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new file mode 100644 |
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| 1 |
+x1 <- t(scale(t(m))) |
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| 2 |
+x1 |
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| 3 |
+m[gene.sets[[1]],] |
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| 4 |
+x1[gene.sets[[1]], ] |
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| 5 |
+x1 <- t(scale(t(m))) |
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| 6 |
+x1 <- svd(x1) |
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| 7 |
+x1 <- x1$v[,1] |
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| 8 |
+x1 |
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| 9 |
+x2 <- runExactSVD(m, scale = TRUE, center = TRUE) |
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| 10 |
+x2 <- x2$v[,1] |
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| 11 |
+x2 |
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| 12 |
+x2 <- runExactSVD(t(m), scale = TRUE, center = TRUE) |
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| 13 |
+x2 <- x2$v[,1] |
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| 14 |
+x2 |
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| 15 |
+x1 |
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| 16 |
+t(m) |
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| 17 |
+scale(t(m)) |
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| 18 |
+x2 <- runExactSVD(m, scale = TRUE, center = TRUE) |
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| 19 |
+x1 <- t(scale(t(m))) |
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| 20 |
+x1 <- svd(x1) |
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| 21 |
+x1 <- x1$v[,1] |
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| 22 |
+x1 |
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| 23 |
+x2 <- runExactSVD(m, scale = colMeans(m), center = TRUE) |
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| 24 |
+x2 <- x2$v[,1] |
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| 25 |
+x2 |
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| 26 |
+colMeans(m) |
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| 27 |
+x2 <- runExactSVD(m, scale = colSds(m), center = TRUE) |
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| 28 |
+x2 <- x2$v[,1] |
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| 29 |
+x2 |
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| 30 |
+x1 |
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| 31 |
+x2 |
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| 32 |
+x2 <- runExactSVD(m, scale = colSds(m), center = colMeans(m)) |
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| 33 |
+x2 <- x2$v[,1] |
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| 34 |
+x1 |
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| 35 |
+x2 |
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| 36 |
+colSds(m) |
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| 37 |
+colMeans(m) |
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| 38 |
+x2 <- runExactSVD(t(m), scale = colSds(m), center = colMeans(m)) |
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| 39 |
+x2 <- x2$v[,1] |
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| 40 |
+x2 |
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| 41 |
+x1 |
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| 42 |
+x2 <- runExactSVD(m, scale = colSds(t(m)), center = colMeans(t(m))) |
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| 43 |
+x2 <- x2$v[,1] |
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| 44 |
+x2 |
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| 45 |
+x1 |
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| 46 |
+x1 <- t(scale(t(m))) |
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| 47 |
+x1 |
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| 48 |
+colSds(m) |
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| 49 |
+rowSds(m) |
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| 50 |
+x1 <- t(scale(t(m))) |
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| 51 |
+x1 <- svd(x1) |
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| 52 |
+x1 <- x1$v[,1] |
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| 53 |
+x1 |
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| 54 |
+x2 <- runExactSVD(m, center= colMeans(m), scale= rowSds(m)) |
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| 55 |
+x2 <- x2$v[,1] |
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| 56 |
+x2 |
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| 57 |
+x2 <- runExactSVD(m, center= colMeans(m), scale= rowSds(m)) |
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| 58 |
+x2$v[,1] |
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| 59 |
+x3 <- runSVD(m, k=Inf) |
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| 60 |
+x4 <- runExactSVD(m) |
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| 61 |
+x3 |
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| 62 |
+x4 <- runExactSVD(m) |
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| 63 |
+x4 |
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| 64 |
+x1 <- t(scale(t(m))) |
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| 65 |
+x1 <- svd(x1[gene.sets[[1]],]) |
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| 66 |
+x1 <- x1$v[,1] |
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| 67 |
+x1 |
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| 68 |
+x2 <- runExactSVD(m[gene.sets[[1]],], center= colMeans(m), scale= rowSds(m)) |
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| 69 |
+x2 <- x2$v[,1] |
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| 70 |
+x2 |
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| 71 |
+x3 <- runSVD(m[gene.sets[[1]],], k=Inf) |
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| 72 |
+x3$v[,1] |
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| 73 |
+x2 |
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| 74 |
+x2 <- runExactSVD(m[gene.sets[[1]],], center= colMeans(m), scale= rowSds(m)) |
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| 75 |
+x2 |
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| 76 |
+x1 |
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| 77 |
+x1 <- t(scale(t(m))) |
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| 78 |
+x1 |
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| 79 |
+x1 <- svd(x1[gene.sets[[1]],]) |
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| 80 |
+x1 |
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| 81 |
+x2 <- runExactSVD(m[gene.sets[[1]],], center= colMeans(m), scale= rowSds(m)) |
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| 82 |
+x2 |
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| 83 |
+rowSds(m) |
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| 84 |
+x1 <- t(scale(t(m))) |
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| 85 |
+x1 <- svd(x1[gene.sets[[1]],]) |
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| 86 |
+x1 <- x1$v[,1] |
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| 87 |
+x1 |
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| 88 |
+x2 <- runExactSVD(m[gene.sets[[1]],], center= colMeans(m), scale= rowSds(t(m))) |
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| 89 |
+x2 <- x2$v[,1] |
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| 90 |
+x2 |
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| 91 |
+colMeans(m) |
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| 92 |
+apply(m, 2, sd) |
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| 93 |
+x1 <- t(scale(t(m))) |
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| 94 |
+x1 <- svd(x1[gene.sets[[1]],]) |
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| 95 |
+x1 <- x1$v[,1] |
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| 96 |
+x1 |
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| 97 |
+x2 <- runExactSVD(m[gene.sets[[1]],], center= colMeans(m), scale= TRUE) |
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| 98 |
+x2 <- x2$v[,1] |
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| 99 |
+x2 |
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| 100 |
+x1 <- scale(m) |
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| 101 |
+x1 <- t(scale(t(m))) |
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| 102 |
+x1 <- svd(x1[1,]) |
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| 103 |
+x1 |
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| 104 |
+x1 <- svd(x1[1,,drop=FALSE]) |
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| 105 |
+x1 <- svd(x1[1, , drop=FALSE]) |
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| 106 |
+x1[1, , drop=FALSE] |
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| 107 |
+x1[1, drop=FALSE] |
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| 108 |
+x1 <- t(scale(t(m))) |
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| 109 |
+x1 <- t(scale(t(m))) |
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| 110 |
+x1 <- svd(x1[1, ,drop=FALSE]) |
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| 111 |
+x1 |
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| 112 |
+x2 <- runExactSVD(m[1, ]) |
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| 113 |
+x2 <- runExactSVD(m[1, , drop=FALSE]) |
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| 114 |
+x2 |
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| 115 |
+x2 <- runExactSVD(m[1, , drop=FALSE], center=TRUE, scale=TRUE) |
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| 116 |
+x2 |
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| 117 |
+x1 <- t(scale(t(m))) |
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| 118 |
+x1 <- svd(x1[1:2,]) |
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| 119 |
+x1 |
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| 120 |
+x2 <- runExactSVD(m[1:2,], center=TRUE, scale=TRUE) |
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| 121 |
+x2 |
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| 122 |
+x2 <- runExactSVD(m[1:2,], center=colMeans(t(m)), scale=TRUE) |
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| 123 |
+x2 |
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| 124 |
+x <- gene.sets[[1]] |
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| 125 |
+x |
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| 126 |
+x1 <- svd(x1[x,]) |
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| 127 |
+x <- gene.sets[[1]] |
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| 128 |
+x1 <- t(scale(t(m))) |
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| 129 |
+x1 <- svd(x1[x,]) |
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| 130 |
+x1 <- x1$v[,1] |
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| 131 |
+x1 |
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| 132 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= colSds(t(m))) |
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| 133 |
+x2 <- x2$v[,1] |
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| 134 |
+x2 |
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| 135 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= colSds(m)) |
