├── .Rbuildignore ├── .github ├── .gitignore └── workflows │ └── pkgdown.yaml ├── .gitignore ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── R ├── create_init_exp.R ├── filtering_adv.R ├── filtering_basic.R ├── get_associations.R ├── get_tidy_factors.R ├── normalization.R ├── plot_MOFA_hmap.R ├── plot_sample_2D.R ├── project_data.R └── utils-pipe.R ├── README.Rmd ├── README.md ├── _pkgdown.yml ├── inst └── extdata │ ├── testcoldata.rda │ ├── testmetadata.rds │ ├── testmodel.hdf5 │ └── testpbcounts.rda ├── man ├── center_views.Rd ├── create_init_exp.Rd ├── filt_gex_bybckgrnd.Rd ├── filt_gex_byexpr.Rd ├── filt_gex_byhvg.Rd ├── filt_profiles.Rd ├── filt_samples_bycov.Rd ├── filt_views_bygenes.Rd ├── filt_views_bysamples.Rd ├── get_associations.Rd ├── get_geneweights.Rd ├── get_tidy_factors.Rd ├── pb_dat2MOFA.Rd ├── pipe.Rd ├── plot_MOFA_hmap.Rd ├── plot_sample_2D.Rd ├── project_data.Rd └── tmm_trns.Rd └── vignettes ├── .gitignore └── get-started.Rmd /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^MOFAcellulaR\.Rproj$ 2 | ^\.Rproj\.user$ 3 | ^LICENSE\.md$ 4 | ^\.github$ 5 | ^_pkgdown\.yml$ 6 | ^docs$ 7 | ^pkgdown$ 8 | -------------------------------------------------------------------------------- /.github/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | -------------------------------------------------------------------------------- /.github/workflows/pkgdown.yaml: -------------------------------------------------------------------------------- 1 | # Workflow derived from https://github.com/r-lib/actions/tree/v2/examples 2 | # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help 3 | on: 4 | push: 5 | branches: [main, master] 6 | pull_request: 7 | branches: [main, master] 8 | release: 9 | types: [published] 10 | workflow_dispatch: 11 | 12 | name: pkgdown 13 | 14 | jobs: 15 | pkgdown: 16 | runs-on: ubuntu-latest 17 | # Only restrict concurrency for non-PR jobs 18 | concurrency: 19 | group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} 20 | env: 21 | GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} 22 | steps: 23 | - uses: actions/checkout@v3 24 | 25 | - uses: r-lib/actions/setup-pandoc@v2 26 | 27 | - uses: r-lib/actions/setup-r@v2 28 | with: 29 | use-public-rspm: true 30 | 31 | - uses: r-lib/actions/setup-r-dependencies@v2 32 | with: 33 | extra-packages: any::pkgdown, local::. 34 | needs: website 35 | 36 | - name: Build site 37 | run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) 38 | shell: Rscript {0} 39 | 40 | - name: Deploy to GitHub pages 🚀 41 | if: github.event_name != 'pull_request' 42 | uses: JamesIves/github-pages-deploy-action@v4.4.1 43 | with: 44 | clean: false 45 | branch: gh-pages 46 | folder: docs 47 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | *.Rproj 3 | inst/doc 4 | .DS_Store 5 | .Rhistory 6 | docs 7 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: MOFAcellulaR 2 | Title: Multicellular Factor Analysis Using MOFA 3 | Version: 0.0.0.9000 4 | Authors@R: 5 | person("Ricardo O.", "Ramirez Flores", , "roramirezf@uni-heidelberg.de", role = c("aut", "cre"), 6 | comment = c(ORCID = "0000-0003-0087-371X")) 7 | Description: Functions to estimate multicellular programs from single cell data using MOFA. 8 | License: GPL (>= 3) + file LICENSE 9 | Encoding: UTF-8 10 | Roxygen: list(markdown = TRUE) 11 | RoxygenNote: 7.2.3 12 | Depends: 13 | R (>= 3.5.0), 14 | SummarizedExperiment, 15 | MOFA2, 16 | ComplexHeatmap, 17 | grid, 18 | grDevices, 19 | circlize, 20 | uwot, 21 | ggplot2 22 | Imports: 23 | broom, 24 | dplyr, 25 | edgeR, 26 | magrittr, 27 | MASS, 28 | purrr, 29 | S4Vectors, 30 | scran, 31 | tibble, 32 | tidyr 33 | Suggests: 34 | knitr, 35 | rmarkdown 36 | VignetteBuilder: knitr 37 | URL: https://saezlab.github.io/MOFAcellulaR/ 38 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | GNU General Public License 2 | ========================== 3 | 4 | _Version 3, 29 June 2007_ 5 | _Copyright © 2007 Free Software Foundation, Inc. <>_ 6 | 7 | Everyone is permitted to copy and distribute verbatim copies of this license 8 | document, but changing it is not allowed. 9 | 10 | ## Preamble 11 | 12 | The GNU General Public License is a free, copyleft license for software and other 13 | kinds of works. 14 | 15 | The licenses for most software and other practical works are designed to take away 16 | your freedom to share and change the works. By contrast, the GNU General Public 17 | License is intended to guarantee your freedom to share and change all versions of a 18 | program--to make sure it remains free software for all its users. We, the Free 19 | Software Foundation, use the GNU General Public License for most of our software; it 20 | applies also to any other work released this way by its authors. You can apply it to 21 | your programs, too. 22 | 23 | When we speak of free software, we are referring to freedom, not price. Our General 24 | Public Licenses are designed to make sure that you have the freedom to distribute 25 | copies of free software (and charge for them if you wish), that you receive source 26 | code or can get it if you want it, that you can change the software or use pieces of 27 | it in new free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you these rights or 30 | asking you to surrender the rights. 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Definitions 69 | 70 | “This License” refers to version 3 of the GNU General Public License. 71 | 72 | “Copyright” also means copyright-like laws that apply to other kinds of 73 | works, such as semiconductor masks. 