diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index e357f7e..04efc72 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -16,7 +16,6 @@ jobs: - name: Check out repository uses: actions/checkout@v4 - - name: Set up specific version of Python uses: actions/setup-python@v5 with: @@ -28,13 +27,11 @@ jobs: - name: Set up Quarto uses: quarto-dev/quarto-actions/setup@v2 - # - name: Install AWS CLI - # uses: unfor19/install-aws-cli-action@v1 - name: Cache usecase_data uses: actions/cache@v2 with: path: | - notebooks/usecase_data + book/usecase/data key: usecase_data_${{ runner.os }} - name: Make sure data is available @@ -43,26 +40,7 @@ jobs: aws s3 cp \ --no-sign-request \ s3://openproblems-bio/public/neurips-2023-competition/sc_counts_reannotated_with_counts.h5ad \ - notebooks/usecase_data/sc_counts_reannotated_with_counts.h5ad - - # attempt with pixi: - - # - name: Install pixi - # run: curl -fsSL https://pixi.sh/install.sh | bash - - # - name: Cache several folders - # uses: actions/cache@v2 - # with: - # path: | - # _freeze - # .pixi - # key: pixi_${{ runner.os }} - - # - name: Set up dependencies - # run: pixi install -e dev - - # - name: Render slides - # run: pixi run render_slides + book/usecase/data/sc_counts_reannotated_with_counts.h5ad # attempt with renv - name: Set up renv diff --git a/README.md b/README.md index 3e39c7b..af6cf48 100644 --- a/README.md +++ b/README.md @@ -93,10 +93,3 @@ To use the environment, use the following command: source renv/python/virtualenvs/renv-python-3.12/bin/activate quarto preview ``` - -Or to render the slides: - -```bash -source renv/python/virtualenvs/renv-python-3.12/bin/activate -quarto render -``` \ No newline at end of file diff --git a/_extensions/quarto-ext/fontawesome/_extension.yml b/_extensions/quarto-ext/fontawesome/_extension.yml new file mode 100644 index 0000000..c0787a8 --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/_extension.yml @@ -0,0 +1,7 @@ +title: Font Awesome support +author: Carlos Scheidegger +version: 1.1.0 +quarto-required: ">=1.2.269" +contributes: + shortcodes: + - fontawesome.lua diff --git a/_extensions/quarto-ext/fontawesome/assets/css/all.css b/_extensions/quarto-ext/fontawesome/assets/css/all.css new file mode 100644 index 0000000..3e24980 --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/assets/css/all.css @@ -0,0 +1,7971 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands, +.fal, +.fa-light, +.fat, +.fa-thin, +.fad, +.fa-duotone { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before 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content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + 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+.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/FontAwesome6Brands-Regular-400.woff2") format("woff2"), url("../webfonts/FontAwesome6Brands-Regular-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + 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+.fa-square-snapchat:before { + content: "\f2ad"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-evernote:before { + content: "\f839"; } + +.fa-hacker-news:before { + content: "\f1d4"; } + +.fa-creative-commons-sampling:before { + content: "\f4f0"; } + +.fa-adversal:before { + content: "\f36a"; } + +.fa-creative-commons:before { + content: "\f25e"; } + +.fa-watchman-monitoring:before { + content: "\e087"; } + +.fa-fonticons:before { + content: "\f280"; } + +.fa-weixin:before { + content: "\f1d7"; } + +.fa-shirtsinbulk:before { + content: "\f214"; } + +.fa-codepen:before { + content: "\f1cb"; } + +.fa-git-alt:before { + content: "\f841"; } + +.fa-lyft:before { + content: "\f3c3"; } + +.fa-rev:before { + content: "\f5b2"; } + +.fa-windows:before { + content: "\f17a"; } + +.fa-wizards-of-the-coast:before { + content: "\f730"; } + +.fa-square-viadeo:before { + content: "\f2aa"; } + +.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa-meetup:before { + content: "\f2e0"; } + +.fa-centos:before { + content: "\f789"; } + +.fa-adn:before { + content: "\f170"; } + +.fa-cloudsmith:before { + content: "\f384"; } + +.fa-pied-piper-alt:before { + content: "\f1a8"; } + +.fa-square-dribbble:before { + content: "\f397"; } + +.fa-dribbble-square:before { + content: "\f397"; } + +.fa-codiepie:before { + content: "\f284"; } + +.fa-node:before { + content: "\f419"; } + +.fa-mix:before { + content: "\f3cb"; } + +.fa-steam:before { + content: "\f1b6"; } + +.fa-cc-apple-pay:before { + content: "\f416"; } + +.fa-scribd:before { + content: "\f28a"; } + +.fa-debian:before { + content: "\e60b"; } + +.fa-openid:before { + content: "\f19b"; } + +.fa-instalod:before { + content: "\e081"; } + +.fa-expeditedssl:before { + content: "\f23e"; } + +.fa-sellcast:before { + content: "\f2da"; } + +.fa-square-twitter:before { + content: "\f081"; } + +.fa-twitter-square:before { + content: "\f081"; } + +.fa-r-project:before { + content: "\f4f7"; } + +.fa-delicious:before { + content: "\f1a5"; } + +.fa-freebsd:before { + content: "\f3a4"; } + +.fa-vuejs:before { + content: "\f41f"; } + +.fa-accusoft:before { + content: "\f369"; } + +.fa-ioxhost:before { + content: "\f208"; } + +.fa-fonticons-fi:before { + content: "\f3a2"; } + +.fa-app-store:before { + content: "\f36f"; } + +.fa-cc-mastercard:before { + content: "\f1f1"; } + +.fa-itunes-note:before { + content: "\f3b5"; } + +.fa-golang:before { + content: "\e40f"; } + +.fa-kickstarter:before { + content: "\f3bb"; } + +.fa-grav:before { + content: "\f2d6"; } + +.fa-weibo:before { + content: "\f18a"; } + +.fa-uncharted:before { + content: "\e084"; } + +.fa-firstdraft:before { + content: "\f3a1"; } + +.fa-square-youtube:before { + content: "\f431"; } + +.fa-youtube-square:before { + content: "\f431"; } + +.fa-wikipedia-w:before { + content: "\f266"; } + +.fa-wpressr:before { + content: "\f3e4"; } + +.fa-rendact:before { + content: "\f3e4"; } + +.fa-angellist:before { + content: "\f209"; } + +.fa-galactic-republic:before { + content: "\f50c"; } + +.fa-nfc-directional:before { + content: "\e530"; } + +.fa-skype:before { + content: "\f17e"; } + +.fa-joget:before { + content: "\f3b7"; } + +.fa-fedora:before { + content: "\f798"; } + +.fa-stripe-s:before { + content: "\f42a"; } + +.fa-meta:before { + content: "\e49b"; } + +.fa-laravel:before { + content: "\f3bd"; } + +.fa-hotjar:before { + content: "\f3b1"; } + +.fa-bluetooth-b:before { + content: "\f294"; } + +.fa-sticker-mule:before { + content: "\f3f7"; } + +.fa-creative-commons-zero:before { + content: "\f4f3"; } + +.fa-hips:before { + content: "\f452"; } + +.fa-behance:before { + content: "\f1b4"; } + +.fa-reddit:before { + content: "\f1a1"; } + +.fa-discord:before { + content: "\f392"; } + +.fa-chrome:before { + content: "\f268"; } + +.fa-app-store-ios:before { + content: "\f370"; } 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b/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.woff2 differ diff --git a/_extensions/quarto-ext/fontawesome/fontawesome.lua b/_extensions/quarto-ext/fontawesome/fontawesome.lua new file mode 100644 index 0000000..ff64dca --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/fontawesome.lua @@ -0,0 +1,84 @@ +local function ensureLatexDeps() + quarto.doc.use_latex_package("fontawesome5") +end + +local function ensureHtmlDeps() + quarto.doc.add_html_dependency({ + name = 'fontawesome6', + version = '0.1.0', + stylesheets = {'assets/css/all.css', 'assets/css/latex-fontsize.css'} + }) +end + +local function isEmpty(s) + return s == nil or s == '' +end + +local function isValidSize(size) + local validSizes = { + "tiny", + "scriptsize", + "footnotesize", + "small", + "normalsize", + "large", + "Large", + "LARGE", + "huge", + "Huge" + } + for _, v in ipairs(validSizes) do + if v == size then + return size + end + end + return "" +end + +return { + ["fa"] = function(args, kwargs) + + local group = "solid" + local icon = pandoc.utils.stringify(args[1]) + if #args > 1 then + group = icon + icon = pandoc.utils.stringify(args[2]) + end + + local title = pandoc.utils.stringify(kwargs["title"]) + if not isEmpty(title) then + title = " title=\"" .. title .. "\"" + end + + local label = pandoc.utils.stringify(kwargs["label"]) + if isEmpty(label) then + label = " aria-label=\"" .. icon .. "\"" + else + label = " aria-label=\"" .. label .. "\"" + end + + local size = pandoc.utils.stringify(kwargs["size"]) + + -- detect html (excluding epub which won't handle fa) + if quarto.doc.is_format("html:js") then + ensureHtmlDeps() + if not isEmpty(size) then + size = " fa-" .. size + end + return pandoc.RawInline( + 'html', + "" + ) + -- detect pdf / beamer / latex / etc + elseif quarto.doc.is_format("pdf") then + ensureLatexDeps() + if isEmpty(isValidSize(size)) then + return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") + else + return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") + end + else + return pandoc.Null() + end + end +} diff --git a/_quarto.yml b/_quarto.yml index d78fa0b..56da45d 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -14,11 +14,11 @@ book: repo-actions: [edit, issue, source] chapters: - index.qmd - - book/intro.qmd - - notebooks/usecase.qmd - - notebooks/file_formats.qmd - - notebooks/in_memory.qmd - - notebooks/workflows/index.qmd + - book/introduction.qmd + - book/usecase/index.qmd + - book/in_memory_interoperability.qmd + - book/on_disk_interoperability.qmd + - book/workflow_frameworks/index.qmd - book/book_slides.qmd - book/references.qmd diff --git a/notebooks/in_memory2.qmd b/book/in_memory2.qmd similarity index 100% rename from notebooks/in_memory2.qmd rename to book/in_memory2.qmd diff --git a/notebooks/in_memory.qmd b/book/in_memory_interoperability.qmd similarity index 93% rename from notebooks/in_memory.qmd rename to book/in_memory_interoperability.qmd index e3788c7..75f53cf 100644 --- a/notebooks/in_memory.qmd +++ b/book/in_memory_interoperability.qmd @@ -1,5 +1,5 @@ --- -title: In memory interoperability (from Python) +title: In-memory interoperability engine: knitr --- @@ -13,7 +13,7 @@ Read in the anndata object ```{python} import anndata as ad -adata_path = "usecase_data/sc_counts_reannotated_with_counts.h5ad" +adata_path = "usecase/data/sc_counts_reannotated_with_counts.h5ad" adata = ad.read_h5ad(adata_path) ``` @@ -54,7 +54,7 @@ We will perform the Compute DE step not in R, but in Python ```{python load_data} import anndata as ad -pd_adata = ad.read_h5ad("usecase_data/pseudobulk.h5ad") +pd_adata = ad.read_h5ad("usecase/data/pseudobulk.h5ad") ``` Select small molecule and control: @@ -153,6 +153,6 @@ Write to disk: with (robjects.default_converter + pandas2ri.converter).context(): res_pd = robjects.conversion.get_conversion().rpy2py(res) - res_pd.to_csv("usecase_data/de_contrasts.csv") + res_pd.to_csv("usecase/data/de_contrasts.csv") ``` diff --git a/book/intro.qmd b/book/intro.qmd deleted file mode 100644 index 4a9b827..0000000 --- a/book/intro.qmd +++ /dev/null @@ -1,10 +0,0 @@ -# Introduction - -Any bioinformatician that has analysed a single-cell dataset knows that using methods developed for different ecosystems or programming languages is necessary but painful. -Any package developer has asked themselves the question on how to best provide access to their tool or method. - -We will give an overview of the interoperability tools you can use when analysing a single-cell dataset: do you want to convert your data to a different data format, or is just calling one R function in your Jupyter notebook sufficient? Do you want fine-grained control over each step in the analysis pipeline or do you run a series of scripts that you really should convert to a workflow system? - -We will give information on different options for package developers to provide better interoperability. Should you reimplement your package in a new language? How do you ensure that the results are the same? - -In order to follow this workshop, we expect the participants to have some Python or R programming knowledge. diff --git a/book/introduction.qmd b/book/introduction.qmd new file mode 100644 index 0000000..a878f6f --- /dev/null +++ b/book/introduction.qmd @@ -0,0 +1,30 @@ +--- +title: Introduction +engine: knitr +--- + +Single-cell analysis has emerged as a transformative force in biology, +providing unprecedented insights into cellular heterogeneity and complex biological processes. The rapid advancement in this field has led to a proliferation of specialized tools and methods [@Zappia2021], often developed in different programming languages and software ecosystems. While this diversity empowers researchers to leverage the best tools for each analysis step [@Heumos2023], it also presents a significant challenge: how to seamlessly integrate and execute analyses across these disparate languages and frameworks. + +The need to utilize tools from different programming ecosystems creates a "polyglot" landscape in single-cell analysis, where researchers must navigate the complexities of interoperability, data exchange, and workflow management. This fragmentation can hinder productivity, introduce errors, and impede reproducibility. + +Researchers can approach this challenge in various ways, each with its own trade-offs and considerations. In the next chapters, we'll explore different strategies for achieving interoperability in single-cell analysis, including: + +## Code porting + +Porting tools from one language to another can offer complete control and eliminate interoperability concerns. However, one should not underestimate the effort required to reimplement complex algorithms, and the risk of introducing errors. + +Furthermore, work is not done after the initial port -- in order for the researcher's work to be useful to others, the ported code must be maintained and kept up-to-date with the original implementation. For this reason, we don't consider reimplementation a viable option for most use-cases and will not discuss it further in this book. + +## In-memory Interoperability + +Tools like rpy2 and reticulate allow for direct communication between languages within a single analysis session. This approach provides flexibility and avoids intermediate file I/O, but can introduce complexity in managing dependencies and environments. + + +## File-based Interoperability + +Storing intermediate results in standardized, language-agnostic file formats (e.g., HDF5, Parquet) allows for sequential execution of scripts written in different languages. This approach is relatively simple but can lead to increased storage requirements and I/O overhead. + +## Workflow Frameworks + +Workflow management systems (e.g., Nextflow, Snakemake) provide a structured approach to orchestrate complex, multi-language pipelines, enhancing reproducibility and automation. However, they may require a learning curve and additional configuration. diff --git a/notebooks/anndataR.qmd b/book/on_disk_interoperability.qmd similarity index 50% rename from notebooks/anndataR.qmd rename to book/on_disk_interoperability.qmd index 6d1014f..50a9d62 100644 --- a/notebooks/anndataR.qmd +++ b/book/on_disk_interoperability.qmd @@ -1,15 +1,27 @@ --- -title: "WIP: In memory interoperability (Python side)" -engine: jupyter +title: On-disk interoperability +engine: knitr --- +Data format based interoperability -# anndataR +1. h5ad / zarr / Apache Arrow +2. Reading and writing these formats -Calling python from R and vice versa +## Setup -1. rpy2 & reticulate -2. How to do this in jupyter notebooks and rmarkdown scripts +```{python} +import anndata +import numpy +import scanpy +``` + +```{python} +anndata.__version__ +``` + + +## anndataR ```{r} library(anndataR) diff --git a/book/references.bib b/book/references.bib index c131410..868e557 100644 --- a/book/references.bib +++ b/book/references.bib @@ -51,3 +51,17 @@ @article{Wratten2021 month = sep, pages = {1161–1168} } + +@article{Zappia2021, + title = {Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape}, + volume = {22}, + ISSN = {1474-760X}, + url = {http://dx.doi.org/10.1186/s13059-021-02519-4}, + DOI = {10.1186/s13059-021-02519-4}, + number = {1}, + journal = {Genome Biology}, + publisher = {Springer Science and Business Media LLC}, + author = {Zappia, Luke and Theis, Fabian J.}, + year = {2021}, + month = oct +} diff --git a/book/usecase/.gitignore b/book/usecase/.gitignore new file mode 100644 index 0000000..3af0ccb --- /dev/null +++ b/book/usecase/.gitignore @@ -0,0 +1 @@ +/data diff --git a/assets/perturbation_prediction.png b/book/usecase/images/perturbation_prediction.png similarity index 100% rename from assets/perturbation_prediction.png rename to book/usecase/images/perturbation_prediction.png diff --git a/notebooks/usecase.qmd b/book/usecase/index.qmd similarity index 88% rename from notebooks/usecase.qmd rename to book/usecase/index.qmd index 1adcdc6..4dc4812 100644 --- a/notebooks/usecase.qmd +++ b/book/usecase/index.qmd @@ -1,14 +1,13 @@ --- title: "Use-case" engine: knitr -bibliography: ../book/references.bib --- The provided dataset is a perturb-seq dataset that was generated as part of an OpenProblems competition on predicting how small molecules change gene expression in different cell types [@kaggle-op3]. The winners of the competition have since been added to the OpenProblems Perturbation Prediction benchmarking task [@openproblems-pp]. As part of the competition, competitors received the differential gene expression (DGE) of genes in cell types treated with different small molecules, and had to predict the DGE of genes in cell types of which some small molecules were withheld. The task is illustrated in the figure below [@fig-dge]. -![DGE perturbation prediction task](../assets/perturbation_prediction.png){#fig-dge} +![DGE perturbation prediction task](images/perturbation_prediction.png){#fig-dge} The dataset consists of single-cell RNA-seq data from 4 cell types treated with 144 small molecules in 3 replicates. The DGE matrix (which competitors received) was in the following steps: @@ -25,11 +24,11 @@ The dataset has since been uploaded to SRA [@sra-op3], will be uploaded to GEO, If you haven't already, you can download the dataset from S3 using the following command: ```{bash s3_sync} -if [[ ! -f usecase_data/sc_counts_reannotated_with_counts.h5ad ]]; then +if [[ ! -f data/sc_counts_reannotated_with_counts.h5ad ]]; then aws s3 cp \ --no-sign-request \ s3://openproblems-bio/public/neurips-2023-competition/sc_counts_reannotated_with_counts.h5ad \ - usecase_data/sc_counts_reannotated_with_counts.h5ad + data/sc_counts_reannotated_with_counts.h5ad fi ``` @@ -40,7 +39,7 @@ The dataset is stored in an AnnData object, which can be loaded in Python as fol ```{python load_anndata} import anndata as ad -adata = ad.read_h5ad("usecase_data/sc_counts_reannotated_with_counts.h5ad") +adata = ad.read_h5ad("data/sc_counts_reannotated_with_counts.h5ad") adata ``` @@ -50,7 +49,7 @@ The same code can be run in R using the `anndata` package (not run): ```r library(anndata) -adata <- read_h5ad("usecase_data/sc_counts_reannotated_with_counts.h5ad") +adata <- read_h5ad("data/sc_counts_reannotated_with_counts.h5ad") adata ``` @@ -115,7 +114,7 @@ pb_adata = ad.AnnData( Store to disk: ```{python store_pseudobulk} -pb_adata.write_h5ad("usecase_data/pseudobulk.h5ad") +pb_adata.write_h5ad("data/pseudobulk.h5ad") ``` ## 5. Compute DE @@ -124,7 +123,7 @@ pb_adata.write_h5ad("usecase_data/pseudobulk.h5ad") library(anndata) library(dplyr, warn.conflicts = FALSE) -pb_adata <- read_h5ad("usecase_data/pseudobulk.h5ad") +pb_adata <- read_h5ad("data/pseudobulk.h5ad") ``` Select small molecule and control: @@ -167,6 +166,6 @@ res |> Write to disk: ```{r write_results} -write.csv(res, "usecase_data/de_contrasts.csv") +write.csv(res, "data/de_contrasts.csv") ``` diff --git a/book/workflow_frameworks/data/criteria.csv b/book/workflow_frameworks/data/criteria.csv new file mode 100644 index 0000000..20228a6 --- /dev/null +++ b/book/workflow_frameworks/data/criteria.csv @@ -0,0 +1,44 @@ +"name","aspect","item" +"Polyglot","Is it easy to switch between different programming / scripting language within one workflow?","Calling a script from another language is possible" +"Polyglot","Is it easy to switch between different programming / scripting language within one workflow?","Components can be written in multiple languages and communicate via a file-based interface" +"Polyglot","Is it easy to switch between different programming / scripting language within one workflow?","Components can be written in multiple languages and communicate via an in-memory data channel" +"Community","Does a library of components exist?","A library of OSS components is available" +"Community","Does a library of components exist?","Community repository has frequent commits" +"Community","Does a library of components exist?","Community repository has > 10 frequent non-employee commitors" +"Community","Does a library of components exist?","Are there repositories of OSS workflows available?" +"Modularity","How effectively does the framework promote modular design and reusability?","Software can be easily encapsulated as a modular component." +"Modularity","How effectively does the framework promote modular design and reusability?","Components have well-defined inputs and outputs." +"Modularity","How effectively does the framework promote modular design and reusability?","Components can be shared and reused across different workflows." +"Modularity","How effectively does the framework promote modular design and reusability?","The framework supports dependency management" +"Modularity","How effectively does the framework promote modular design and reusability?","The framework supports versioning of components." +"Scalability","How well does the framework handle large and complex workflows?","The framework supports asynchronous and distributed execution." +"Scalability","How well does the framework handle large and complex workflows?","Resource management and optimization features are available." +"Scalability","How well does the framework handle large and complex workflows?","Performance monitoring and profiling tools are provided." +"Reproducibility","How effectively does the framework ensure reproducibility of results?","Individual components can list their software dependencies" +"Reproducibility","How effectively does the framework ensure reproducibility of results?","Per-component containerisation is supported" +"Reproducibility","How effectively does the framework ensure reproducibility of results?","Extending images with additional dependencies is supported" +"Reproducibility","How effectively does the framework ensure reproducibility of results?","Data provenance tracking is built-in or can be easily integrated." +"Reproducibility","How effectively does the framework ensure reproducibility of results?","Framework promotes versioned releases of the workflow software and images to ensure reproducibility" +"Portability","Support for various compute platforms","Support for AWS Batch" +"Portability","Support for various compute platforms","Support for Azure Batch" +"Portability","Support for various compute platforms","Support for Google Cloud" +"Portability","Support for various compute platforms","Support for Kubernetes" +"Portability","Support for various compute platforms","Support for Local execution" +"Portability","Support for various compute platforms","Support for PBS/Torque" +"Portability","Support for various compute platforms","Support for SLURM" +"Portability","Support for various compute platforms","Support for additional compute platforms" +"Portability","Support for various containerization technologies","Support for Apptainer" +"Portability","Support for various containerization technologies","Support for Docker" +"Portability","Support for various containerization technologies","Support for Podman" +"Portability","Support for various containerization technologies","Support for additional containerization technologies" +"Portability","Support for various storage solutions","AWS S3" +"Portability","Support for various storage solutions","Azure Blob Storage / Files" +"Portability","Support for various storage solutions","Google Storage" +"Portability","Support for various storage solutions","HTTPS" +"Portability","Support for various storage solutions","FTP" +"Portability","Support for various storage solutions","Support for additional storage solutions" +"Maintainability","How easy is it to update, modify, and troubleshoot workflows?","Workflows are well-structured and easy to understand." +"Maintainability","How easy is it to update, modify, and troubleshoot workflows?","Framework supports documenting the interfaces of components and workflows" +"Maintainability","How easy is it to update, modify, and troubleshoot workflows?","Version control and collaboration features facilitate team-based development." +"Maintainability","How easy is it to update, modify, and troubleshoot workflows?","Components can be unit tested" +"Maintainability","How easy is it to update, modify, and troubleshoot workflows?","Documentation specifies how to unit test components" diff --git a/book/workflow_frameworks/data/wf_aggregated_scores.csv b/book/workflow_frameworks/data/wf_aggregated_scores.csv new file mode 100644 index 0000000..76be3c4 --- /dev/null +++ b/book/workflow_frameworks/data/wf_aggregated_scores.csv @@ -0,0 +1,36 @@ +"name","framework","mean_score" +"Community","Argo Workflows",0.125 +"Community","Galaxy",0.75 +"Community","Nextflow",1 +"Community","Snakemake",0.75 +"Community","Viash + Nextflow",0.75 +"Maintainability","Argo Workflows",0.5 +"Maintainability","Galaxy",0.6 +"Maintainability","Nextflow",0.5 +"Maintainability","Snakemake",0.7 +"Maintainability","Viash + Nextflow",1 +"Modularity","Argo Workflows",0.8 +"Modularity","Galaxy",0.9 +"Modularity","Nextflow",0.5 +"Modularity","Snakemake",1 +"Modularity","Viash + Nextflow",1 +"Polyglot","Argo Workflows",0.666666666666667 +"Polyglot","Galaxy",0.333333333333333 +"Polyglot","Nextflow",0.666666666666667 +"Polyglot","Snakemake",0.666666666666667 +"Polyglot","Viash + Nextflow",0.666666666666667 +"Portability","Argo Workflows",0.5 +"Portability","Galaxy",0.666666666666667 +"Portability","Nextflow",0.944444444444444 +"Portability","Snakemake",0.888888888888889 +"Portability","Viash + Nextflow",0.944444444444444 +"Reproducibility","Argo Workflows",0.7 +"Reproducibility","Galaxy",0.8 +"Reproducibility","Nextflow",0.5 +"Reproducibility","Snakemake",0.7 +"Reproducibility","Viash + Nextflow",0.8 +"Scalability","Argo Workflows",0.5 +"Scalability","Galaxy",0.333333333333333 +"Scalability","Nextflow",0.833333333333333 +"Scalability","Snakemake",0.833333333333333 +"Scalability","Viash + Nextflow",0.833333333333333 diff --git a/book/workflow_frameworks/data/wf_metadata.csv b/book/workflow_frameworks/data/wf_metadata.csv new file mode 100644 index 0000000..eb58bc6 --- /dev/null +++ b/book/workflow_frameworks/data/wf_metadata.csv @@ -0,0 +1,8 @@ +"project","reviewer","project_website","project_description","project_repository","documentation","doi","community","community_repository","id" +"Nextflow","R","https://www.nextflow.io/","A domain-specific language for creating scalable and reproducible data analysis pipelines.","https://github.com/nextflow-io/nextflow","https://www.nextflow.io/docs/latest/index.html","https://doi.org/10.1038/nbt.3820","https://nf-co.re/","https://github.com/nf-core/","nextflow" +"Snakemake","J/M","https://snakemake.github.io/","A workflow management system that uses Python-based rules to define dependencies and execute tasks.","https://github.com/snakemake/snakemake","https://snakemake.readthedocs.io/en/stable/","https://doi.org/10.12688/f1000research.29032.2","https://snakemake-wrappers.readthedocs.io/en/stable/","https://github.com/snakemake-workflows +https://github.com/snakemake/snakemake-wrappers","snakemake" +"Galaxy","R","https://galaxyproject.org/","A web-based platform for creating, running, and sharing data analysis workflows without the need for programming expertise.","https://github.com/galaxyproject/galaxy","https://docs.galaxyproject.org/en/master/","https://doi.org/10.1093/nar/gkae410","https://galaxyproject.org/iuc/","https://github.com/galaxyproject/tools-iuc","galaxy" +"Viash + Nextflow","R","https://viash.io/","A combination of Viash, a tool for defining bioinformatics workflow components, and Nextflow for scalable and reproducible execution.","https://github.com/viash-io/viash","https://viash.io/guide/ +https://www.nextflow.io/docs/latest/index.html","https://joss.theoj.org/papers/10.21105/joss.06089","https://web.viash-hub.com/packages","https://github.com/viash-hub","viash_nextflow" +"Argo Workflows","T","https://argoproj.github.io/workflows/","A Kubernetes-native workflow engine for orchestrating containerized tasks and automating complex workflows.","https://github.com/argoproj/argo-workflows","https://argo-workflows.readthedocs.io",NA,NA,NA,"argo_workflows" diff --git a/book/workflow_frameworks/examples/nextflow/nf-core/environment.yml b/book/workflow_frameworks/examples/nextflow/nf-core/environment.yml new file mode 100644 index 0000000..691d4c7 --- /dev/null +++ b/book/workflow_frameworks/examples/nextflow/nf-core/environment.yml @@ -0,0 +1,5 @@ +channels: + - conda-forge + - bioconda +dependencies: + - bioconda::fastqc=0.12.1 diff --git a/book/workflow_frameworks/examples/nextflow/nf-core/main.nf b/book/workflow_frameworks/examples/nextflow/nf-core/main.nf new file mode 100644 index 0000000..d8989f4 --- /dev/null +++ b/book/workflow_frameworks/examples/nextflow/nf-core/main.nf @@ -0,0 +1,64 @@ +process FASTQC { + tag "$meta.id" + label 'process_medium' + + conda "${moduleDir}/environment.yml" + container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ? + 'https://depot.galaxyproject.org/singularity/fastqc:0.12.1--hdfd78af_0' : + 'biocontainers/fastqc:0.12.1--hdfd78af_0' }" + + input: + tuple val(meta), path(reads) + + output: + tuple val(meta), path("*.html"), emit: html + tuple val(meta), path("*.zip") , emit: zip + path "versions.yml" , emit: versions + + when: + task.ext.when == null || task.ext.when + + script: + def args = task.ext.args ?: '' + def prefix = task.ext.prefix ?: "${meta.id}" + // Make list of old name and new name pairs to use for renaming in the bash while loop + def old_new_pairs = reads instanceof Path || reads.size() == 1 ? [[ reads, "${prefix}.${reads.extension}" ]] : reads.withIndex().collect { entry, index -> [ entry, "${prefix}_${index + 1}.${entry.extension}" ] } + def rename_to = old_new_pairs*.join(' ').join(' ') + def renamed_files = old_new_pairs.collect{ old_name, new_name -> new_name }.join(' ') + + // The total amount of allocated RAM by FastQC is equal to the number of threads defined (--threads) time the amount of RAM defined (--memory) + // https://github.com/s-andrews/FastQC/blob/1faeea0412093224d7f6a07f777fad60a5650795/fastqc#L211-L222 + // Dividing the task.memory by task.cpu allows to stick to requested amount of RAM in the label + def memory_in_mb = MemoryUnit.of("${task.memory}").toUnit('MB') / task.cpus + // FastQC memory value allowed range (100 - 10000) + def fastqc_memory = memory_in_mb > 10000 ? 10000 : (memory_in_mb < 100 ? 100 : memory_in_mb) + + """ + printf "%s %s\\n" $rename_to | while read old_name new_name; do + [ -f "\${new_name}" ] || ln -s \$old_name \$new_name + done + + fastqc \\ + $args \\ + --threads $task.cpus \\ + --memory $fastqc_memory \\ + $renamed_files + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + fastqc: \$( fastqc --version | sed '/FastQC v/!d; s/.*v//' ) + END_VERSIONS + """ + + stub: + def prefix = task.ext.prefix ?: "${meta.id}" + """ + touch ${prefix}.html + touch ${prefix}.zip + + cat <<-END_VERSIONS > versions.yml + "${task.process}": + fastqc: \$( fastqc --version | sed '/FastQC v/!d; s/.*v//' ) + END_VERSIONS + """ +} diff --git a/book/workflow_frameworks/examples/nextflow/nf-core/meta.yml b/book/workflow_frameworks/examples/nextflow/nf-core/meta.yml new file mode 100644 index 0000000..ee5507e --- /dev/null +++ b/book/workflow_frameworks/examples/nextflow/nf-core/meta.yml @@ -0,0 +1,57 @@ +name: fastqc +description: Run FastQC on sequenced reads +keywords: + - quality control + - qc + - adapters + - fastq +tools: + - fastqc: + description: | + FastQC gives general quality metrics about your reads. + It provides information about the quality score distribution + across your reads, the per base sequence content (%A/C/G/T). + You get information about adapter contamination and other + overrepresented sequences. + homepage: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ + documentation: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/Help/ + licence: ["GPL-2.0-only"] +input: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - reads: + type: file + description: | + List of input FastQ files of size 1 and 2 for single-end and paired-end data, + respectively. +output: + - meta: + type: map + description: | + Groovy Map containing sample information + e.g. [ id:'test', single_end:false ] + - html: + type: file + description: FastQC report + pattern: "*_{fastqc.html}" + - zip: + type: file + description: FastQC report archive + pattern: "*_{fastqc.zip}" + - versions: + type: file + description: File containing software versions + pattern: "versions.yml" +authors: + - "@drpatelh" + - "@grst" + - "@ewels" + - "@FelixKrueger" +maintainers: + - "@drpatelh" + - "@grst" + - "@ewels" + - "@FelixKrueger" diff --git a/book/workflow_frameworks/examples/nextflow/nf-core/tests/main.nf.test b/book/workflow_frameworks/examples/nextflow/nf-core/tests/main.nf.test new file mode 100644 index 0000000..f9027b0 --- /dev/null +++ b/book/workflow_frameworks/examples/nextflow/nf-core/tests/main.nf.test @@ -0,0 +1,39 @@ +nextflow_process { + + name "Test Process FASTQC" + script "../main.nf" + process "FASTQC" + + tag "modules" + tag "modules_nfcore" + tag "fastqc" + + test("sarscov2 single-end [fastq]") { + + when { + process { + """ + input[0] = Channel.of([ + [ id: 'test', single_end:true ], + [ file(params.modules_testdata_base_path + 'genomics/sarscov2/illumina/fastq/test_1.fastq.gz', checkIfExists: true) ] + ]) + """ + } + } + + then { + assertAll ( + { assert process.success }, + // NOTE The report contains the date inside it, which means that the md5sum is stable per day, but not longer than that. So you can't md5sum it. + // looks like this:
Mon 2 Oct 2023
test.gz
+ // https://github.com/nf-core/modules/pull/3903#issuecomment-1743620039 + { assert process.out.html[0][1] ==~ ".*/test_fastqc.html" }, + { assert process.out.zip[0][1] ==~ ".*/test_fastqc.zip" }, + { assert path(process.out.html[0][1]).text.contains("File typeConventional base calls") }, + { assert snapshot(process.out.versions).match() } + ) + } + } + + /* The rest of the tests are omitted */ +} diff --git a/book/workflow_frameworks/examples/nextflow/wratten2021_poc/main.nf b/book/workflow_frameworks/examples/nextflow/wratten2021_poc/main.nf new file mode 100644 index 0000000..baa74a1 --- /dev/null +++ b/book/workflow_frameworks/examples/nextflow/wratten2021_poc/main.nf @@ -0,0 +1,14 @@ +process FASTQC { + publishDir params.outdir + + input: + path index + path left + path right + output: + path 'qc' + + """ + mkdir qc && fastqc --quiet '${params.left}' '${params.right}' --outdir qc + """ +} diff --git a/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/environment.yaml b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/environment.yaml new file mode 100644 index 0000000..29c94f5 --- /dev/null +++ b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/environment.yaml @@ -0,0 +1,7 @@ +channels: + - conda-forge + - bioconda + - nodefaults +dependencies: + - fastqc =0.12.1 + - snakemake-wrapper-utils =0.6.2 diff --git a/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/meta.yaml b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/meta.yaml new file mode 100644 index 0000000..b414fe9 --- /dev/null +++ b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/meta.yaml @@ -0,0 +1,11 @@ +name: fastqc +description: | + Generate fastq qc statistics using fastqc. +url: https://github.com/s-andrews/FastQC +authors: + - Julian de Ruiter +input: + - fastq file +output: + - html file containing statistics + - zip file containing statistics diff --git a/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/test/Snakefile b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/test/Snakefile new file mode 100644 index 0000000..6ee68f3 --- /dev/null +++ b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/test/Snakefile @@ -0,0 +1,15 @@ +rule fastqc: + input: + "reads/{sample}.fastq" + output: + html="qc/fastqc/{sample}.html", + zip="qc/fastqc/{sample}_fastqc.zip" # the suffix _fastqc.zip is necessary for multiqc to find the file. If not using multiqc, you are free to choose an arbitrary filename + params: + extra = "--quiet" + log: + "logs/fastqc/{sample}.log" + threads: 1 + resources: + mem_mb = 1024 + wrapper: + "master/bio/fastqc" diff --git a/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/wrapper.py b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/wrapper.py new file mode 100644 index 0000000..39997e9 --- /dev/null +++ b/book/workflow_frameworks/examples/snakemake/snakemake-wrappers/wrapper.py @@ -0,0 +1,64 @@ +"""Snakemake wrapper for fastqc.""" + +__author__ = "Julian de Ruiter" +__copyright__ = "Copyright 2017, Julian de Ruiter" +__email__ = "julianderuiter@gmail.com" +__license__ = "MIT" + + +from os import path +import re +from tempfile import TemporaryDirectory +from snakemake.shell import shell +from snakemake_wrapper_utils.snakemake import get_mem + +extra = snakemake.params.get("extra", "") +log = snakemake.log_fmt_shell(stdout=True, stderr=True) +# Define memory per thread (https://github.com/s-andrews/FastQC/blob/master/fastqc#L201-L222) +mem_mb = int(get_mem(snakemake, "MiB") / snakemake.threads) + + +def basename_without_ext(file_path): + """Returns basename of file path, without the file extension.""" + + base = path.basename(file_path) + # Remove file extension(s) (similar to the internal fastqc approach) + base = re.sub("\\.gz$", "", base) + base = re.sub("\\.bz2$", "", base) + base = re.sub("\\.txt$", "", base) + base = re.sub("\\.fastq$", "", base) + base = re.sub("\\.fq$", "", base) + base = re.sub("\\.sam$", "", base) + base = re.sub("\\.bam$", "", base) + + return base + + +# If you have multiple input files fastqc doesn't know what to do. Taking silently only first gives unapreciated results + +if len(snakemake.input) > 1: + raise IOError("Got multiple input files, I don't know how to process them!") + +# Run fastqc, since there can be race conditions if multiple jobs +# use the same fastqc dir, we create a temp dir. +with TemporaryDirectory() as tempdir: + shell( + "fastqc" + " --threads {snakemake.threads}" + " --memory {mem_mb}" + " {extra}" + " --outdir {tempdir:q}" + " {snakemake.input[0]:q}" + " {log}" + ) + + # Move outputs into proper position. + output_base = basename_without_ext(snakemake.input[0]) + html_path = path.join(tempdir, output_base + "_fastqc.html") + zip_path = path.join(tempdir, output_base + "_fastqc.zip") + + if snakemake.output.html != html_path: + shell("mv {html_path:q} {snakemake.output.html:q}") + + if snakemake.output.zip != zip_path: + shell("mv {zip_path:q} {snakemake.output.zip:q}") diff --git a/book/workflow_frameworks/examples/snakemake/wratten2021_poc/fastqc.smk b/book/workflow_frameworks/examples/snakemake/wratten2021_poc/fastqc.smk new file mode 100644 index 0000000..69369ac --- /dev/null +++ b/book/workflow_frameworks/examples/snakemake/wratten2021_poc/fastqc.smk @@ -0,0 +1,13 @@ +rule fastqc: + input: + get_fastqs, + output: + directory("results/fastqc/{sample}"), + log: + "logs/fastqc/{sample}.log", + conda: + "envs/fastqc.yaml" + params: + "--quiet --outdir", + shell: + "mkdir {output}; fastqc {input} {params} {output} 2> {log}" \ No newline at end of file diff --git a/book/workflow_frameworks/examples/wdl/biowdl/fastqc.wdl b/book/workflow_frameworks/examples/wdl/biowdl/fastqc.wdl new file mode 100644 index 0000000..45095dc --- /dev/null +++ b/book/workflow_frameworks/examples/wdl/biowdl/fastqc.wdl @@ -0,0 +1,81 @@ +version 1.0 + +# ... license ... + +task Fastqc { + input { + File seqFile + String outdirPath + Boolean casava = false + ## ... other arguments ... + + # Set javaXmx a little high. + String javaXmx="1750M" + Int threads = 1 + String memory = "2GiB" + Int timeMinutes = 1 + ceil(size(seqFile, "G")) * 4 + String dockerImage = "quay.io/biocontainers/fastqc:0.12.1--hdfd78af_0" + + Array[File]? noneArray + File? noneFile + } + + # Chops of the .gz extension if present. + String name = basename(sub(seqFile, "\.gz$","")) + # This regex chops of the extension just as fastqc does it. + String reportDir = outdirPath + "/" + sub(name, "\.[^\.]*$", "_fastqc") + + # We reimplement the perl wrapper here. This has the advantage that it + # gives us more control over the amount of memory used. + command <<< + set -e + mkdir -p "~{outdirPath}" + FASTQC_DIR="/usr/local/opt/fastqc-0.12.1" + export CLASSPATH="$FASTQC_DIR:$FASTQC_DIR/sam-1.103.jar:$FASTQC_DIR/jbzip2-0.9.jar:$FASTQC_DIR/cisd-jhdf5.jar" + java -Djava.awt.headless=true -XX:ParallelGCThreads=1 \ + -Xms200M -Xmx~{javaXmx} \ + ~{"-Dfastqc.output_dir=" + outdirPath} \ + ~{true="-Dfastqc.casava=true" false="" casava} \ + # ... other arguments ... + ~{"-Dfastqc.kmer_size=" + kmers} \ + ~{"-Djava.io.tmpdir=" + dir} \ + uk.ac.babraham.FastQC.FastQCApplication \ + ~{seqFile} + >>> + + output { + File htmlReport = reportDir + ".html" + File reportZip = reportDir + ".zip" + File? summary = if extract then reportDir + "/summary.txt" else noneFile + File? rawReport = if extract then reportDir + "/fastqc_data.txt" else noneFile + Array[File]? images = if extract then glob(reportDir + "/Images/*.png") else noneArray + } + + runtime { + cpu: threads + memory: memory + time_minutes: timeMinutes + docker: dockerImage + } + + parameter_meta { + # inputs + seqFile: {description: "A fastq file.", category: "required"} + outdirPath: {description: "The path to write the output to.", catgory: "required"} + # ... other arguments ... + dockerImage: {description: "The docker image used for this task. Changing this may result in errors which the developers may choose not to address.", category: "advanced"} + + # outputs + htmlReport: {description: "HTML report file."} + reportZip: {description: "Source data file."} + summary: {description: "Summary file."} + rawReport: {description: "Raw report file."} + images: {description: "Images in report file."} + } + + meta { + WDL_AID: { + exclude: ["noneFile", "noneArray"] + } + } +} diff --git a/book/workflow_frameworks/examples/wdl/biowdl/fastqc_full.wdl b/book/workflow_frameworks/examples/wdl/biowdl/fastqc_full.wdl new file mode 100644 index 0000000..8e1ad5c --- /dev/null +++ b/book/workflow_frameworks/examples/wdl/biowdl/fastqc_full.wdl @@ -0,0 +1,142 @@ +version 1.0 + +# Copyright (c) 2017 Leiden University Medical Center +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in +# all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +task Fastqc { + input { + File seqFile + String outdirPath + Boolean casava = false + Boolean nano = false + Boolean noFilter = false + Boolean extract = false + Boolean nogroup = false + + Int? minLength + String? format + File? contaminants + File? adapters + File? limits + Int? kmers + String? dir + + # Set javaXmx a little high. Equal to fastqc default with 7 threads. + # This is because some fastq files need more memory. 2G per core + # is a nice cluster default, so we use all the rest of the memory for + # fastqc so we should have as little OOM crashes as possible even with + # weird edge case fastq's. + String javaXmx="1750M" + Int threads = 1 + String memory = "2GiB" + Int timeMinutes = 1 + ceil(size(seqFile, "G")) * 4 + String dockerImage = "quay.io/biocontainers/fastqc:0.12.1--hdfd78af_0" + + Array[File]? noneArray + File? noneFile + } + + # Chops of the .gz extension if present. + # The Basename needs to be taken here. Otherwise paths might differ + # between similar jobs. + String name = basename(sub(seqFile, "\.gz$","")) + # This regex chops of the extension and replaces it with _fastqc for + # the reportdir. + # Just as fastqc does it. + String reportDir = outdirPath + "/" + sub(name, "\.[^\.]*$", "_fastqc") + + # We reimplement the perl wrapper here. This has the advantage that it + # gives us more control over the amount of memory used. + command <<< + set -e + mkdir -p "~{outdirPath}" + FASTQC_DIR="/usr/local/opt/fastqc-0.12.1" + export CLASSPATH="$FASTQC_DIR:$FASTQC_DIR/sam-1.103.jar:$FASTQC_DIR/jbzip2-0.9.jar:$FASTQC_DIR/cisd-jhdf5.jar" + java -Djava.awt.headless=true -XX:ParallelGCThreads=1 \ + -Xms200M -Xmx~{javaXmx} \ + ~{"-Dfastqc.output_dir=" + outdirPath} \ + ~{true="-Dfastqc.casava=true" false="" casava} \ + ~{true="-Dfastqc.nano=true" false="" nano} \ + ~{true="-Dfastqc.nofilter=true" false="" noFilter} \ + ~{true="-Dfastqc.unzip=true" false="" extract} \ + ~{true="-Dfastqc.nogroup=true" false="" nogroup} \ + ~{"-Dfastqc.min_length=" + minLength} \ + ~{"-Dfastqc.sequence_format=" + format} \ + ~{"-Dfastqc.threads=" + threads} \ + ~{"-Dfastqc.contaminant_file=" + contaminants} \ + ~{"-Dfastqc.adapter_file=" + adapters} \ + ~{"-Dfastqc.limits_file=" + limits} \ + ~{"-Dfastqc.kmer_size=" + kmers} \ + ~{"-Djava.io.tmpdir=" + dir} \ + uk.ac.babraham.FastQC.FastQCApplication \ + ~{seqFile} + >>> + + output { + File htmlReport = reportDir + ".html" + File reportZip = reportDir + ".zip" + File? summary = if extract then reportDir + "/summary.txt" else noneFile + File? rawReport = if extract then reportDir + "/fastqc_data.txt" else noneFile + Array[File]? images = if extract then glob(reportDir + "/Images/*.png") else noneArray + } + + runtime { + cpu: threads + memory: memory + time_minutes: timeMinutes + docker: dockerImage + } + + parameter_meta { + # inputs + seqFile: {description: "A fastq file.", category: "required"} + outdirPath: {description: "The path to write the output to.", catgory: "required"} + casava: {description: "Equivalent to fastqc's --casava flag.", category: "advanced"} + nano: {description: "Equivalent to fastqc's --nano flag.", category: "advanced"} + noFilter: {description: "Equivalent to fastqc's --nofilter flag.", category: "advanced"} + extract: {description: "Equivalent to fastqc's --extract flag.", category: "advanced"} + nogroup: {description: "Equivalent to fastqc's --nogroup flag.", category: "advanced"} + minLength: {description: "Equivalent to fastqc's --min_length option.", category: "advanced"} + format: {description: "Equivalent to fastqc's --format option.", category: "advanced"} + contaminants: {description: "Equivalent to fastqc's --contaminants option.", category: "advanced"} + adapters: {description: "Equivalent to fastqc's --adapters option.", category: "advanced"} + limits: {description: "Equivalent to fastqc's --limits option.", category: "advanced"} + kmers: {description: "Equivalent to fastqc's --kmers option.", category: "advanced"} + dir: {description: "Equivalent to fastqc's --dir option.", category: "advanced"} + javaXmx: {description: "The maximum memory available to the program. Should be lower than `memory` to accommodate JVM overhead.", category: "advanced"} + threads: {description: "The number of cores to use.", category: "advanced"} + memory: {description: "The amount of memory this job will use.", category: "advanced"} + timeMinutes: {description: "The maximum amount of time the job will run in minutes.", category: "advanced"} + dockerImage: {description: "The docker image used for this task. Changing this may result in errors which the developers may choose not to address.", category: "advanced"} + + # outputs + htmlReport: {description: "HTML report file."} + reportZip: {description: "Source data file."} + summary: {description: "Summary file."} + rawReport: {description: "Raw report file."