giftmama.blogg.se - Cmap matlab

Cmap matlab code#

Im1 = grid.imshow(data, cmap='jet', interpolation='nearest') Im0 = grid.imshow(data, cmap='gray', interpolation='nearest') However, if img were an array of shape (M,N), then the cmap controls the colormap used to display the values. Per the help(plt.imshow) docstring:Ĭmap : ~, optional, default: None When img1 has shape (M,N,3) or (M,N,4), the values in img1 are interpreted as RGB or RGBA values. Then what does the second argument 'gray' do? Can someone explain all this to me? Any help appreciated. What does plt.imshow(img1, 'gray') do? I tried searching Google and all I could understand was that the 'gray' argument was a Color map. Plt.subplot(236),plt.imshow(constant,'gray'),plt.title('CONSTANT') Plt.subplot(233),plt.imshow(reflect,'gray'),plt.title('REFLECT') Plt.subplot(232),plt.imshow(replicate,'gray'),plt.title('REPLICATE')

Cmap matlab code#

Statistically significant enrichment at either end of the ranking.I'm trying to learn opencv using python and came across this code below: import cv2 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. Colormaps are often split into several categories based on their function (see, e.g., ): Sequential: change in lightness and often saturation of color incrementally, often using a single hue should be used for representing information that has ordering. 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.