74 | 75 | “The Program” refers to any copyrightable work licensed under this 76 | License. Each licensee is addressed as “you”. “Licensees” and 77 | “recipients” may be individuals or organizations. 78 | 79 | To “modify” a work means to copy from or adapt all or part of the work in 80 | a fashion requiring copyright permission, other than the making of an exact copy. 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Mere interaction with a user through a computer 95 | network, with no transfer of a copy, is not conveying. 96 | 97 | An interactive user interface displays “Appropriate Legal Notices” to the 98 | extent that it includes a convenient and prominently visible feature that **(1)** 99 | displays an appropriate copyright notice, and **(2)** tells the user that there is no 100 | warranty for the work (except to the extent that warranties are provided), that 101 | licensees may convey the work under this License, and how to view a copy of this 102 | License. If the interface presents a list of user commands or options, such as a 103 | menu, a prominent item in the list meets this criterion. 104 | 105 | ### 1. Source Code 106 | 107 | The “source code” for a work means the preferred form of the work for 108 | making modifications to it. “Object code” means any non-source form of a 109 | work. 110 | 111 | A “Standard Interface” means an interface that either is an official 112 | standard defined by a recognized standards body, or, in the case of interfaces 113 | specified for a particular programming language, one that is widely used among 114 | developers working in that language. 115 | 116 | The “System Libraries” of an executable work include anything, other than 117 | the work as a whole, that **(a)** is included in the normal form of packaging a Major 118 | Component, but which is not part of that Major Component, and **(b)** serves only to 119 | enable use of the work with that Major Component, or to implement a Standard 120 | Interface for which an implementation is available to the public in source code form. 121 | A “Major Component”, in this context, means a major essential component 122 | (kernel, window system, and so on) of the specific operating system (if any) on which 123 | the executable work runs, or a compiler used to produce the work, or an object code 124 | interpreter used to run it. 125 | 126 | The “Corresponding Source” for a work in object code form means all the 127 | source code needed to generate, install, and (for an executable work) run the object 128 | code and to modify the work, including scripts to control those activities. 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Basic Permissions 143 | 144 | All rights granted under this License are granted for the term of copyright on the 145 | Program, and are irrevocable provided the stated conditions are met. This License 146 | explicitly affirms your unlimited permission to run the unmodified Program. The 147 | output from running a covered work is covered by this License only if the output, 148 | given its content, constitutes a covered work. This License acknowledges your rights 149 | of fair use or other equivalent, as provided by copyright law. 150 | 151 | You may make, run and propagate covered works that you do not convey, without 152 | conditions so long as your license otherwise remains in force. You may convey covered 153 | works to others for the sole purpose of having them make modifications exclusively 154 | for you, or provide you with facilities for running those works, provided that you 155 | comply with the terms of this License in conveying all material for which you do not 156 | control copyright. Those thus making or running the covered works for you must do so 157 | exclusively on your behalf, under your direction and control, on terms that prohibit 158 | them from making any copies of your copyrighted material outside their relationship 159 | with you. 160 | 161 | Conveying under any other circumstances is permitted solely under the conditions 162 | stated below. Sublicensing is not allowed; section 10 makes it unnecessary. 163 | 164 | ### 3. Protecting Users' Legal Rights From Anti-Circumvention Law 165 | 166 | No covered work shall be deemed part of an effective technological measure under any 167 | applicable law fulfilling obligations under article 11 of the WIPO copyright treaty 168 | adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention 169 | of such measures. 170 | 171 | When you convey a covered work, you waive any legal power to forbid circumvention of 172 | technological measures to the extent such circumvention is effected by exercising 173 | rights under this License with respect to the covered work, and you disclaim any 174 | intention to limit operation or modification of the work as a means of enforcing, 175 | against the work's users, your or third parties' legal rights to forbid circumvention 176 | of technological measures. 177 | 178 | ### 4. Conveying Verbatim Copies 179 | 180 | You may convey verbatim copies of the Program's source code as you receive it, in any 181 | medium, provided that you conspicuously and appropriately publish on each copy an 182 | appropriate copyright notice; keep intact all notices stating that this License and 183 | any non-permissive terms added in accord with section 7 apply to the code; keep 184 | intact all notices of the absence of any warranty; and give all recipients a copy of 185 | this License along with the Program. 186 | 187 | You may charge any price or no price for each copy that you convey, and you may offer 188 | support or warranty protection for a fee. 189 | 190 | ### 5. Conveying Modified Source Versions 191 | 192 | You may convey a work based on the Program, or the modifications to produce it from 193 | the Program, in the form of source code under the terms of section 4, provided that 194 | you also meet all of these conditions: 195 | 196 | * **a)** The work must carry prominent notices stating that you modified it, and giving a 197 | relevant date. 198 | * **b)** The work must carry prominent notices stating that it is released under this 199 | License and any conditions added under section 7. This requirement modifies the 200 | requirement in section 4 to “keep intact all notices”. 201 | * **c)** You must license the entire work, as a whole, under this License to anyone who 202 | comes into possession of a copy. This License will therefore apply, along with any 203 | applicable section 7 additional terms, to the whole of the work, and all its parts, 204 | regardless of how they are packaged. This License gives no permission to license the 205 | work in any other way, but it does not invalidate such permission if you have 206 | separately received it. 207 | * **d)** If the work has interactive user interfaces, each must display Appropriate Legal 208 | Notices; however, if the Program has interactive interfaces that do not display 209 | Appropriate Legal Notices, your work need not make them do so. 210 | 211 | A compilation of a covered work with other separate and independent works, which are 212 | not by their nature extensions of the covered work, and which are not combined with 213 | it such as to form a larger program, in or on a volume of a storage or distribution 214 | medium, is called an “aggregate” if the compilation and its resulting 215 | copyright are not used to limit the access or legal rights of the compilation's users 216 | beyond what the individual works permit. Inclusion of a covered work in an aggregate 217 | does not cause this License to apply to the other parts of the aggregate. 218 | 219 | ### 6. Conveying Non-Source Forms 220 | 221 | You may convey a covered work in object code form under the terms of sections 4 and 222 | 5, provided that you also convey the machine-readable Corresponding Source under the 223 | terms of this License, in one of these ways: 224 | 225 | * **a)** Convey the object code in, or embodied in, a physical product (including a 226 | physical distribution medium), accompanied by the Corresponding Source fixed on a 227 | durable physical medium customarily used for software interchange. 228 | * **b)** Convey the object code in, or embodied in, a physical product (including a 229 | physical distribution medium), accompanied by a written offer, valid for at least 230 | three years and valid for as long as you offer spare parts or customer support for 231 | that product model, to give anyone who possesses the object code either **(1)** a copy of 232 | the Corresponding Source for all the software in the product that is covered by this 233 | License, on a durable physical medium customarily used for software interchange, for 234 | a price no more than your reasonable cost of physically performing this conveying of 235 | source, or **(2)** access to copy the Corresponding Source from a network server at no 236 | charge. 237 | * **c)** Convey individual copies of the object code with a copy of the written offer to 238 | provide the Corresponding Source. This alternative is allowed only occasionally and 239 | noncommercially, and only if you received the object code with such an offer, in 240 | accord with subsection 6b. 241 | * **d)** Convey the object code by offering access from a designated place (gratis or for 242 | a charge), and offer equivalent access to the Corresponding Source in the same way 243 | through the same place at no further charge. You need not require recipients to copy 244 | the Corresponding Source along with the object code. If the place to copy the object 245 | code is a network server, the Corresponding Source may be on a different server 246 | (operated by you or a third party) that supports equivalent copying facilities, 247 | provided you maintain clear directions next to the object code saying where to find 248 | the Corresponding Source. Regardless of what server hosts the Corresponding Source, 249 | you remain obligated to ensure that it is available for as long as needed to satisfy 250 | these requirements. 251 | * **e)** Convey the object code using peer-to-peer transmission, provided you inform 252 | other peers where the object code and Corresponding Source of the work are being 253 | offered to the general public at no charge under subsection 6d. 254 | 255 | A separable portion of the object code, whose source code is excluded from the 256 | Corresponding Source as a System Library, need not be included in conveying the 257 | object code work. 258 | 259 | A “User Product” is either **(1)** a “consumer product”, which 260 | means any tangible personal property which is normally used for personal, family, or 261 | household purposes, or **(2)** anything designed or sold for incorporation into a 262 | dwelling. In determining whether a product is a consumer product, doubtful cases 263 | shall be resolved in favor of coverage. For a particular product received by a 264 | particular user, “normally used” refers to a typical or common use of 265 | that class of product, regardless of the status of the particular user or of the way 266 | in which the particular user actually uses, or expects or is expected to use, the 267 | product. A product is a consumer product regardless of whether the product has 268 | substantial commercial, industrial or non-consumer uses, unless such uses represent 269 | the only significant mode of use of the product. 