  • Changing Illumina Expression Knowledge Into the GenePattern GCT format
  • Importing Knowledge from caArray to GenePattern
  • Exporting Datasets from InsilicoDB to GenePattern
  • Imputing Lacking Values in GenePattern

Further assets can be found from our partnered website GenomeSpace. Their Convert file codecs web page lists supported file conversions, descriptions of file varieties, and an electronic mail to request new file format converters. Verify their weblog for newly added converters.

Remodeling Plain Textual content Recordsdata

Though totally different GenePattern modules require totally different file codecs, the entire information are plain textual content information. Plain textual content differs from wealthy textual content as described in Wikipedia, and these can’t be interchanged.

Knowledge inside information are parsed in varied methods, e.g. tab-delimited, space-delimited, comma-separated, or one merchandise per line. Most gene expression information is tab-delimited and you’ll open the file in a spreadsheet or database program that means that you can export the information right into a plain textual content file. To transvert a plain textual content file to a selected format, e.g. GCT or CLS, the contents should conform to the attributes of the format as described and have the right file extension. Required attributes embrace a selected delimiter, e.g. tab, particular utilization for choose cells, e.g. headings and numeric tallies, and/or distinctive pattern and probe identifiers. File extensions appended to the tip of a file are acknowledged by GenePattern modules, begin with a interval, and are sometimes three letters, e.g. dataset1.gct or dataset1.cls.

A number of modules exist underneath Knowledge Format Conversion and Preprocess & Utilities that remodel plain textual content information to particular codecs, e.g. Read_group_trackingToGct, assist create supporting information from such expression information, e.g. ClsFileCreator, or course of information in desired methods, e.g. UniquifyLabels and SelectFileMatrix

Alternatively, you could manually remodel information to format-specific plain textual content information utilizing the next steps:

  1. Open the information file in a textual content editor or spreadsheet editor.
    • Take into account some spreadsheet applications convert gene symbols to dates, e.g. Excel converts MARCH1 to 1-Mar, in addition to introduce different irreversible auto-formatting conversions as described in Zeeberg, et al (2004). Keep away from these conversion by utilizing Excel’s Textual content Import Wizard, e.g. by way of File>Import. Within the third step of the Textual content Import Wizard, spotlight the column(s) and choose Textual content as the Column information format as a substitute of the default Basic.
    • Use Excel’s Textual content Import Wizard to transform house delimited information to spreadsheet format, i.e. choose Delimiters>Area, and to separate labels separated by a longbar (|) into two columns, i.e. choose Delimiters>Different: and sort | within the textbox.
  2. Make the required modifications that conform to file format attributes described on this web page and save as a plain textual content file.
    • Excel’s Save As>Tab delimited textual content and Comma Separated Values save as plain textual content with TXT and CSV extensions, respectively.
    • For a space-delimited file, open the CSV file with a textual content editor and discover and substitute every comma with an area.
    • Mac’s TextEdit defaults to wealthy textual content format. Change this for the present doc by choosing Format>Make Plain Textual content, and for future paperwork by setting TextEdit>Preferences>Format to Plain textual content.
  3. Change the file extension to the applicable extension, e.g. GCT or CLS. 
    • In Mac OS, to change the file extension, right-click on the file>Get Data, broaden Title & Extension part, uncheck Cover extension choice, then change the extension within the field supplied.

In flip, open these plain textual content format information utilizing (1) the Open with perform and choosing a textual content editor, (2) including a .txt extension to the tip and doubling-clicking, or (3) importing right into a spreadsheet program, e.g. Excel.

Creating GCT/RES Recordsdata

GenePattern offers a number of modules for creating GCT and/or RES information from gene expression information from varied sources. For details about these modules:

  1. Login to the general public GenePattern server: http://genepattern.broadinstitute.org/gp/.
    In case you don’t have a GenePattern account, you’ll be able to register on the login web page.
  2. Assessment the GCT and RES Recordsdata web page of the GenePattern protocols.

Changing and Processing Recordsdata

The Modules web page of the GenePattern website offers a whole listing of the modules and pipelines accessible from the Broad Institute. Modules within the Knowledge Format Conversion class convert information from one format to a different. Modules within the Preprocess & Utilities class present strategies for importing and dealing with information information.