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| 136 |
+x2 <- x2$v[,1] |
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| 137 |
+x2 |
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| 138 |
+x1 <- t(scale(t(m))) |
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| 139 |
+x1 <- svd(x1[x,]) |
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| 140 |
+x1 <- x1$v[,1] |
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| 141 |
+x1 |
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| 142 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(t(m))) |
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| 143 |
+x2 <- x2$v[,1] |
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| 144 |
+x2 |
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| 145 |
+x2 <- runExactSVD(m[x,], center= colMeans(m), scale= rowSds(t(m))) |
|
| 146 |
+x2 <- x2$v[,1] |
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| 147 |
+x2 |
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| 148 |
+x2 <- runExactSVD(m[x,], center= colMeans(m), scale= colSds(t(m))) |
|
| 149 |
+x2 <- x2$v[,1] |
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| 150 |
+x2 |
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| 151 |
+x1 |
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| 152 |
+x2 <- runExactSVD(m[x,], center= colMeans(m), scale= colSds(m)) |
|
| 153 |
+x2 <- x2$v[,1] |
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| 154 |
+x2 |
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| 155 |
+x1 |
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| 156 |
+scale(t(m)) |
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| 157 |
+colMeans(t(m)) |
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| 158 |
+colSds(m) |
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| 159 |
+colSds(t(m)) |
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| 160 |
+rowSds(m) |
|
| 161 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(m)) |
|
| 162 |
+x2 <- x2$v[,1] |
|
| 163 |
+x2 |
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| 164 |
+x1 |
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| 165 |
+x1 <- t(scale(t(m))) |
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| 166 |
+x1 <- svd(x1[x,]) |
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| 167 |
+x1 |
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| 168 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(m)) |
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| 169 |
+x2 |
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| 170 |
+min(dim(m[x,])) |
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| 171 |
+x2 <- runSvd(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m)) |
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| 172 |
+x2 <- runSVD(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m)) |
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| 173 |
+x2 |
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| 174 |
+x1$v[,1] |
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| 175 |
+x2$v[,1] |
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| 176 |
+x2 <- runSVD(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m), BSPARAM = ExactParam()) |
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| 177 |
+x2$v[,1] |
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| 178 |
+x2 <- runSVD(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m), BSPARAM = FastAutoParam()) |
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| 179 |
+x2$v[,1] |
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| 180 |
+x2 <- runSVD(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m), BSPARAM = bsparam()) |
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| 181 |
+x2$v[,1] |
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| 182 |
+x2 <- runSVD(m[x,], k=2, center= colMeans(t(m)), scale= rowSds(m), BSPARAM = ExactParam(deferred=TRUE)) |
|
| 183 |
+x2$v[,1] |
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| 184 |
+x1 <- t(scale(t(m))) |
|
| 185 |
+x1 |
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| 186 |
+colMeans(t(m)) |
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| 187 |
+owSds(m) |
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| 188 |
+rowSds(m) |
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| 189 |
+m |
|
| 190 |
+m <- matrix(sample.int(10, 25, T), 5, 5) |
|
| 191 |
+colnames(m) <- paste0("cell_", 1:5)
|
|
| 192 |
+rownames(m) <- paste0("gene_", 1:5)
|
|
| 193 |
+set.seed(123) |
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| 194 |
+m <- matrix(sample.int(10, 25, T), 5, 5) |
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| 195 |
+colnames(m) <- paste0("cell_", 1:5)
|
|
| 196 |
+rownames(m) <- paste0("gene_", 1:5)
|
|
| 197 |
+gene.sets <- list("my_list1"= c(1,2))
|
|
| 198 |
+m <- matrix(sample.int(10, 25, T), 5, 5) |
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| 199 |
+colnames(m) <- paste0("cell_", 1:5)
|
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| 200 |
+rownames(m) <- paste0("gene_", 1:5)
|
|
| 201 |
+x <- c(1,2) |
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| 202 |
+x1 <- t(scale(t(m))) |
|
| 203 |
+x1 <- svd(x1[x,]) |
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| 204 |
+x1$v[,1] |
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| 205 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(m)) |
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| 206 |
+x2$v[,1] |
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| 207 |
+rowSds(m) |
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| 208 |
+scale(t(m)) |
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| 209 |
+t(scale(t(m))) |
|
| 210 |
+x1 <- t(scale(t(m))) |
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| 211 |
+x1@scaled |
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| 212 |
+str(x) |
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| 213 |
+str(x1) |
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| 214 |
+x1$scaled |
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| 215 |
+x1@scaled |
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| 216 |
+attr(x1) |
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| 217 |
+attr(x1, "scaled:scale") |
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| 218 |
+x2 <- runExactSVD(t(m[x,]), center= colMeans(t(m)), scale= rowSds(m)) |
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| 219 |
+x2$v[,1] |
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| 220 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(m)) |
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| 221 |
+x2$v[,1] |
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| 222 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(t(m))) |
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| 223 |
+x1$v[,1] |
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| 224 |
+x1 <- t(scale(t(m))) |
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| 225 |
+x1 <- svd(x1[x,]) |
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| 226 |
+x1$v[,1] |
|
| 227 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(t(m))) |
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| 228 |
+x2$v[,1] |
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| 229 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= rowSds(m)) |
|
| 230 |
+x2$v[,1] |
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| 231 |
+C <- colMeans(t(m)) |
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| 232 |
+i <- 1 |
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| 233 |
+sqrt(sum((m[,i] - C[i])^2)/(ncol(m)-1)) |
|
| 234 |
+x2 <- runExactSVD(m[x,], center= colMeans(t(m)), scale= TRUE) |
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| 235 |
+x2$v[,1] |
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| 236 |
+x1$v[,1] |
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| 237 |
+colMeans(t(m)) |
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| 238 |
+rowMeans(m) |
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| 239 |
+rowSds(m) |
|
| 240 |
+x1 <- t(scale(t(m))) |
|
| 241 |
+x1 <- svd(x1[x,]) |
|
| 242 |
+x1$v[,1] |
|
| 243 |
+x2 <- runExactSVD(m[x,], center= rowMeans(m), scale= rowSds(m)) |
|
| 244 |
+x2$v[,1] |
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| 245 |
+library(BiocSingular) |
|
| 246 |
+set.seed(123) |
|
| 247 |
+m <- matrix(sample.int(10, 25, T), 5, 5) |
|
| 248 |
+x <- c(1,2) |
|
| 249 |
+x1 <- t(scale(t(m))) |
|
| 250 |
+x1 <- svd(x1[x,]) |
|
| 251 |
+x1$v[,1] |
|
| 252 |
+x2 <- runExactSVD(t(scale(t(m)))) |
|
| 253 |
+x2$v[,1] |
|
| 254 |
+x1 <- t(scale(t(m))) |
|
| 255 |
+res1 <- svd(x1[x,]) |
|
| 256 |
+res1$v[,1] |
|
| 257 |
+x2 <- runExactSVD(x1[x,]) |
|
| 258 |
+x2$v[,1] |
|
| 259 |
+x3 <- runExactSVD(m[x,], center=colMeans(t(m))) |
|
| 260 |
+x3 |
|
| 261 |
+library(matrixStats) |
|
| 262 |
+x1 <- t(scale(t(m))) |
|
| 263 |
+x2 <- t( (t(m) - colMeans(t(m))) / colSds(t(m)) ) |
|
| 264 |
+x1 |
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| 265 |
+x2 |
|
| 266 |
+x2 <- t( (t(m) - colMeans(t(m))) / rowSds(t(m)) ) |
|
| 267 |
+x2 |
|
| 268 |
+x1 <- t(scale(t(m))) |
|
| 269 |
+x2 <- t( (t(m) - colMeans(t(m))) / apply(t(m), 2, sd) ) |
|
| 270 |
+x1 |
|
| 271 |
+x2 |
|
| 272 |
+apply(t(m), 2, sd) |
|
| 273 |
+m |
|
| 274 |
+x1 |
|
| 275 |
+t(m) |
|
| 276 |
+scale(m) |
|
| 277 |
+m |
|
| 278 |
+x1 <- t(scale(t(m))) |
|
| 279 |
+x1 |
|
| 280 |
+x1 <- t(scale(t(m))) |
|
| 281 |
+x2 <- t( (t(m) - colMeans(t(m))) / apply(t(m), 2, sd) ) |
|
| 282 |
+x1 |
|
| 283 |
+x2 |
|
| 284 |
+colMeans(t(m)) |
|
| 285 |
+apply(t(m), 2, sd) |
|
| 286 |
+m |
|
| 287 |
+x1 <- scale(t(m)) |
|
| 288 |
+x2 <- (t(m) - colMeans(t(m))) / apply(t(m), 2, sd) |
|
| 289 |
+x1 |
|
| 290 |
+x2 |
|
| 291 |
+x2 <- (t(m) - colMeans(t(m))) / (apply(t(m), 2, sd)) |
|
| 292 |
+x1 |
|
| 293 |
+x2 |
|
| 294 |
+x1 <- scale(m) |
|
| 295 |
+x2 <- (m - colMeans(m)) / (apply(m, 2, sd)) |
|
| 296 |
+x1 |
|
| 297 |
+x2 |
|
| 298 |
+library(bench) |
|
| 299 |
+m <- matrix(sample.int(10, 25, T), 10, 10) |
|
| 300 |
+genes <- c(1,2) |
|
| 301 |
+x <- t(scale(t(m))) |
|
| 302 |
+m <- matrix(sample.int(10, 25, T), 10, 10) |
|
| 303 |
+genes <- c(1,2) |
|
| 304 |
+bench::mark( |
|
| 305 |
+svd(x[genes,]), |
|
| 306 |
+runExactSVD(x[genes,]) |
|
| 307 |
+) |
|
| 308 |
+library(Matrix) |
|
| 309 |
+m<-rsparsematrix(10,10,.5) |
|
| 310 |
+m |
|
| 311 |
+# m <- matrix(sample.int(10, 25, T), 10, 10) |
|
| 312 |
+m<-rsparsematrix(1000,10000,.5) |
|
| 313 |
+genes <- c(1:400) |
|
| 314 |
+x <- t(scale(t(m))) |
|
| 315 |
+X |
|
| 316 |
+x |
|
| 317 |
+x1 <- t(scale(t(m))) |
|
| 318 |
+x2 <- as(x1, "dgCMatrix") |
|
| 319 |
+bench::mark( |
|
| 320 |
+svd(x1[genes,]), |
|
| 321 |
+runExactSVD(x2[genes,]) |
|
| 322 |
+) |
|
| 323 |
+bench::mark( |
|
| 324 |
+svd(x1[genes,]), |
|
| 325 |
+runExactSVD(m[genes,]) |
|
| 326 |
+) |
|
| 327 |
+bench::mark( |
|
| 328 |
+svd(x1[genes,]), |
|
| 329 |
+runExactSVD(m[genes,]), |
|
| 330 |
+check=FALSE |
|
| 331 |
+) |
|
| 332 |
+bench::mark( |
|
| 333 |
+svd(x1[genes,]), |
|
| 334 |
+runExactSVD(x1[genes,]), |
|
| 335 |
+check=FALSE |
|
| 336 |
+) |
|
| 337 |
+x1 <- NULL |
|
| 338 |
+x2 <- NULL |
|
| 339 |
+m <- NULL |
|
| 340 |
+x <- NULL |
|
| 341 |
+library(BiocSingular) |
|
| 342 |
+set.seed(123) |
|
| 343 |
+m <- matrix(sample.int(10, 25, T), 10, 10) |