} + images: {description: "Images in report file."} + } + + meta { + WDL_AID: { + exclude: ["noneFile", "noneArray"] + } + } +} diff --git a/book/workflow_frameworks/examples/wdl/wratten2021_poc/fastqc.wdl b/book/workflow_frameworks/examples/wdl/wratten2021_poc/fastqc.wdl new file mode 100644 index 0000000..c1cc6a1 --- /dev/null +++ b/book/workflow_frameworks/examples/wdl/wratten2021_poc/fastqc.wdl @@ -0,0 +1,17 @@ +task FastQCone { + input { + File reads + } + + command { + zcat "${reads}" | fastqc stdin:readsone + } + + output { + File fastqc_res = "readsone_fastqc.html" + } + + runtime { + docker: 'pegi3s/fastqc' + } +} diff --git a/notebooks/workflows/fig_productionization.svg b/book/workflow_frameworks/images/productionization.svg similarity index 100% rename from notebooks/workflows/fig_productionization.svg rename to book/workflow_frameworks/images/productionization.svg diff --git a/notebooks/workflows/fig_qualities.svg b/book/workflow_frameworks/images/qualities.svg similarity index 93% rename from notebooks/workflows/fig_qualities.svg rename to book/workflow_frameworks/images/qualities.svg index c828daf..a51556b 100644 --- a/notebooks/workflows/fig_qualities.svg +++ b/book/workflow_frameworks/images/qualities.svg @@ -8,7 +8,7 @@ version="1.1" id="svg1" inkscape:version="1.3.2 (091e20ef0f, 2023-11-25)" - sodipodi:docname="fig_qualities.svg" + sodipodi:docname="qualities.svg" xml:space="preserve" xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape" xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd" @@ -24,9 +24,9 @@ inkscape:pagecheckerboard="0" inkscape:deskcolor="#d1d1d1" inkscape:document-units="mm" - inkscape:zoom="1.9345809" - inkscape:cx="186.34527" - inkscape:cy="172.38876" + inkscape:zoom="2.7552996" + inkscape:cx="229.0132" + inkscape:cy="138.46044" inkscape:window-width="3840" inkscape:window-height="2091" inkscape:window-x="0" @@ -455,7 +455,7 @@ id="tspan50-3" style="text-align:center;text-anchor:middle;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1" x="101.94839" - y="70.253143">AutomatedCommunity + transform="translate(11.869203,5.0878816)"> diff --git a/book/workflow_frameworks/images/technologies.svg b/book/workflow_frameworks/images/technologies.svg new file mode 100644 index 0000000..11678ca --- /dev/null +++ b/book/workflow_frameworks/images/technologies.svg @@ -0,0 +1,2536 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + PolyglotMaintainable + +PortableCommunityScalableComputeenvironmentsAWS BatchGoogle CloudPBS/Torque...Azure BatchKubernetesSLURMStorage solutionsAWS S3Azure FilesGoogle Storage...ModularInteroperablefile formatsHDF5ArrowTileDBJSONZarrParquetXMLYAML...File formatsAgreed-uponinterfacesFile schemasReproducibleContainerisationShifterDockerPodmanApptainerSarus...Automated referencedocumentationBest practicesOSS componentsOSS workflowsUnit testingVersioned releasesContinuous Integration...PolyglotModularScalableReproduciblePortableMaintainableCommunity + +Interoperablefile formatsHDF5ArrowTileDBJSONZarrParquetXMLYAMLComputeenvironmentsAWS BatchGoogle CloudPBS/Torque...Azure BatchKubernetesSLURMContainerisationShifterDockerPodmanApptainerSarusStoragesolutionsAWS S3Azure FilesGoogle Storage.........Automated referencedocumentationBest practicesOSS componentsOSS workflowsUnit testsVersioned releasesContinuous IntegrationFile I/OSchemasData provenance...Dataprovenance diff --git a/book/workflow_frameworks/index.qmd b/book/workflow_frameworks/index.qmd new file mode 100644 index 0000000..ecaa6a8 --- /dev/null +++ b/book/workflow_frameworks/index.qmd @@ -0,0 +1,480 @@ +--- +title: Workflows +engine: knitr +--- + +In the previous chapters, we've explored strategies for supporting data operability across programming language. Now, we turn our attention to how to effectively integrate these tools and languages into a cohesive and scalable analysis workflow. + +## Productionization + +Productionization is the process of transforming research-oriented analysis pipelines into robust, scalable, and maintainable workflows that can be reliably executed in a production environment ([@fig-productionization]). This transition is essential for ensuring the reproducibility of results, facilitating collaboration among researchers, and enabling the efficient processing of large and complex single-cell datasets. + +![An example of the productionization process for single-cell analysis workflows. **A)** The research environment is characterized by scattered data, manual steps, and ad-hoc analysis pipelines. **B)** The production environment is streamlined, automated, and standardized, with reproducibility engines in place.](images/productionization.svg){#fig-productionization} + +### But how to ensure that your workflow is production-ready? {.unnumbered} + +In this chapter, we will explore: + +* **Key qualities** of workflows built to stand the test of time +* Which **technologies and workflow frameworks** contribute to these qualities +* **Best practices** to keep in mind during development + + +## Review of Workflow Frameworks + +[A lot of different workflow frameworks exist](https://github.com/pditommaso/awesome-pipeline), and there are a lot of factors to consider when choosing the right one for your project. +@Wratten2021 conducted a review of popular workflow managers for bioinformatics, evaluating them based on several key aspects, including ease of use, expressiveness, portability, scalability, and learning resources ([@tbl-strengths]). + + +| Tool | Class | Ease of use | Expressiveness | Portability | Scalability | Learning resources | Pipeline initiatives | +| ---- | ----- | ----------- | -------------- | ----------- | ----------- | ------------------ | -------------------- | +| Galaxy | Graphical | ●●● | ●○○ | ●●● | ●●● | ●●● | ●●○ | +| KNIME | Graphical | ●●● | ●○○ | ○○○ | ●●◐ | ●●● | ●●○ | +| Nextflow | DSL | ●●○ | ●●● | ●●● | ●●● | ●●● | ●●● | +| Snakemake | DSL | ●●○ | ●●● | ●●◐ | ●●● | ●●○ | ●●● | +| GenPipes | DSL | ●●○ | ●●● | ●●○ | ●●○ | ●●○ | ●●○ | +| bPipe | DSL | ●●○ | ●●● | ●●○ | ●●◐ | ●●○ | ●○○ | +| Pachyderm | DSL | ●●○ | ●●● | ●○○ | ●●○ | ●●● | ○○○ | +| SciPipe | Library | ●●○ | ●●● | ○○○ | ○○○ | ●●○ | ○○○ | +| Luigi | Library | ●●○ | ●●● | ●○○ | ●●◐ | ●●○ | ○○○ | +| Cromwell + WDL | Execution + workflow specification | ●○○ | ●●○ | ●●● | ●●◐ | ●●○ | ●●○ | +| cwltool + CWL | Execution + workflow specification | ●○○ | ●●○ | ●●◐ | ○○○ | ●●● | ●●○ | +| Toil + CWL/WDL/Python | Execution + workflow specification | ●○○ | ●●● | ●◐○ | ●●● | ●●○ | ●●○ | + +: Overview of workflow managers for bioinformatics [@Wratten2021]. {#tbl-strengths} + +Even more interesting is the accompanying GitHub repository ([GoekeLab/bioinformatics-workflows](https://github.com/GoekeLab/bioinformatics-workflows)), which contains a **Proof of Concept (PoC) RNA-seq workflow** implemented in the different workflow frameworks. These implementations were contributed and reviewed by the **developers of the respective frameworks** themselves! + +![Wow! ;)](https://media2.giphy.com/media/v1.Y2lkPTc5MGI3NjExcDZ0bmhuNmx1c3Y4dmUzNDUwZTdrM3dwdHk3MXVjcXVtZnlrYzQ2cSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/J87jeioCWcipG/giphy.webp){} + +Looking at these implementations, at first glance, one would think that the differences between the frameworks are minimal, and that the choice of framework is mostly a matter of personal preference. + +### Comparing PoC Workflows to Community-Made Modules + +However, comparing the POC workflows (_left_) to community-made modules (_right_), it becomes clear that **creating production-ready components** requires a lot more than **specifying a command's input and output files**. + + +:::{.column-page} + +#### Nextflow + +:::{.grid} + +:::{.g-col-5} + +Wratten et al. 2021 PoC ([Source](https://github.com/GoekeLab/bioinformatics-workflows/tree/master/nextflow)): + +:::{.panel-tabset} + +## `main.nf` + +```{embed lang="groovy"} +examples/nextflow/wratten2021_poc/main.nf +``` + +::: + +::: + +:::{.g-col-7} + +nf-core ([Source](https://github.com/nf-core/modules/tree/master/modules/nf-core/fastqc)): + +:::{.panel-tabset} + +## `environment.yml` + +```{embed lang="yaml"} +examples/nextflow/nf-core/environment.yml +``` + +## `main.nf` + +```{embed lang="groovy"} +examples/nextflow/nf-core/main.nf +``` + +## `meta.yaml` + +```{embed lang="yaml"} +examples/nextflow/nf-core/meta.yml +``` + +## `tests/main.nf.test` + +```{embed lang="groovy"} +examples/nextflow/nf-core/tests/main.nf.test +``` + +::: + +::: + +::: + +::: + + + +:::{.column-page} + +#### Snakemake + +:::{.grid} + +:::{.g-col-5} + +Wratten et al. 2021 PoC ([Source](https://github.com/GoekeLab/bioinformatics-workflows/tree/master/snakemake)): + +:::{.panel-tabset} + +## `fastqc.smk` + +```{embed lang="python"} +examples/snakemake/wratten2021_poc/fastqc.smk +``` + +::: + +::: + +:::{.g-col-7} + +snakemake-wrappers ([Source](https://github.com/snakemake/snakemake-wrappers/tree/master/bio/fastqc)): + +:::{.panel-tabset} + +## `environment.yaml` + +```{embed lang="yaml"} +examples/snakemake/snakemake-wrappers/environment.yaml +``` + +## `meta.yaml` + +```{embed lang="yaml"} +examples/snakemake/snakemake-wrappers/meta.yaml +``` + +## `wrapper.py` + +```{embed lang="python"} +examples/snakemake/snakemake-wrappers/wrapper.py +``` + +## `test/Snakefile` + +```{embed lang="python"} +examples/snakemake/snakemake-wrappers/test/Snakefile +``` + +::: + +::: + +::: + +::: + +:::{.column-page} + +#### Toil + WDL + +:::{.grid} + +:::{.g-col-5} + +Wratten et al. 2021 PoC ([Source](https://github.com/GoekeLab/bioinformatics-workflows/tree/master/wdl)): + +:::{.panel-tabset} + +## `fastqc.wdl` + +```{embed lang="bash"} +examples/wdl/wratten2021_poc/fastqc.wdl +``` + +::: + +::: + +:::{.g-col-7} + +BioWDL ([Source](https://github.com/biowdl/tasks/blob/develop/fastqc.wdl)): + +:::{.panel-tabset} + +## `fastqc.wdl` (Excerpt) + +```{embed lang="bash"} +examples/wdl/biowdl/fastqc.wdl +``` + +## `fastqc.wdl` (Full) + +```{embed lang="bash"} +examples/wdl/biowdl/fastqc_full.wdl +``` + +::: + +::: + +::: + +::: + + +### Limitations of the study + +However, the [Supplementary Table](https://static-content.springer.com/esm/art%3A10.1038%2Fs41592-021-01254-9/MediaObjects/41592_2021_1254_MOESM2_ESM.xlsx) shows that the comparison in [@tbl-strengths] was rather limited, since the score of each category was only based on a single criterion. Of the following categories, only "Scalability" was determined by more than one criterion: + +* **Ease of Use**: Graphical interface with execution environment (score of 3), programming interface with in-built execution environment (score of 2), separated development and execution environment (score of 1). +* **Expressiveness**: Based on an existing programming language (3) or a new language or restricted vocabulary (2), primary interaction with graphical user interface (1). +* **Portability**: Integration with three or more container and package manager platforms (3), two platforms are supported (2), one platform is supported (1). +* **Scalability**: Considers cloud support, scheduler and orchestration tool integration, and executor support. Please refer to Supplementary Table 1 - Sheet 2 (Scalability). +* **Learning resources**: Official tutorials, forums and events (3), tutorials and forums (2), tutorials or forums (1). +* **Pipelines Initiatives**: Community and curated (3), community or curated (2), not community or curated (1). + +By comparing the example code of the respective workflow frameworks, it also becomes clear that we need not only look at example code of POC workflows, but actual production-ready workflows and pipelines. Such code often require a lot more functionality, including: + +* Error handling +* Logging +* Data provenance +* Parameterization +* Testing +* Documentation +* Containerization +* Resource management + + +## Qualities of a Production-Ready Workflow + +Building production-ready workflows for single-cell analysis involves integrating a variety of tools, technologies, and best practices. In order to meet the demands of large-scale data processing, reproducibility, and collaboration, a production-ready workflow should exhibit the following essential qualities ([@fig-qualities]): + +![Essential qualities of a production-ready workflow.](images/qualities.svg){#fig-qualities} + +* **Polyglot**: Seamlessly integrate tools and libraries from different programming languages, allowing you to leverage the strengths of each language for specific tasks. This facilitates the use of specialized tools and optimizes the analysis pipeline for performance and efficiency. +* **Modular**: A well-structured workflow should be composed of modular and reusable components, promoting code maintainability and facilitating collaboration. Each module should have a clear purpose and well-defined inputs and outputs, enabling easy integration and replacement of individual steps within the pipeline. +* **Scalable**: Single-cell datasets can be massive, and a production-ready workflow should be able to handle large volumes of data efficiently. This involves utilizing scalable compute environments, optimizing data storage and retrieval, and implementing parallelization strategies to accelerate analysis. +* **Reproducible**: Ensuring reproducibility is crucial for scientific rigor and validation. A production-ready workflow should capture all the necessary information, including code, data, parameters, and software environments, to enable others to replicate the analysis and obtain consistent results. +* **Portable**: The workflow should be designed to run seamlessly across different computing platforms and environments, promoting accessibility and collaboration. Containerization technologies like Docker can help achieve portability by encapsulating the workflow and its dependencies into a self-contained unit. +* **Community**: Leveraging community resources, tools, and best practices can accelerate the development of production-ready workflows. This is because developing high-quality components can at times be time-consuming, and sharing resources can help reduce duplication of effort and promote collaboration. +* **Maintainable**: A production-ready workflow should be well-documented, organized, and easy to understand, facilitating updates, modifications, and troubleshooting. Clear documentation of code, data, and parameters ensures that the workflow remains accessible and usable over time. + +## Technologies for Production-Ready Workflows + +The essential qualities of a production-ready workflow are achieved through a combination of enabling technologies ([@fig-technologies]). These technologies provide the foundation for building scalable, reproducible, and maintainable workflows for single-cell analysis. + +![The essential qualities of a production-ready workflow are achieved through a combination of enabling technologies.](images/technologies.svg){#fig-technologies} + + +## Quality Assessment of Workflow Frameworks + +```{r tibble, echo=FALSE, eval=FALSE} +library(tibble) +library(dplyr) +library(tidyr) +library(purrr) + +googlesheets4::gs4_auth(email = "robrecht@data-intuitive.com") +wf_qc_sheet <- "https://docs.google.com/spreadsheets/d/1OMsnm8bIWc_Hp5neBOj7hWURT7ZL-L6qSbr0oAzmvSI" +wf_qc <- googlesheets4::read_sheet(wf_qc_sheet, sheet = "qc") |> + mutate_each(function(x) { + map_chr(x, function(y) { + if (is.null(y)) { + NA_character_ + } else { + y + } + }) + }) + +wf_qc2 <- wf_qc + +# fill in NAs due to merged cells +for (i in seq(2, nrow(wf_qc2), by = 1)) { + if (is.na(wf_qc2$name[[i]])) { + wf_qc2$name[[i]] <- wf_qc2$name[[i - 1]] + } + if (is.na(wf_qc$aspect[[i]])) { + wf_qc2$aspect[[i]] <- wf_qc2$aspect[[i - 1]] + } +} + +# remove incomplete frameworks and unused questions +wf_qc2 <- wf_qc2 |> + select(name, aspect, item, Nextflow, Snakemake, Galaxy, `Viash + Nextflow`, `Argo Workflows`) %>% + filter(!is.na(Nextflow)) %>% + filter(item != "The framework provides clear error messages and debugging tools.") + +# sanitize string +string_to_colname <- function(x) { + x <- tolower(x) + x <- gsub(" ", "_", x) + x <- gsub("[^[:alnum:]_]", "", x) + x <- gsub("__", "_", x) + x +} + +# process metadata +meta <- wf_qc2 |> + filter(name == "Metadata") |> + select(-name, -aspect) |> + as.matrix() |> + t() |> + as.data.frame() %>% + rownames_to_column("project") +colnames(meta) <- string_to_colname(unname(unlist(meta[1,]))) +meta <- meta[-1,] +rownames(meta) <- NULL +meta <- meta |> + rename(project = item) |> + mutate(id = string_to_colname(project)) + +# process criteria +criteria <- wf_qc2 |> + filter(name != "Metadata") |> + select(name, aspect, item) + +# process scores +scores <- wf_qc2 |> + filter(name != "Metadata") |> + gather(key = "framework", value = "data", -item, -name, -aspect) %>% + mutate( + score = gsub("^([0-9\\.]*).*", "\\1", data) |> as.numeric(), + url = gsub(".*(http[^ ]*)$", "\\1", data) %>% ifelse(. == data, NA_character_, .), + explanation = gsub("^[0-9\\.]* ", "", data) %>% gsub("http[^ ]*$", "", .) + ) + +agg <- scores |> + group_by(name, framework) |> + summarize(mean_score = mean(score), .groups = "drop") + +# write to file +write.csv(meta, "notebooks/workflows/data/wf_metadata.csv", row.names = FALSE) +write.csv(criteria, "notebooks/workflows/data/criteria.csv", row.names = FALSE) +write.csv(agg, "notebooks/workflows/data/wf_aggregated_scores.csv", row.names = FALSE) +``` + +Given the abovementioned limitations, we decided to conduct our own quality assessment of workflow frameworks. This assessment is still largely in the works, but we're happy to share the preliminary results with you. + +The data is based on a review of the documentation and community resources for each framework. We evaluated the frameworks based on the list of essential qualities mentioned in the previous section ([@fig-qualities]). + +### Included frameworks + +The following workflow frameworks were included in the assessment: + +```{r wf_metadata, echo=FALSE, results="asis"} +meta <- read.csv("data/wf_metadata.csv") + +for (i in seq_len(nrow(meta))) { + project <- meta$project[[i]] + project_description <- meta$project_description[[i]] + project_website <- meta$project_website[[i]] + project_repository <- meta$project_repository[[i]] + documentation <- meta$documentation[[i]] + doi <- meta$doi[[i]] + community <- meta$community[[i]] + community_repository <- meta$community_repository[[i]] + + strs <- c( + paste0("[", project, "](", project_website, "): ", + gsub("\\. *$", "", project_description), ".") + ) + + if (!is.na(project_repository)) { + strs <- c(strs, paste0("[{{< fa brands github >}}](", project_repository, ")")) + } + + if (!is.na(documentation)) { + strs <- c(strs, paste0("[{{< fa circle-info >}}](", documentation, ")")) + } + + if (!is.na(doi)) { + strs <- c(strs, paste0("[{{< fa book-open >}}](", doi, ")")) + } + + if (!is.na(community)) { + strs <- c(strs, paste0("[{{< fa users >}}](", community, ")")) + } + + cat(paste0( + "- ", paste(strs, collapse = " "), "\n" + )) +} +``` + +### Quality Assessment Criteria + +The quality assessment was based on the following criteria: + +:::{.panel-tabset} + +```{r criteria, echo=FALSE, results="asis"} +criteria <- read.csv("data/criteria.csv") + +unique_names <- unique(criteria$name) + +for (name in sort(unique(criteria$name))) { + crit <- criteria[criteria$name == name, , drop = FALSE] + + cat(paste0("#### ", name, " {.unnumbered}\n\n")) + + for (aspect in sort(unique(crit$aspect))) { + asp <- crit[crit$aspect == aspect, , drop = FALSE] + cat(paste0("**", aspect, "**\n\n")) + + for (item in asp$item) { + cat(paste0(" * ", item, "\n")) + } + + cat("\n\n") + } +} +``` + +::: + +These criteria and subsequent scores will be further refined and validated as part of our ongoing research. + +### Quality Scores + +The aggregated quality scores for each framework are shown below. The scores are based on the evaluation of the essential qualities of a production-ready workflow. + +```{r plot_scores, echo=FALSE} +#| fig-cap: Quality scores for different workflow frameworks. +#| fig-format: svg +library(ggplot2) + +agg <- read.csv("data/wf_aggregated_scores.csv") +agg$framework <- factor(agg$framework, levels = sort(unique(agg$framework), decreasing = TRUE)) + +ggplot(agg) + + geom_point(aes(mean_score, framework, color = framework)) + + geom_segment(aes(x = 0, xend = mean_score, y = framework, yend = framework, color = framework), linewidth = 1) + + facet_wrap(~name, ncol = 2) + + theme_minimal() + + theme( + panel.grid.minor = element_blank(), + panel.grid.major.y = element_blank(), + legend.position = "none" + ) + + labs( + x = NULL, + y = NULL + ) + + scale_color_brewer(palette = "Set1") +``` + +Raw scores and detailed explanations behind the reasoning of the resulting scores can be found in the [Workflow Quality Assessment Spreadsheet](https://docs.google.com/spreadsheets/d/1OMsnm8bIWc_Hp5neBOj7hWURT7ZL-L6qSbr0oAzmvSI). + +### Quality assessment contributors + +* Jakub Majerčík +* Michaela Müller +* Robrecht Cannoodt +* Toni Verbeiren + +## Conclusion + diff --git a/notebooks/.gitignore b/notebooks/.gitignore deleted file mode 100644 index 52465d6..0000000 --- a/notebooks/.gitignore +++ /dev/null @@ -1 +0,0 @@ -usecase_data \ No newline at end of file diff --git a/notebooks/file_formats.qmd b/notebooks/file_formats.qmd deleted file mode 100644 index 7fdd7ef..0000000 --- a/notebooks/file_formats.qmd +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: File formats ---- - -# File formats - -Data format based interoperability - -1. h5ad / zarr / Apache Arrow -2. Reading and writing these formats - -## Setup - -```{python} -import anndata -import numpy -import scanpy -``` - -```{python} -anndata.__version__ -``` diff --git a/notebooks/workflows/fig_overview_workflow.webp b/notebooks/workflows/fig_overview_workflow.webp deleted file mode 100644 index 2c09cc0..0000000 Binary files a/notebooks/workflows/fig_overview_workflow.webp and /dev/null differ diff --git a/notebooks/workflows/index.qmd b/notebooks/workflows/index.qmd deleted file mode 100644 index 3ee6120..0000000 --- a/notebooks/workflows/index.qmd +++ /dev/null @@ -1,84 +0,0 @@ ---- -title: Workflows -author: Robrecht Cannoodt ---- - -Single-cell analysis has revolutionized our understanding of cellular heterogeneity and complex biological processes. However, this cutting-edge field often demands the use of multiple programming languages and frameworks, each with its strengths and specialized tools [@Heumos2023]. This polyglot approach, while powerful, introduces significant technical challenges in terms of interoperability, usability, and reproducibility. - -In the previous chapters, we've explored strategies for supporting data operability across programming language. Now, we turn our attention to how to effectively integrate these tools and languages into a cohesive and scalable analysis workflow. - -**Productionization** is the process of transforming research-oriented analysis pipelines into robust, scalable, and maintainable workflows that can be reliably executed in a production environment ([@fig-productionization]). This transition is essential for ensuring the reproducibility of results, facilitating collaboration among researchers, and enabling the efficient processing of large and complex single-cell datasets. - -![An example of the productionization process for single-cell analysis workflows. **A)** The research environment is characterized by scattered data, manual steps, and ad-hoc analysis pipelines. **B)** The production environment is streamlined, automated, and standardized, with reproducibility engines in place.](fig_productionization.svg){#fig-productionization} - -In this chapter, we'll delve into the key components and considerations involved in building production-ready multi-language single-cell analysis workflows. We'll explore essential elements such as data storage, compute environments, containerization, workflow management systems, and best practices for reproducibility. By the end of this chapter, you'll have an understanding of the tools and strategies needed to create robust and scalable workflows for single-cell analysis (or any other data-intensive domain). - - -## Qualities of a Production-Ready Workflow - -Building production-ready workflows for single-cell analysis involves integrating a variety of tools, technologies, and best practices. In order to meet the demands of large-scale data processing, reproducibility, and collaboration, a production-ready workflow should exhibit the following essential qualities ([@fig-qualities]): - -![Essential qualities of a production-ready workflow.](fig_qualities.svg){#fig-qualities} - -* **Polyglot**: Seamlessly integrate tools and libraries from different programming languages, allowing you to leverage the strengths of each language for specific tasks. This facilitates the use of specialized tools and optimizes the analysis pipeline for performance and efficiency. -* **Modular**: A well-structured workflow should be composed of modular and reusable components, promoting code maintainability and facilitating collaboration. Each module should have a clear purpose and well-defined inputs and outputs, enabling easy integration and replacement of individual steps within the pipeline. -* **Scalable**: Single-cell datasets can be massive, and a production-ready workflow should be able to handle large volumes of data efficiently. This involves utilizing scalable compute environments, optimizing data storage and retrieval, and implementing parallelization strategies to accelerate analysis. -* **Reproducible**: Ensuring reproducibility is crucial for scientific rigor and validation. A production-ready workflow should capture all the necessary information, including code, data, parameters, and software environments, to enable others to replicate the analysis and obtain consistent results. -* **Portable**: The workflow should be designed to run seamlessly across different computing platforms and environments, promoting accessibility and collaboration. Containerization technologies like Docker can help achieve portability by encapsulating the workflow and its dependencies into a self-contained unit. -* **Automated**: Automating routine tasks and data processing steps reduces manual intervention, minimizes errors, and improves efficiency. Workflow management systems can automate the execution of complex pipelines, handling dependencies, parallelization, and error recovery. -* **Maintainable**: A production-ready workflow should be well-documented, organized, and easy to understand, facilitating updates, modifications, and troubleshooting. Clear documentation of code, data, and parameters ensures that the workflow remains accessible and usable over time. - - - -## Key components - - -![Overview of bioinformatics analysis workflows using an example of transcript expression quantifications [@Wratten2021].](fig_overview_workflow.webp){.lightbox} - - - - -## Data Storage - -## Compute Environments - -## Containerization - -## Workflow Management Systems - - - -| Tool | Class | Ease of use | Expressiveness | Portability | Scalability | Learning resources | Pipeline initiatives | -| ---- | ----- | ----------- | -------------- | ----------- | ----------- | ------------------ | -------------------- | -| Galaxy | Graphical | ●●● | ●○○ | ●●● | ●●● | ●●● | ●●○ | -| KNIME | Graphical | ●●● | ●○○ | ○○○ | ●●◐ | ●●● | ●●○ | -| Nextflow | DSL | ●●○ | ●●● | ●●● | ●●● | ●●● | ●●● | -| Snakemake | DSL | ●●○ | ●●● | ●●◐ | ●●● | ●●○ | ●●● | -| GenPipes | DSL | ●●○ | ●●● | ●●○ | ●●○ | ●●○ | ●●○ | -| bPipe | DSL | ●●○ | ●●● | ●●○ | ●●◐ | ●●○ | ●○○ | -| Pachyderm | DSL | ●●○ | ●●● | ●○○ | ●●○ | ●●● | ○○○ | -| SciPipe | Library | ●●○ | ●●● | ○○○ | ○○○ | ●●○ | ○○○ | -| Luigi | Library | ●●○ | ●●● | ●○○ | ●●◐ | ●●○ | ○○○ | -| Cromwell + WDL | Execution + workflow specification | ●○○ | ●●○ | ●●● | ●●◐ | ●●○ | ●●○ | -| cwltool + CWL | Execution + workflow specification | ●○○ | ●●○ | ●●◐ | ○○○ | ●●● | ●●○ | -| Toil + CWL/WDL/Python | Execution + workflow specification | ●○○ | ●●● | ●◐○ | ●●● | ●●○ | ●●○ | - -: Overview of workflow managers for bioinformatics [@Wratten2021]. {#tbl-strengths} - - - - -| Tool(s) | Dialect | Ease of use | Expressiveness | Modularity | Separation of concerns | Learning resources | Pipeline initiatives | -| --- | --- | --- | --- | --- | --- | --- | --- | -| Nextflow | DSL1 | ●○○ | ●●◐ | ●○○ | ●○○ | ●●● | ●○○ | -| Nextflow | DSL2 | ●●○ | ●●● | ●◐○ | ●◐○ | ●●○ | ●●○ | -| Nextflow + Groovy | nf-core | ●●○ | ●●● | ●●○ | ●◐○ | ●○○ | ●●● | -| Viash + Nextflow | VDSL3 | ●●● | ●●● | ●●● | ●●● | ●○○[^1] | ●●○ | - -: Different approaches to Nextflow programming. Not shown: Portability: ●●●, Scalability: ●●●. {#tbl-dialects} - - - -## Best Practices - -## Conclusion