Changing CDT to GCT Recordsdata

One frequent query from GenePattern customers is convert a cdt file to a gct file. Following is a quick tutorial that walks you thru this course of by changing pattern.cdt to pattern.imputed.gct:

  1. Save the pattern.cdt file to your native drive and open it in Microsoft Excel.
  2. Delete the CLID and GWEIGHT columns. The gct file format permits for under two columns of annotations.
  3. Delete the second row, which accommodates array identifiers (AID). The gct file format permits for just one row of identifiers.
  4. Add two header rows on the prime of the file:
    • Within the first row, first cell, enter: #1.2
    • Within the second row, first cell, enter the variety of information rows: 1553
    • Within the second row, second cell, enter the variety of information columns: 44

  5. Save the modified file as a textual content (tab delimited) file with the identify pattern.gct. 
  6. Confirm that your new .gct file matches the necessities of a gct file in GenePattern.
  7. Your unique cdt file contained cells that have been lacking information. Most GenePattern modules require that every one cells in a gct file include information. Use the GenePattern evaluation module ImputeMissingValues.KNN so as to add the lacking information to your gct file. The module will take pattern.gct because the enter file, impute the lacking information, and generate a pattern.imputed.gct file.

ATR

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

ATR information shall be created by the HierarchicalClustering module. It is a format outlined at Stanford for his or her Hierarchical Clustering program.  Word that the HierarchicalClustering module may also generate a CDT file.

The ATR (array tree) file information the order wherein the arrays (columns) have been joined throughout clustering.

CBS

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

CBS information are created by the CBS module. A CBS (circular binary segmentation) file is a tab-delimited textual content file with six columns: the pattern id, the chromosome quantity, the map place of the beginning of the section, the map place of the tip of the section, the variety of markers within the section, and the typical worth within the section. The primary row accommodates column headers and the second row onwards accommodates information values.

Pattern CBS file: mynah.sorted.cbs.txt

CDT

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

CDT information are created by the HierarchicalClustering module. It is a format outlined at Stanford for his or her Hierarchical Clustering program. The CDT (clustered data tin a position) file accommodates the unique information, however reordered, to replicate the clustering. An extra column and/or row is added if clustering is carried out on genes and/or arrays. The extra column/row accommodates a novel identifier for every row/column that’s linked to the outline of the tree construction within the GTR/ATR file additionally generated by the module:

  • if you happen to clustered by genes, a GTR file (gene tree)
  • if you happen to clustered by samples, an ATR file (array tree)

These tree information replicate the historical past of how the cluster was constructed, and can be utilized to reconstruct the tree(s).

CEL

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

Affymetrix picture evaluation software program generates CEL information, which retailer details about the probes on a chip and the depth values for the probes. GenePattern modules, corresponding to ExpressionFileCreator and SNPFileCreator, take CEL information as enter and generate information information that may be learn by subsequent GenePattern modules. Extra data on this format is accessible right here.

CHIP

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

The CHIP file format accommodates annotation a few microarray (used with GSEA module). It lists the options (i.e probe units) used within the microarray together with their mapping to gene symbols (when accessible). Whereas this file is just not used instantly within the GSEA algorithm, it’s used to annotate the output outcomes and might also be used to break down every probe set within the expression dataset to a single gene vector.

Chip annotation information may be laid out in a tab-delimited file format (*.chip) or in a comma-separated file format (*.csv). The codecs are equivalent aside from the separation character (tab or comma). Usually, you employ the tab-delimited (*.chip) file format.

The CHIP file format is organized as follows:

  1. The primary line accommodates column headings that determine the content material of every column within the the rest of the file. The file should include three column headings:
    • Probe Set ID
    • Gene_Symbol
    • Gene_Title

    These three columns can seem in any order. The file might include further columns, which shall be ignored.

    • For instance:
      Probe Set ID Gene_Symbol Gene_Title
  2. The remainder of the information file accommodates information for every probe set ID used within the microarray.
    • Line format:
      (probe set id) (tab) (gene image) (tab) (gene title)
    • For instance:
      205699_at MAP2K6 mitogen-activated protein kinase kinase 6

Pattern CHIP file: HG_U133A_annot.chip

CLM

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

The CLM file format is a tab-delimited file format that describes the samples in a zipped assortment of CEL or IDAT information (used with the ExpressionFileCreator and IlluminaExpressionFileCreator modules, respectively).

Every row of the CLM format describes a CEL file within the zip file:

  • Line format:
    (CEL file identify) (tab) (pattern identify) (tab) (class)
  • For instance:
    cat_a.CEL sample_cat_a tumor
  1. The primary column accommodates the CEL file identify (file extension is optionally available).
  2. The second column accommodates a pattern identify for the CEL file information.
  3. The third column accommodates a phenotype class identify for the CEL file information.

Pattern CLM file: pattern.clm

CLS

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

The CLS file format defines phenotype (class or template) labels and associates every pattern within the expression information with a label. It makes use of areas or tabs to separate the fields. The CLS file format differs considerably relying on whether or not you might be defining categorical or steady phenotypes:

  • Categorical labels outline discrete phenotypes, e.g. regular versus tumor.
  • Steady phenotypes are used for time collection experiments or to outline the profile of a gene of curiosity, e.g. gene neighbors.

Word: Most GenePattern modules are meant to be used with categorical phenotypes. Subsequently, except the module documentation explicitly states in any other case, a CLS file ought to outline categorical labels. GenePattern’s ClsFileCreator module offers a guided wizard to create a CLS file from .gct or .res information.

Categorical labels

Categorical labels outline discrete phenotypes (for instance, regular vs tumor). For categorical labels, the CLS file format is organized as follows:

  1. The primary line of a CLS file accommodates numbers indicating the variety of samples and variety of lessons. The variety of samples ought to correspond to the variety of samples within the related RES or GCT information file.
    • Line format:
      (variety of samples) (house) (variety of lessons) (house) 1
    • For instance:
      58 2 1
  2. The second line in a CLS file accommodates names for the category numbers. The road ought to start with a pound signal (#) adopted by an area.
    • Line format:
      # (house) (class 0 identify) (house) (class 1 identify)
    • For instance:
      # cured deadly/ref
  3. The third line accommodates a category label for every pattern. The category labels are sequential numbers starting with zero. The primary label used (0) is assigned to the primary class named on the second line; the second distinctive label (1) is assigned to the second class named; and so forth. (NOTE: Whereas most GenePattern modules adhere to this rule of 0 as the primary class, some modules (corresponding to GSEA) don’t. Verify the documentation for the module you might be utilizing in case you are not sure.)
    The variety of class labels specified on this line must be the identical because the variety of samples specified within the first line. The variety of distinctive class labels specified on this line must be the identical because the variety of lessons specified within the first line.

    • Line format:
      (pattern 1 class) (house) (pattern 2 class) (house) … (pattern N class)
    • For instance:
      0 0 … 1

CLS file for pattern RES file (categorical labels): all_aml_test.cls

Steady labels

Steady phenotypes are used for time collection experiments or to outline the profile of a gene of curiosity (gene neighbors). A CLS file that defines steady labels can include a number of labels. The next instance reveals a CLS file that defines two steady labels:

#numeric
#AFFX-BioB-5_st
206.0 31.0 252.0 -20.0 -169.0 -66.0 230.0 -23.0 67.0 173.0 -55.0 -20.0 469.0 -201.0 -117.0 
-162.0 -5.0 -86.0 350.0 74.0 -215.0 193.0 506.0 183.0 350.0 113.0 -17.0 29.0 247.0 -131.0 
358.0 561.0 24.0 524.0 167.0 -56.0 176.0 320.0
#AFFX-BioDn-5
75.0 142.0 32.0 109.0 -38.0 -80.0 62.0 39.0 196.0 -42.0 199.0 49.0 171.0 327.0 115.0 
-71.0 85.0 80.0 270.0 182.0 208.0 -94.0 292.0 233.0 34.0 0.0 59.0 233.0 48.0 466.0 -7.0 
-96.0 297.0 38.0 208.0 -15.0 30.0 357.0
  1. The primary line accommodates the textual content “#numeric” which signifies that the file defines steady labels.
  2. The rest of the file defines the continual phenotypes. For every phenotype:
    1. The primary line defines the identify of the phenotype; for instance, #AFFX-BIOB-5_st.
    2. The second line accommodates a price for every pattern within the .gct file. Usually, your phrase processor wraps the second line of the phenotype definition, as proven within the instance.