|
| 344 |
+genes <- 1:2 |
|
| 345 |
+genes |
|
| 346 |
+genes <- c(1:2) |
|
| 347 |
+genes |
|
| 348 |
+genes <- 1:2 |
|
| 349 |
+m <- matrix(sample.int(10, 25, T), 10, 10) |
|
| 350 |
+genes <- 1:2 |
|
| 351 |
+x1 <- t(scale(t(m))) |
|
| 352 |
+res1 <- svd(x1[genes,]) |
|
| 353 |
+res$v[,1] |
|
| 354 |
+res1$v[,1] |
|
| 355 |
+m |
|
| 356 |
+t(m) |
|
| 357 |
+apply(t(m),2,sd) |
|
| 358 |
+res2 <- runExactSVD(m, center=colMeans(t(m)), scale=apply(t(m),2,sd)) |
|
| 359 |
+res2$v[,1] |
|
| 360 |
+res1$v[,1] |
|
| 361 |
+res2 <- runExactSVD(m, center=colMeans(t(m)), scale=apply(m,2,sd)) |
|
| 362 |
+res2$v[,1] |
|
| 363 |
+svd( t( (t(m) - C) / colSds(t(m)) )[genes,] ) |
|
| 364 |
+t(m) - colMeans(t(m)) |
|
| 365 |
+t(m) |
|
| 366 |
+colMeans(t(m)) |
|
| 367 |
+colSds(t(m)) |
|
| 368 |
+( t(m) - colMeans(t(m)) ) / colSds(t(m)) |
|
| 369 |
+sweep(t(m), 2, colMeanst(m)) |
|
| 370 |
+sweep(t(m), 2, colMeans(t(m))) |
|
| 371 |
+svd( t( sweep(t(m), 2, colMeans(t(m))) / colSds(t(m)) ) ) |
|
| 372 |
+svd( t( sweep(t(m), 2, colMeans(t(m))) / colSds(t(m)) )[genes,] ) |
|
| 373 |
+svd( t( sweep(t(m), 2, colMeans(t(m))) / colSds(t(m)) )[genes,] )$v[,1] |
|
| 374 |
+x1 <- t(scale(t(m))) |
|
| 375 |
+res1 <- svd(x1[genes,]) |
|
| 376 |
+res1$v[,1] |
|
| 377 |
+svd( t( sweep(t(m), 2, colMeans(t(m))) / rowSds(t(m)) )[genes,] )$v[,1] |
|
| 378 |
+res1$v[,1] |
|
| 379 |
+X <- m |
|
| 380 |
+Z <- Matrix::t(X) |
|
| 381 |
+Z <- .dgCapply(Z, scale, 2) |
|
| 382 |
+Z <- Matrix::t(Z) |
|
| 383 |
+Z |
|
| 384 |
+library(Matrix) |
|
| 385 |
+library(SingleCellExperiment) |
|
| 386 |
+library(BiocParallel) |
|
| 387 |
+library(sparseMatrixStats) |
|
| 388 |
+m<-rsparsematrix(10,10,.5) |
|
| 389 |
+colnames(m) <- paste0("cell_", 1:10)
|
|
| 390 |
+rownames(m) <- paste0("gene_", 1:10)
|
|
| 391 |
+# gset.idx.list <- list("my_genes" = sample(rownames(m), 2))
|
|
| 392 |
+geneSets <- list("my_genes1" = c(1,3))
|
|
| 393 |
+library(GSVA) |
|
| 394 |
+x <- gsva(m, geneSets, method="plage") |
|
| 395 |
+colnames(m) <- paste0("cell_", 1:10)
|
|
| 396 |
+rownames(m) <- paste0("gene_", 1:10)
|
|
| 397 |
+x <- gsva(m, geneSets, method="plage") |
|
| 398 |
+# gset.idx.list <- list("my_genes" = sample(rownames(m), 2))
|
|
| 399 |
+geneSets <- list("my_genes1" = c("gene_1","gene_3"))
|
|
| 400 |
+x <- gsva(m, geneSets, method="plage") |
|
| 401 |
+x |
|
| 402 |
+x <- gsva(m, geneSets, method="zscore") |
|
| 403 |
+x |
|
| 404 |
+x <- gsva(m, geneSets, method="ssgsea") |
|
| 405 |
+x |
|
| 406 |
+X <- m |
|
| 407 |
+n <- ncol(X) |
|
| 408 |
+if(is(X, "dgCMatrix")){
|
|
| 409 |
+R <- t(sparseMatrixStats::colRanks(X, ties.method = "average")) |
|
| 410 |
+mode(R) <- "integer" |
|
| 411 |
+} else {
|
|
| 412 |
+R <- apply(X, 2, function(x, p) as.integer(rank(x)), p) |
|
| 413 |
+} |
|
| 414 |
+R |
|
| 415 |
+R <- t(sparseMatrixStats::colRanks(X, ties.method = "average")) |
|
| 416 |
+mode(R) <- "integer" |
|
| 417 |
+R |
|
| 418 |
+R <- apply(X, 2, function(x, p) as.integer(rank(x)), p) |
|
| 419 |
+R |
|
| 420 |
+R <- t(sparseMatrixStats::colRanks(X, ties.method = "average")) |
|
| 421 |
+source('~/curro/gsva-devel/devel3_ssgsea.R', echo=TRUE)
|
|
| 422 |
+mode(R) <- "integer" |
|
| 423 |
+R |
|
| 424 |
+Ra <- abs(R)^alpha |
|
| 425 |
+Ra |
|
| 426 |
+es <- bplapply(as.list(1:n), function(j) {
|
|
| 427 |
+geneRanking <- order(R[, j], decreasing=TRUE) |
|
| 428 |
+es_sample <- lapply(geneSets, .fastRndWalk, geneRanking, j, Ra) |
|
| 429 |
+unlist(es_sample) |
|
| 430 |
+}, BPPARAM=BPPARAM) |
|
| 431 |
+.fastRndWalk <- function(gSetIdx, geneRanking, j, Ra) {
|
|
| 432 |
+n <- length(geneRanking) |
|
| 433 |
+k <- length(gSetIdx) |
|
| 434 |
+idxs <- sort.int(fastmatch::fmatch(gSetIdx, geneRanking)) |
|
| 435 |
+stepCDFinGeneSet2 <- |
|
| 436 |