270 | 271 | “Installation Information” for a User Product means any methods, 272 | procedures, authorization keys, or other information required to install and execute 273 | modified versions of a covered work in that User Product from a modified version of 274 | its Corresponding Source. The information must suffice to ensure that the continued 275 | functioning of the modified object code is in no case prevented or interfered with 276 | solely because modification has been made. 277 | 278 | If you convey an object code work under this section in, or with, or specifically for 279 | use in, a User Product, and the conveying occurs as part of a transaction in which 280 | the right of possession and use of the User Product is transferred to the recipient 281 | in perpetuity or for a fixed term (regardless of how the transaction is 282 | characterized), the Corresponding Source conveyed under this section must be 283 | accompanied by the Installation Information. But this requirement does not apply if 284 | neither you nor any third party retains the ability to install modified object code 285 | on the User Product (for example, the work has been installed in ROM). 286 | 287 | The requirement to provide Installation Information does not include a requirement to 288 | continue to provide support service, warranty, or updates for a work that has been 289 | modified or installed by the recipient, or for the User Product in which it has been 290 | modified or installed. Access to a network may be denied when the modification itself 291 | materially and adversely affects the operation of the network or violates the rules 292 | and protocols for communication across the network. 293 | 294 | Corresponding Source conveyed, and Installation Information provided, in accord with 295 | this section must be in a format that is publicly documented (and with an 296 | implementation available to the public in source code form), and must require no 297 | special password or key for unpacking, reading or copying. 298 | 299 | ### 7. Additional Terms 300 | 301 | “Additional permissions” are terms that supplement the terms of this 302 | License by making exceptions from one or more of its conditions. Additional 303 | permissions that are applicable to the entire Program shall be treated as though they 304 | were included in this License, to the extent that they are valid under applicable 305 | law. If additional permissions apply only to part of the Program, that part may be 306 | used separately under those permissions, but the entire Program remains governed by 307 | this License without regard to the additional permissions. 308 | 309 | When you convey a copy of a covered work, you may at your option remove any 310 | additional permissions from that copy, or from any part of it. (Additional 311 | permissions may be written to require their own removal in certain cases when you 312 | modify the work.) You may place additional permissions on material, added by you to a 313 | covered work, for which you have or can give appropriate copyright permission. 314 | 315 | Notwithstanding any other provision of this License, for material you add to a 316 | covered work, you may (if authorized by the copyright holders of that material) 317 | supplement the terms of this License with terms: 318 | 319 | * **a)** Disclaiming warranty or limiting liability differently from the terms of 320 | sections 15 and 16 of this License; or 321 | * **b)** Requiring preservation of specified reasonable legal notices or author 322 | attributions in that material or in the Appropriate Legal Notices displayed by works 323 | containing it; or 324 | * **c)** Prohibiting misrepresentation of the origin of that material, or requiring that 325 | modified versions of such material be marked in reasonable ways as different from the 326 | original version; or 327 | * **d)** Limiting the use for publicity purposes of names of licensors or authors of the 328 | material; or 329 | * **e)** Declining to grant rights under trademark law for use of some trade names, 330 | trademarks, or service marks; or 331 | * **f)** Requiring indemnification of licensors and authors of that material by anyone 332 | who conveys the material (or modified versions of it) with contractual assumptions of 333 | liability to the recipient, for any liability that these contractual assumptions 334 | directly impose on those licensors and authors. 335 | 336 | All other non-permissive additional terms are considered “further 337 | restrictions” within the meaning of section 10. If the Program as you received 338 | it, or any part of it, contains a notice stating that it is governed by this License 339 | along with a term that is a further restriction, you may remove that term. If a 340 | license document contains a further restriction but permits relicensing or conveying 341 | under this License, you may add to a covered work material governed by the terms of 342 | that license document, provided that the further restriction does not survive such 343 | relicensing or conveying. 344 | 345 | If you add terms to a covered work in accord with this section, you must place, in 346 | the relevant source files, a statement of the additional terms that apply to those 347 | files, or a notice indicating where to find the applicable terms. 348 | 349 | Additional terms, permissive or non-permissive, may be stated in the form of a 350 | separately written license, or stated as exceptions; the above requirements apply 351 | either way. 352 | 353 | ### 8. Termination 354 | 355 | You may not propagate or modify a covered work except as expressly provided under 356 | this License. Any attempt otherwise to propagate or modify it is void, and will 357 | automatically terminate your rights under this License (including any patent licenses 358 | granted under the third paragraph of section 11). 359 | 360 | However, if you cease all violation of this License, then your license from a 361 | particular copyright holder is reinstated **(a)** provisionally, unless and until the 362 | copyright holder explicitly and finally terminates your license, and **(b)** permanently, 363 | if the copyright holder fails to notify you of the violation by some reasonable means 364 | prior to 60 days after the cessation. 365 | 366 | Moreover, your license from a particular copyright holder is reinstated permanently 367 | if the copyright holder notifies you of the violation by some reasonable means, this 368 | is the first time you have received notice of violation of this License (for any 369 | work) from that copyright holder, and you cure the violation prior to 30 days after 370 | your receipt of the notice. 371 | 372 | Termination of your rights under this section does not terminate the licenses of 373 | parties who have received copies or rights from you under this License. If your 374 | rights have been terminated and not permanently reinstated, you do not qualify to 375 | receive new licenses for the same material under section 10. 376 | 377 | ### 9. Acceptance Not Required for Having Copies 378 | 379 | You are not required to accept this License in order to receive or run a copy of the 380 | Program. Ancillary propagation of a covered work occurring solely as a consequence of 381 | using peer-to-peer transmission to receive a copy likewise does not require 382 | acceptance. However, nothing other than this License grants you permission to 383 | propagate or modify any covered work. These actions infringe copyright if you do not 384 | accept this License. Therefore, by modifying or propagating a covered work, you 385 | indicate your acceptance of this License to do so. 386 | 387 | ### 10. Automatic Licensing of Downstream Recipients 388 | 389 | Each time you convey a covered work, the recipient automatically receives a license 390 | from the original licensors, to run, modify and propagate that work, subject to this 391 | License. You are not responsible for enforcing compliance by third parties with this 392 | License. 393 | 394 | An “entity transaction” is a transaction transferring control of an 395 | organization, or substantially all assets of one, or subdividing an organization, or 396 | merging organizations. If propagation of a covered work results from an entity 397 | transaction, each party to that transaction who receives a copy of the work also 398 | receives whatever licenses to the work the party's predecessor in interest had or 399 | could give under the previous paragraph, plus a right to possession of the 400 | Corresponding Source of the work from the predecessor in interest, if the predecessor 401 | has it or can get it with reasonable efforts. 402 | 403 | You may not impose any further restrictions on the exercise of the rights granted or 404 | affirmed under this License. For example, you may not impose a license fee, royalty, 405 | or other charge for exercise of rights granted under this License, and you may not 406 | initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging 407 | that any patent claim is infringed by making, using, selling, offering for sale, or 408 | importing the Program or any portion of it. 409 | 410 | ### 11. Patents 411 | 412 | A “contributor” is a copyright holder who authorizes use under this 413 | License of the Program or a work on which the Program is based. The work thus 414 | licensed is called the contributor's “contributor version”. 415 | 416 | A contributor's “essential patent claims” are all patent claims owned or 417 | controlled by the contributor, whether already acquired or hereafter acquired, that 418 | would be infringed by some manner, permitted by this License, of making, using, or 419 | selling its contributor version, but do not include claims that would be infringed 420 | only as a consequence of further modification of the contributor version. For 421 | purposes of this definition, “control” includes the right to grant patent 422 | sublicenses in a manner consistent with the requirements of this License. 423 | 424 | Each contributor grants you a non-exclusive, worldwide, royalty-free patent license 425 | under the contributor's essential patent claims, to make, use, sell, offer for sale, 426 | import and otherwise run, modify and propagate the contents of its contributor 427 | version. 428 | 429 | In the following three paragraphs, a “patent license” is any express 430 | agreement or commitment, however denominated, not to enforce a patent (such as an 431 | express permission to practice a patent or covenant not to sue for patent 432 | infringement). To “grant” such a patent license to a party means to make 433 | such an agreement or commitment not to enforce a patent against the party. 434 | 435 | If you convey a covered work, knowingly relying on a patent license, and the 436 | Corresponding Source of the work is not available for anyone to copy, free of charge 437 | and under the terms of this License, through a publicly available network server or 438 | other readily accessible means, then you must either **(1)** cause the Corresponding 439 | Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the 440 | patent license for this particular work, or **(3)** arrange, in a manner consistent with 441 | the requirements of this License, to extend the patent license to downstream 442 | recipients. “Knowingly relying” means you have actual knowledge that, but 443 | for the patent license, your conveying the covered work in a country, or your 444 | recipient's use of the covered work in a country, would infringe one or more 445 | identifiable patents in that country that you have reason to believe are valid. 446 | 447 | If, pursuant to or in connection with a single transaction or arrangement, you 448 | convey, or propagate by procuring conveyance of, a covered work, and grant a patent 449 | license to some of the parties receiving the covered work authorizing them to use, 450 | propagate, modify or convey a specific copy of the covered work, then the patent 451 | license you grant is automatically extended to all recipients of the covered work and 452 | works based on it. 453 | 454 | A patent license is “discriminatory” if it does not include within the 455 | scope of its coverage, prohibits the exercise of, or is conditioned on the 456 | non-exercise of one or more of the rights that are specifically granted under this 457 | License. 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Interpretation of Sections 15 and 16 535 | 536 | If the disclaimer of warranty and limitation of liability provided above cannot be 537 | given local legal effect according to their terms, reviewing courts shall apply local 538 | law that most closely approximates an absolute waiver of all civil liability in 539 | connection with the Program, unless a warranty or assumption of liability accompanies 540 | a copy of the Program in return for a fee. 541 | 542 | _END OF TERMS AND CONDITIONS_ 543 | 544 | ## How to Apply These Terms to Your New Programs 545 | 546 | If you develop a new program, and you want it to be of the greatest possible use to 547 | the public, the best way to achieve this is to make it free software which everyone 548 | can redistribute and change under these terms. 549 | 550 | To do so, attach the following notices to the program. It is safest to attach them 551 | to the start of each source file to most effectively state the exclusion of warranty; 552 | and each file should have at least the “copyright” line and a pointer to 553 | where the full notice is found. 554 | 555 | 556 | Copyright (C) 557 | 558 | This program is free software: you can redistribute it and/or modify 559 | it under the terms of the GNU General Public License as published by 560 | the Free Software Foundation, either version 3 of the License, or 561 | (at your option) any later version. 562 | 563 | This program is distributed in the hope that it will be useful, 564 | but WITHOUT ANY WARRANTY; without even the implied warranty of 565 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 566 | GNU General Public License for more details. 567 | 568 | You should have received a copy of the GNU General Public License 569 | along with this program. If not, see . 570 | 571 | Also add information on how to contact you by electronic and paper mail. 572 | 573 | If the program does terminal interaction, make it output a short notice like this 574 | when it starts in an interactive mode: 575 | 576 | Copyright (C) 577 | This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. 578 | This is free software, and you are welcome to redistribute it 579 | under certain conditions; type 'show c' for details. 580 | 581 | The hypothetical commands `show w` and `show c` should show the appropriate parts of 582 | the General Public License. Of course, your program's commands might be different; 583 | for a GUI interface, you would use an “about box”. 584 | 585 | You should also get your employer (if you work as a programmer) or school, if any, to 586 | sign a “copyright disclaimer” for the program, if necessary. For more 587 | information on this, and how to apply and follow the GNU GPL, see 588 | <>. 589 | 590 | The GNU General Public License does not permit incorporating your program into 591 | proprietary programs. If your program is a subroutine library, you may consider it 592 | more useful to permit linking proprietary applications with the library. If this is 593 | what you want to do, use the GNU Lesser General Public License instead of this 594 | License. But first, please read 595 | <>. 