For a steady phenotype label, the values for the samples outline the phenotype profile. The relative change within the values defines the relative distance between factors within the phenotype profile. Within the instance proven above, the phenotype profile is the expression profile for a gene: the pattern values for the 2 phenotype labels are gene expression values. For a time collection experiment, you’d select pattern values that outline the specified expression profile. The instance proven beneath assumes that you’ve 5 samples taken at 30 minute intervals. The primary phenotype label defines a phenotype profile that reveals steadily rising gene expression; the second defines a profile that reveals an preliminary peak after which gradual lower:

#numeric
#IncreasingProfle
30 60 90 120 150
#PeakProfle
5 20 15 10 5

CN

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

It is a tab-delimited file format that accommodates SNP copy numbers. It accommodates one row for every SNP and one column for every SNP array: the uncooked copy quantity worth. It’s organized as follows:

  1. The primary line accommodates a listing of labels figuring out the SNP arrays.
    • Line format:
      SNP (tab) Chromosome (tab) PhysicalPosition (tab) (array_1_name) (tab) … (array_N_name)
    • For instance:
      SNP (tab) Chromosome (tab) PhysicalPosition (tab) MYNAH_p_Affy_plate_9_Mapping250K_Sty_A01_49068 (tab) … MYNAH_p_Affy_plate_9_Mapping250K_Sty_A01_49084
  2. The remainder of the SNP file accommodates one row of knowledge for every SNP.
    • Line format:
      (snp) (tab) (chromosome) (tab) (place) (tab) (array_1_cn) (tab) … (array_N_cn)
    • For instance:
      SNP_A-4249904 (tab) 17 (tab) 41420045 (tab) 2.265 (tab) … 1.735

Word: Type the SNPs by chromosome and bodily place (low to excessive). Most GenePattern modules, in addition to many exterior instruments, require sorted information.

Pattern CN file: mynah.sorted.cn

FCS

Stream Cytometry Normal (FCS) information are utilized by GenePattern’s quite a few move cytometry information evaluation and information processing modules. The Worldwide Society for Development of Cytometry (ISAC) offers detailed assets outlining move cytometry information file format requirements, together with for up to date FCS codecs, in addition to instance information transformations.

Verify module documentation to see which variations of FCS information the module accepts. For instance, the FLAME suite modules settle for FCS v2.0, FCS v3.0, and TXT information. 

Along with evaluation modules, GenePattern affords a variety of modules that permit for FCS information manipulation that embrace CsvToFcs, FcsToCsv, ExtractFCSParameters, FCSNormalization, CompensateFCS, and PreviewFCS.

FPKM_tracking and Read_group_tracking 

FPKM_tracking, Count_tracking, and Read_group_tracking information are output by the Cufflinks suite modules, which embrace Cufflinks and Cuffdiff. For extra data on every of those and different Cufflinks suite file varieties, see the Cufflinks web site. You possibly can convert these to GCT format utilizing Fpkm_trackingToGct or Read_group_trackingToGct modules.

FPKM_tracking information include RNA-seq estimated expression values in Fragments Per Kilobase of transcript per Million mapped reads (FPKM) and use a generic format to output estimated expression values. Every FPKM monitoring file has the next format:

Column quantity Column identify Instance Description
1 tracking_id TCONS_00000001 A singular identifier describing the item (gene, transcript, CDS, major transcript)
2 class_code = The class_code attribute for the item, or “-” if not a transcript, or if class_code is not current
3 nearest_ref_id NM_008866.1 The reference transcript to which the category code refers, if any
4 gene_id NM_008866 The gene_id(s) related to the item
5 gene_short_name Lypla1 The gene_short_name(s) related to the item
6 tss_id TSS1 The tss_id related to the item, or “-” if not a transcript/major transcript, or if tss_id is not current
7 locus chr1:4797771-4835363 Genomic coordinates for straightforward looking to the item
8 size 2447 The variety of base pairs within the transcript, or ‘-‘ if not a transcript/major transcript
9 protection 43.4279 Estimate for absolutely the depth of learn protection throughout the item
10 q0_FPKM 8.01089 FPKM of the item in pattern 0
11 q0_FPKM_lo 7.03583 the decrease certain of the 95% confidence interval on the FPKM of the item in pattern 0
12 q0_FPKM_hi 8.98595 the higher certain of the 95% confidence interval on the FPKM of the item in pattern 0
13 q0_status OK Quantification standing for the item in pattern 0. Might be considered one of OK (deconvolution profitable), LOWDATA (too advanced or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or different numerical exception prevents deconvolution.
14 q1_FPKM 8.55155 FPKM of the item in pattern 1
15 q1_FPKM_lo 7.77692 the decrease certain of the 95% confidence interval on the FPKM of the item in pattern 0
16 q1_FPKM_hi 9.32617 the higher certain of the 95% confidence interval on the FPKM of the item in pattern 1
17 q1_status 9.32617 the higher certain of the 95% confidence interval on the FPKM of the item in pattern 1. Might be considered one of OK (deconvolution profitable), LOWDATA (too advanced or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or different numerical exception prevents deconvolution.
3N + 12 qN_FPKM 7.34115 FPKM of the item in pattern N
3N + 13 qN_FPKM_lo 6.33394 the decrease certain of the 95% confidence interval on the FPKM of the item in pattern N
3N + 14 qN_FPKM_hi 8.34836 the higher certain of the 95% confidence interval on the FPKM of the item in pattern N
3N + 15 qN_status OK Quantification standing for the item in pattern N. Might be considered one of OK (deconvolution profitable), LOWDATA (too advanced or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or different numerical exception prevents deconvolution.

Cuffdiff calculates the expression and fragment rely for every transcript, major transcript, and gene in every replicate previous to differential expression calculations. The outcomes are output in learn group monitoring information on the degree of genes, isoforms, transcription begin websites, and coding sequencesEvery Read_group_tracking file has the next format:

Column quantity Column identify Instance Description
1 tracking_id TCONS_00000001 A singular identifier describing the item (gene, transcript, CDS, major transcript)
2 situation Fibroblasts Title of the situation
3 replicate 1 Title of the replicate of the situation
4 raw_frags 170.21 The estimate variety of (unscaled) fragments originating from the item on this replicate
5 internal_scaled_frags 4905.63 Estimated variety of fragments originating from the item, after remodeling to the inner frequent rely scale (for comparability between replicates of this situation.)
6 external_scaled_frags 99.21 Estimated variety of fragments originating from the item, after remodeling to the exterior frequent rely scale (for comparability between situations)
7 FPKM 201.334 FPKM of this object on this replicate
8 effective_length 5988.24 The efficient size of the item on this replicate. Presently a reserved, unreported subject.
9 standing OK Quantification standing for the item. Might be considered one of OK (deconvolution profitable), LOWDATA (too advanced or shallowly sequenced), HIDATA (too many fragments in locus), or FAIL, when an ill-conditioned covariance matrix or different numerical exception prevents deconvolution.

GCT

Word: In case you are utilizing Excel to edit GenePattern information, be sure you save the file as a tab-delimited textual content file and provide the right file extension. You possibly can specify the file identify in quotes to forestall Excel from appending .txt to the file identify. Additionally, observe that Excel’s auto-formatting can introduce errors in gene names, as described in Zeeberg, et al (2004).

The GCT file format is a tab delimited file format that describes an expression dataset. The primary variations between RES and GCT file codecs are the RES file format (1) accommodates labels for every gene’s absent (A) versus current (P) calls as generated by Affymetrix’s GeneChip software program and (2) doesn’t permit lacking expression values. Though the GCT file format permits lacking values, only some modules (corresponding to CART, GSEA and HierarchicalClustering) may be run towards an expression dataset that’s lacking values. Most modules don’t permit lacking expression values.

The GCT file is organized as follows:

  1. The primary line accommodates the model string and is all the time the identical for this file format. Subsequently, the primary line have to be as follows:
    • #1.2
  2. The second line accommodates numbers indicating the scale of the information desk that’s contained within the the rest of the file. Word that the identify and outline columns aren’t included within the variety of information columns.
    • Line format:
      (# of knowledge rows) (tab) (# of knowledge columns)
    • For instance:
      7129 58
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