+sum(Ra[geneRanking[idxs], j] * (n - idxs + 1)) / |
|
| 437 |
+sum((Ra[geneRanking[idxs], j])) |
|
| 438 |
+stepCDFoutGeneSet2 <- (n * (n + 1) / 2 - sum(n - idxs + 1)) / (n - k) |
|
| 439 |
+walkStat <- stepCDFinGeneSet2 - stepCDFoutGeneSet2 |
|
| 440 |
+walkStat |
|
| 441 |
+} |
|
| 442 |
+n <- ncol(X) |
|
| 443 |
+es <- bplapply(as.list(1:n), function(j) {
|
|
| 444 |
+geneRanking <- order(R[, j], decreasing=TRUE) |
|
| 445 |
+es_sample <- lapply(geneSets, .fastRndWalk, geneRanking, j, Ra) |
|
| 446 |
+unlist(es_sample) |
|
| 447 |
+}, BPPARAM=BPPARAM) |
|
| 448 |
+es |
|
| 449 |
+geneSets <- 1,3 |
|
| 450 |
+geneSets <- c(1,3) |
|
| 451 |
+es <- bplapply(as.list(1:n), function(j) {
|
|
| 452 |
+geneRanking <- order(R[, j], decreasing=TRUE) |
|
| 453 |
+es_sample <- lapply(geneSets, .fastRndWalk, geneRanking, j, Ra) |
|
| 454 |
+unlist(es_sample) |
|
| 455 |
+}, BPPARAM=BPPARAM) |
|
| 456 |
+es |
|
| 457 |
+es <- do.call("cbind", es)
|
|
| 458 |
+es |
|
| 459 |
+geneSets <- 1 |
|
| 460 |
+es <- bplapply(as.list(1:n), function(j) {
|
|
| 461 |
+geneRanking <- order(R[, j], decreasing=TRUE) |
|
| 462 |
+es_sample <- lapply(geneSets, .fastRndWalk, geneRanking, j, Ra) |
|
| 463 |
+unlist(es_sample) |
|
| 464 |
+}, BPPARAM=BPPARAM) |
|
| 465 |
+es |
|
| 466 |
+es <- do.call("cbind", es)
|
|
| 467 |
+es |
|
| 468 |
+if (normalization) {
|
|
| 469 |
+## normalize enrichment scores by using the entire data set, as indicated |
|
| 470 |
+## by Barbie et al., 2009, online methods, pg. 2 |
|
| 471 |
+es <- apply(es, 2, function(x, es) x / (range(es)[2] - range(es)[1]), es) |
|
| 472 |
+} |
|
| 473 |
+normalization=TRUE |
|
| 474 |
+if (normalization) {
|
|
| 475 |
+## normalize enrichment scores by using the entire data set, as indicated |
|
| 476 |
+## by Barbie et al., 2009, online methods, pg. 2 |
|
| 477 |
+es <- apply(es, 2, function(x, es) x / (range(es)[2] - range(es)[1]), es) |
|
| 478 |
+} |
|
| 479 |
+es |
|
| 480 |
+rownames(es) <- names(geneSets) |
|
| 481 |
+colnames(es) <- colnames(X) |
|
| 482 |
+geneSets <- c(1,3) |
|
| 483 |
+es <- bplapply(as.list(1:n), function(j) {
|
|
| 484 |
+geneRanking <- order(R[, j], decreasing=TRUE) |
|
| 485 |
+es_sample <- lapply(geneSets, .fastRndWalk, geneRanking, j, Ra) |
|
| 486 |
+unlist(es_sample) |
|
| 487 |
+}, BPPARAM=BPPARAM) |
|
| 488 |
+es <- do.call("cbind", es)
|
|
| 489 |
+if (normalization) {
|
|
| 490 |
+## normalize enrichment scores by using the entire data set, as indicated |
|
| 491 |
+## by Barbie et al., 2009, online methods, pg. 2 |
|
| 492 |
+es <- apply(es, 2, function(x, es) x / (range(es)[2] - range(es)[1]), es) |
|
| 493 |
+} |
|
| 494 |
+es |
|
| 495 |
+if(is(X, "dgCMatrix")){
|
|
| 496 |
+es <- as(es, "dgCMatrix") |
|
| 497 |
+} |
|
| 498 |
+es |
|
| 499 |
+rownames(es) <- names(geneSets) |
|
| 500 |
+colnames(es) <- colnames(X) |
|
| 501 |
+if(is(X, "dgCMatrix")){
|
|
| 502 |
+es <- as(es, "dgCMatrix") |
|
| 503 |
+} |
|
| 504 |
+es |
|
| 505 |
+setwd("~/curro/gsva-devel/GSVA")
|
|
| 506 |
+devtools::check(vignettes = FALSE) |
|
| 507 |
+devtools::check(vignettes = FALSE) |
|
| 508 |
+devtools::build(vignettes = FALSE) |
|
| 509 |
+devtools::install(build_vignettes = FALSE) |
|
| 510 |
+x <- gsva(sce, gset.idx.list, method="zscore") |
|
| 511 |
+x <- gsva(sce, gset.idx.list, method="ssgsea") |
|
| 512 |
+X |
| ... | ... |
@@ -1,7 +1,7 @@ |
| 1 | 1 |
.filterFeatures <- function(expr, method) {
|
| 2 | 2 |
|
| 3 | 3 |
## filter out genes with constant expression values |
| 4 |
- sdGenes <- apply(expr, 1, sd) |
|
| 4 |
+ sdGenes <- DelayedMatrixStats::rowSds(expr) |
|
| 5 | 5 |
if (any(sdGenes == 0) || any(is.na(sdGenes))) {
|
| 6 | 6 |
warning(sum(sdGenes == 0 | is.na(sdGenes)), |
| 7 | 7 |
" genes with constant expression values throuhgout the samples.") |
