diff --git a/README.md b/README.md index 2d77df3..0c11f9f 100644 --- a/README.md +++ b/README.md @@ -77,58 +77,33 @@ higher effectSize or with a pruned dataset (see `preDA`) summary(test) - ## Method AUC FPR FDR Spike.detect.rate Score - ## MgSeq Feature (msf) 1.000 0.000 0.000 1.0 1.000 - ## RAIDA (rai) 1.000 0.000 0.000 1.0 1.000 - ## LIMMA voom (vli) 1.000 0.035 0.031 1.0 0.969 - ## DESeq2 (ds2x) 1.000 0.035 0.062 1.0 0.938 - ## DESeq2 man. geoMeans (ds2) 1.000 0.035 0.062 1.0 0.938 - ## EdgeR exact - TMM (ere) 1.000 0.043 0.062 1.0 0.938 - ## EdgeR exact - RLE (ere2) 1.000 0.049 0.118 1.0 0.882 - ## EdgeR qll - TMM (erq) 1.000 0.049 0.118 1.0 0.882 - ## SAMseq (sam) 1.000 NA 0.118 1.0 0.882 - ## EdgeR qll - RLE (erq2) 1.000 0.054 0.167 1.0 0.833 - ## MgSeq ZIG (zig) 0.980 0.127 0.434 0.9 0.457 - ## ALDEx2 wilcox (adx) 1.000 0.097 0.545 1.0 0.455 - ## ALDEx2 t-test (adx) 1.000 0.124 0.583 1.0 0.417 - ## Log t-test (ltt) 1.000 0.670 0.901 1.0 0.099 - ## Log LIMMA (lli) 1.000 0.689 0.906 1.0 0.094 - ## Log t-test2 (ltt2) 1.000 0.968 0.924 1.0 0.076 - ## Quasi-Poisson GLM (qpo) 1.000 0.970 0.924 1.0 0.076 - ## Log LIMMA 2 (lli2) 1.000 0.989 0.925 1.0 0.075 - ## Negbinom GLM (neb) 1.000 0.989 0.925 1.0 0.075 - ## Poisson GLM (poi) 1.000 1.000 0.925 1.0 0.075 - ## t-test (ttt) 0.986 0.968 0.924 1.0 0.075 - ## Wilcox (wil) 0.884 0.957 0.924 1.0 0.067 - ## Permutation (per) 0.595 0.962 0.924 1.0 0.045 - ## ZI-NegBin GLM (znb) 0.500 0.000 0.000 0.0 0.000 - ## ZI-Poisson GLM (zpo) 0.500 0.000 0.000 0.0 0.000 - ## Score.5% Score.95% - ## 1.000 1.000 - ## 0.577 1.000 - ## 0.536 1.000 - ## 0.556 1.000 - ## 0.556 1.000 - ## 0.536 1.000 - ## 0.536 1.000 - ## 0.536 1.000 - ## 0.500 0.938 - ## 0.500 1.000 - ## 0.000 0.652 - ## 0.214 0.556 - ## 0.183 0.455 - ## 0.081 0.109 - ## 0.081 0.101 - ## 0.075 0.079 - ## 0.073 0.079 - ## 0.075 0.079 - ## 0.075 0.079 - ## 0.075 0.076 - ## 0.069 0.078 - ## 0.065 0.071 - ## 0.042 0.047 - ## 0.000 0.000 - ## 0.000 0.000 + ## Method AUC FPR FDR Spike.detect.rate Score Score.5% Score.95% + ## MgSeq Feature (msf) 1.000 0.000 0.000 1.0 1.000 1.000 1.000 + ## RAIDA (rai) 1.000 0.000 0.000 1.0 1.000 0.577 1.000 + ## LIMMA voom (vli) 1.000 0.035 0.031 1.0 0.969 0.536 1.000 + ## DESeq2 (ds2x) 1.000 0.035 0.062 1.0 0.938 0.556 1.000 + ## DESeq2 man. geoMeans (ds2) 1.000 0.035 0.062 1.0 0.938 0.556 1.000 + ## EdgeR exact - TMM (ere) 1.000 0.043 0.062 1.0 0.938 0.536 1.000 + ## EdgeR exact - RLE (ere2) 1.000 0.049 0.118 1.0 0.882 0.536 1.000 + ## EdgeR qll - TMM (erq) 1.000 0.049 0.118 1.0 0.882 0.536 1.000 + ## SAMseq (sam) 1.000 NA 0.118 1.0 0.882 0.500 0.938 + ## EdgeR qll - RLE (erq2) 1.000 0.054 0.167 1.0 0.833 0.500 1.000 + ## MgSeq ZIG (zig) 0.980 0.127 0.434 0.9 0.457 0.000 0.652 + ## ALDEx2 wilcox (adx) 1.000 0.097 0.545 1.0 0.455 0.214 0.556 + ## ALDEx2 t-test (adx) 1.000 0.124 0.583 1.0 0.417 0.183 0.455 + ## Log t-test (ltt) 1.000 0.670 0.901 1.0 0.099 0.081 0.109 + ## Log LIMMA (lli) 1.000 0.689 0.906 1.0 0.094 0.081 0.101 + ## Log t-test2 (ltt2) 1.000 0.968 0.924 1.0 0.076 0.075 0.079 + ## Quasi-Poisson GLM (qpo) 1.000 0.970 0.924 1.0 0.076 0.073 0.079 + ## Log LIMMA 2 (lli2) 1.000 0.989 0.925 1.0 0.075 0.075 0.079 + ## Negbinom GLM (neb) 1.000 0.989 0.925 1.0 0.075 0.075 0.079 + ## Poisson GLM (poi) 1.000 1.000 0.925 1.0 0.075 0.075 0.076 + ## t-test (ttt) 0.986 0.968 0.924 1.0 0.075 0.069 0.078 + ## Wilcox (wil) 0.884 0.957 0.924 1.0 0.067 0.065 0.071 + ## Permutation (per) 0.595 0.962 0.924 1.0 0.045 0.042 0.047 + ## ZI-NegBin GLM (znb) 0.500 0.000 0.000 0.0 0.000 0.000 0.000 + ## ZI-Poisson GLM (zpo) 0.500 0.000 0.000 0.0 0.000 0.000 0.000 + ## # MetagenomeSeq Featue model appears to be the best res1 <- DA.msf(df, predictor = vec) @@ -152,14 +127,11 @@ higher effectSize or with a pruned dataset (see `preDA`) **Things to consider:** -[Do you have a paired or blocked experimental -design](#if-you-have-a-paired-or-blocked-experimental-design) [Do you -have covariates?](#if-you-have-covariates) [Does your predictor have -more than two -classes?](#if-your-predictor-is-categorical-with-more-than-two-levels) -[Is your data normalized externally or is it absolute -abundances?](#if-data-is-normalized-externally-or-represent-absolute-abundances) -[Do you have a Phyloseq object?](#if-you-have-a-phyloseq-object) +- [Do you have a paired or blocked experimental design](#if-you-have-a-paired-or-blocked-experimental-design) +- [Do you have covariates?](#if-you-have-covariates) +- [Does your predictor have more than two classes?](#if-your-predictor-is-categorical-with-more-than-two-levels) +- [Is your data normalized externally or is it absolute abundances?](#if-data-is-normalized-externally-or-represent-absolute-abundances) +- [Do you have a Phyloseq object?](#if-you-have-a-phyloseq-object) #### Main functions: