The colormap is particularly appropriate for displaying periodic functions. The colors begin with red, pass through yellow, green, cyan, blue, magenta, and return to red. hsv varies the hue component of the hue-saturation-value color model.hot varies smoothly from black, through shades of red, orange, and yellow, to white.gray returns a linear grayscale colormap.This colormap completely changes color with each index increment. flag consists of the colors red, white, blue, and black.copper varies smoothly from black to bright copper.cool consists of colors that are shades of cyan and magenta.colorcube contains as many regularly spaced colors in RGB colorspace as possible, while attempting to provide more steps of gray, pure red, pure green, and pure blue.This colormap is useful for adding an "electronic" look to grayscale images. bone is a grayscale colormap with a higher value for the blue component. autumn varies smoothly from red, through orange, to yellow.If you do not specify a size, MATLAB creates a colormap the same size as the current colormap. For example,Ĭreates an hsv colormap with 128 colors. Each M-file accepts the colormap size as an argument. M-files in the color directory generate a number of colormaps. Sets the current colormap to the default colormap. If any values in map are outside the interval, MATLAB returns the error: Colormap must have values in. The k th row of the colormap defines the k-th color, where map(k,:) = ) specifies the intensity of red, green, and blue. Each row is an RGB vector that defines one color. Statistically significant enrichment at either end of the ranking.Colormap (MATLAB Functions) MATLAB Function ReferenceĪ colormap is an m-by-3 matrix of real numbers between 0.0 and 1.0. It determines whether a priori defined sets show The GSEA Preranked tool computes set-based enrichment analysis against a user-defined Testing enrichment of user-defined sets using the GSEA Preranked tool ¶ fdr_qvalue.gctx : Estimated false discovery rate q-values [signatures xĢ.ncs.gctx : Normalized connectivity score matrix.cs.gctx : Raw connectivity scores matrix.up.gmt, dn.gmt: query genesets in GMT format.Matrices/query : Query parameters and result matrices in GCTx format for all The null signatures (specified by the is_null_sig field in the signature fdr_q_nlog10 : Negative log10 transformed FDR q-values estimated relative to.Normalized using the global means across all signatures. Is_ncs_sig field in the signature metadata file) If the ncs_group field is notĮmpty the scores are normalized within each group, otherwise the scores are norm_cs : Normalized connectivity score computed by dividing the rawĬonnectivity scores by the signed-mean scores of signatures (specified by the.Theįollowing fields are computed by the query tool: query_result.gct : a GCT format text file listing the annotations,Ĭonnectivity scores and q-values for each signature in the dataset. Outputs: the tool produces the following output (in the results folder)Īrfs/: Per-query analysis report files (ARFs) FDR q-values are estimated by comparing theĭistributions of treatments to null signatures in the dataset.ĭATASET_PATH = fullfile ( cmapmpath, 'demo-datasets' ) % Queries UP_GENESET = fullfile ( DATASET_PATH, 'queries/genesets/dexamethasone_resistance_up.gmt' ) DOWN_GENESET = fullfile ( DATASET_PATH, '/queries/genesets/dexamethasone_resistance_down.gmt' ) % Gene Expression Dataset % Differential expression score matrix SCORE_FILE = fullfile ( DATASET_PATH, '/l1000/m2.subset.10k/level5_modz.bing_n10000x10174.gctx' ) % Corresponding rank matrix RANK_FILE = fullfile ( DATASET_PATH, 'l1000/m2.subset.10k/rank.bing_n10000x10174.gctx' ) % Signature annotations SIG_META_FILE = fullfile ( DATASET_PATH, 'l1000/m2.subset.10k/siginfo.txt' ) % results folder OUT_PATH = 'results/queryl1k' % Run the queryl1k tool sig_queryl1k_tool ( 'up', UP_GENESET. The raw scores are then scaled (normalized) by the signed-means to allow forįinally the statistical significance of the connections adjusted for multiple While query methodology isĪgnostic to the specific similarity metric, the default choice is a non-parametric, two-tailed weighted gene-set enrichment score (Subramanian, A. First raw similarity (connectivity) scoresīetween a query and CMap signatures are computed. (Note that while the tool is optimized for datasets generated by the L1000 platform, Queries) and a small subset of L1000 perturbational gene-expression signatures. The QueryL1k tool computes a set-based enrichment similarity between input genesets (aka Running a Cmap Query against an L1000 dataset using the QueryL1k tool ¶ Connectivity analysis using SigTools ¶ 1.
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