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"offsetStart": 30, "offsetEnd": 33}, "context": "For example, we observed that RLR performs well for all metrics in all DNAm datasets.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9998019337654114}, "created": {"value": false, "score": 7.545948028564453e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999912977218628}, "created": {"value": false, "score": 0.0013218522071838379}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "InstaPrism", "normalizedForm": "InstaPrism", "offsetStart": 32, "offsetEnd": 42}, "context": "As an illustration, we compared InstaPrism and SVR to deconvolve PaCL2.", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999008178710938}, "created": {"value": false, "score": 0.00021213293075561523}, "shared": {"value": false, "score": 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"software-type": "software", "software-name": {"rawForm": "RLR", "normalizedForm": "RLR", "offsetStart": 34, "offsetEnd": 37}, "context": "Among supervised methods however, RLR remains the best tool (Fig.   , D left panels).", "mentionContextAttributes": {"used": {"value": false, "score": 0.00011646747589111328}, "created": {"value": false, "score": 5.257129669189453e-05}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999912977218628}, "created": {"value": false, "score": 0.0013218522071838379}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "script", "normalizedForm": "script", "offsetStart": 35, "offsetEnd": 46}, "context": "We compared the original CIBERSORT script      with its EpiDISH implementation and found substantial differences (Additional file 1: Fig.    ).", "mentionContextAttributes": {"used": 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efficient to estimate the proportions of rare immune cell types compared to common ones except for RLR and CIBERSORT in the LuCL dataset (Fig. 6 upper panel).", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999744892120361}, "created": {"value": false, "score": 1.0728836059570312e-05}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999912977218628}, "created": {"value": false, "score": 0.0013218522071838379}, "shared": {"value": false, "score": 3.5762786865234375e-07}}}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "CIBERSORT", "normalizedForm": "CIBERSORT", "offsetStart": 187, "offsetEnd": 196}, "context": "Starting with methylation deconvolution, we observed that all methods were less efficient to estimate the proportions of rare immune cell types compared to common ones except for RLR and CIBERSORT in the LuCL dataset (Fig. 6 upper panel).", "mentionContextAttributes": {"used": {"value": true, "score": 0.9999744892120361}, "created": {"value": false, "score": 1.0728836059570312e-05}, "shared": {"value": false, "score": 1.1920928955078125e-07}}, "documentContextAttributes": {"used": {"value": true, "score": 0.9999744892120361}, "created": {"value": false, "score": 0.0013218522071838379}, "shared": {"value": false, "score": 2.384185791015625e-07}}, "references": [{"label": "[32]", "normalizedForm": "[32]", "refKey": 32}]}, {"type": "software", "software-type": "software", "software-name": {"rawForm": "debCAM", "normalizedForm": "debCAM", "offsetStart": 199, "offsetEnd": 205}, "context": "On the other hand, methods for which the detection of rare cell types is strongly degraded (defined by a difference in mean correlation above 0.4) include NNLS, InstaPrism, epi-dishCP, ICA, NMF, and debCAM.", "mentionContextAttributes": {"used": {"value": false, "score": 0.0003590583801269531}, "created": {"value": false, "score": 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