596 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export("%>%") 4 | export(center_views) 5 | export(create_init_exp) 6 | export(filt_gex_bybckgrnd) 7 | export(filt_gex_byexpr) 8 | export(filt_gex_byhvg) 9 | export(filt_profiles) 10 | export(filt_samples_bycov) 11 | export(filt_views_bygenes) 12 | export(filt_views_bysamples) 13 | export(get_associations) 14 | export(get_geneweights) 15 | export(get_tidy_factors) 16 | export(pb_dat2MOFA) 17 | export(plot_MOFA_hmap) 18 | export(plot_sample_2D) 19 | export(project_data) 20 | export(tmm_trns) 21 | import(ComplexHeatmap) 22 | import(MASS) 23 | import(MOFA2) 24 | import(S4Vectors) 25 | import(SummarizedExperiment) 26 | import(broom) 27 | import(circlize) 28 | import(dplyr) 29 | import(edgeR) 30 | import(ggplot2) 31 | import(grDevices) 32 | import(grid) 33 | import(purrr) 34 | import(scran) 35 | import(stats) 36 | import(tibble) 37 | import(tidyr) 38 | import(uwot) 39 | importFrom(magrittr,"%>%") 40 | -------------------------------------------------------------------------------- /R/create_init_exp.R: -------------------------------------------------------------------------------- 1 | #' Single-cell SummarizedExperiment object 2 | #' 3 | #' @description 4 | #' Creates a SummarizedExperiment object necessary to build a multi-view representation. 5 | #' 6 | #' @details 7 | #' 8 | #' This function is the first step for a multicellular factor analysis. 9 | #' It collects in a single object the pseudobulk counts of a single cell experiment 10 | #' and its annotations. 11 | #' 12 | #' @param counts Named numeric matrix with features in rows and samples in columns. 13 | #' @param coldata A data frame containing the annotations of the samples. 14 | #' 15 | #' @return SummarizedExperiment with provided data 16 | #' @export 17 | #' 18 | #' @import SummarizedExperiment 19 | #' @import S4Vectors 20 | #' 21 | #' @examples 22 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 23 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 24 | #' load(file.path(inputs_dir, "testcoldata.rda")) 25 | #' pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 26 | create_init_exp <- function(counts, coldata) { 27 | 28 | pb_dat <- SummarizedExperiment::SummarizedExperiment(assays = list("counts" = counts), 29 | colData = S4Vectors::DataFrame(coldata)) 30 | 31 | return(pb_dat) 32 | } 33 | 34 | #' Create MOFA-ready dataframe 35 | #' 36 | #' @description 37 | #' Creates from a list of SummarizedExperiments a multi-view representation for MOFA 38 | #' 39 | #' @details 40 | #' 41 | #' This function is the last data preparation step for a multicellular factor analysis. 42 | #' It collects a collection of cell-type-specific SummarizedExperiments into a 43 | #' single data frame ready to be used in MOFA. Features are modified 44 | #' so as to reflect their cell type of origin. 45 | #' 46 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 47 | #' 48 | #' @return Data frame in a multiview representation 49 | #' @export 50 | #' 51 | #' @import SummarizedExperiment 52 | #' @import S4Vectors 53 | #' @import purrr 54 | #' @import scran 55 | #' @import tidyr 56 | #' @import tibble 57 | #' @import dplyr 58 | #' 59 | #' @examples 60 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 61 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 62 | #' load(file.path(inputs_dir, "testcoldata.rda")) 63 | #' pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 64 | #' 65 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 66 | #' cts = c("Fib","CM"), 67 | #' ncells = 5, 68 | #' counts_col = "cell_counts", 69 | #' ct_col = "cell_type") 70 | #' 71 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 72 | #' min.count = 5, 73 | #' min.prop = 0.25) 74 | #' 75 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 76 | #' scale_factor = 1000000) 77 | #' 78 | #' multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 79 | pb_dat2MOFA <- function(pb_dat_list, sample_column = "donor_id") { 80 | 81 | pb_red <- purrr::map(pb_dat_list, function(x) { 82 | 83 | dat <- SummarizedExperiment::assay(x, "logcounts") 84 | 85 | colnames(dat) <- SummarizedExperiment::colData(x)[, sample_column] 86 | 87 | dat %>% 88 | base::as.data.frame() %>% 89 | tibble::rownames_to_column("feature") %>% 90 | tidyr::pivot_longer(-.data$feature, names_to = "sample", values_to = "value") 91 | 92 | }) %>% 93 | tibble::enframe(name = "view") %>% 94 | tidyr::unnest(cols = c(value)) %>% 95 | dplyr::mutate(feature = paste0(.data$view, "_", .data$feature)) 96 | 97 | return(pb_red) 98 | 99 | } 100 | 101 | 102 | #' Center view-data 103 | #' 104 | #' @description 105 | #' Centers each element of a list of SummarizedExperiments 106 | #' 107 | #' @details 108 | #' 109 | #' Given that the MOFA model in general uses centered data, when interested in projecting new 110 | #' data to a new manifold, it is needed to perform centering. 111 | #' 112 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 113 | #' 114 | #' @return A named list of SummarizedExperiments per cell type provided with centered pseudobulk profiles 115 | #' @export 116 | #' 117 | #' @import SummarizedExperiment 118 | #' @import S4Vectors 119 | #' @import purrr 120 | #' @import scran 121 | #' 122 | #' @examples 123 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 124 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 125 | #' load(file.path(inputs_dir, "testcoldata.rda")) 126 | #' pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 127 | #' 128 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 129 | #' cts = c("Fib","CM"), 130 | #' ncells = 5, 131 | #' counts_col = "cell_counts", 132 | #' ct_col = "cell_type") 133 | #' 134 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 135 | #' min.count = 5, 136 | #' min.prop = 0.25) 137 | #' 138 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 139 | #' scale_factor = 1000000) 140 | #' 141 | #' ct_list <- center_views(pb_dat_list = ct_list) 142 | #' 143 | #' multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 144 | center_views <- function(pb_dat_list) { 145 | 146 | pb_red <- purrr::map(pb_dat_list, function(x) { 147 | 148 | dat <- SummarizedExperiment::assay(x, "logcounts") %>% t() # genes in columns now 149 | 150 | scaled_dat <- base::scale(dat, scale = FALSE) %>% t() # genes in rows now 151 | 152 | SummarizedExperiment::assay(x, "logcounts") <- scaled_dat #replace assay 153 | 154 | return(x) 155 | 156 | }) 157 | 158 | return(pb_red) 159 | 160 | } 161 | -------------------------------------------------------------------------------- /R/filtering_adv.R: -------------------------------------------------------------------------------- 1 | #' Indentify highly variable genes 2 | #' 3 | #' @description 4 | #' Identifies highly variable features from a log-normalized count matrix or filters matrices by a list of genes 5 | #' provided by the user. 6 | #' 7 | #' @details 8 | #' 9 | #' This function estimates highly variable genes per cell type using `scran::getTopHVGs`. Alternatively, this function 10 | #' allows the user to provide the features to be used in each cell type. If prior genes are used, for cell types 11 | #' where this information is missing, highly variable genes will be calculated 12 | #' 13 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 14 | #' @param prior_hvg NULL by default. Alternatively, a named list with a character vector containing features to select. 15 | #' @param var.threshold Numeric. Inherited from `scran::getTopHVGs()`. Minimum threshold on the metric of variation 16 | #' @return A named list of SummarizedExperiments per cell type provided with filtered normalized log transformed data in their `logcounts` assay 17 | #' @export 18 | #' 19 | #' @import SummarizedExperiment 20 | #' @import S4Vectors 21 | #' @import edgeR 22 | #' @import purrr 23 | #' @import scran 24 | #' 25 | #' @examples 26 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 27 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 28 | #' load(file.path(inputs_dir, "testcoldata.rda")) 29 | #' 30 | #' pb_obj <- create_init_exp(counts = testpbcounts, 31 | #' coldata = testcoldata) 32 | #' 33 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 34 | #' cts = c("Fib","CM"), 35 | #' ncells = 5, 36 | #' counts_col = "cell_counts", 37 | #' ct_col = "cell_type") 38 | #' 39 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 40 | #' min.count = 5, 41 | #' min.prop = 0.25) 42 | #' 43 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 44 | #' scale_factor = 1000000) 45 | #' 46 | #' ct_list <- filt_gex_byhvg(pb_dat_list = ct_list, 47 | #' prior_hvg = NULL, 48 | #' var.threshold = 0) 49 | #' 50 | filt_gex_byhvg <- function(pb_dat_list, 51 | prior_hvg = NULL, 52 | var.threshold = 1) { 53 | # If there is no prior, calculate hvgs for every member of the list 54 | if(is.null(prior_hvg)) { 55 | 56 | pb_dat_red <- purrr::map(pb_dat_list, function(x) { 57 | hvg <- scran::getTopHVGs(x,var.threshold = var.threshold) 58 | return(x[hvg, ]) 59 | }) 60 | 61 | return(pb_dat_red) 62 | 63 | } else { 64 | 65 | cts_in_data <- purrr::set_names(names(pb_dat_list)) 66 | cts_in_prior <- purrr::set_names(names(prior_hvg)) 67 | 68 | in_cts <- cts_in_data[cts_in_data %in% cts_in_prior] 69 | out_cts <- cts_in_data[!cts_in_data %in% cts_in_prior] 70 | 71 | in_cts_data <- pb_dat_list[in_cts] 72 | # For cell types with prior, filter with available genes 73 | for(ct in in_cts) { 74 | ct_genes <- in_cts_data[[ct]] %>% rownames() 75 | ct_genes <- ct_genes[ct_genes %in% prior_hvg[[ct]]] 76 | in_cts_data[[ct]] <- in_cts_data[[ct]][ct_genes,] 77 | } 78 | 79 | if(length(out_cts) == 0) { 80 | 81 | return(in_cts_data) 82 | 83 | } else { 84 | # For the rest, calculate hvgs 85 | out_cts_data <- pb_dat_list[out_cts] 86 | 87 | out_cts_data <- purrr::map(out_cts_data, function(x) { 88 | hvg <- scran::getTopHVGs(x,var.threshold = var.threshold) 89 | return(x[hvg, ]) 90 | }) 91 | 92 | return(c(in_cts_data, out_cts_data)) 93 | 94 | } 95 | } 96 | } 97 | 98 | #' Filter background expression of marker genes 99 | #' 100 | #' @description 101 | #' For a collection of matrices, we exclude features that are considered background based on prior 102 | #' knowledge of marker genes 103 | #' 104 | #' @details 105 | #' Performs filtering of highly variable genes (after data transformation). 106 | #' This is based on marker genes. The assumption is that background gene expression can be traced 107 | #' by expression of cell type marker genes in cell types which shouldn't express the gene. 108 | #' Marker genes will be only kept in the matrix if they are expressed in the expected cell type 109 | #' 110 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 111 | #' @param prior_mrks A named list providing marker genes per cell type. Names should be identical as in `pb_dat_list` 112 | #' @return A named list of SummarizedExperiments per cell type provided with filtered normalized log transformed data in their `logcounts` assay 113 | #' @export 114 | #' 115 | #' @import SummarizedExperiment 116 | #' @import S4Vectors 117 | #' @import edgeR 118 | #' @import purrr 119 | #' @import scran 120 | #' @import tidyr 121 | #' @import tibble 122 | #' @import dplyr 123 | #' 124 | #' @examples 125 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 126 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 127 | #' load(file.path(inputs_dir, "testcoldata.rda")) 128 | #' 129 | #' pb_obj <- create_init_exp(counts = testpbcounts, 130 | #' coldata = testcoldata) 131 | #' 132 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 133 | #' cts = c("Fib","CM"), 134 | #' ncells = 5, 135 | #' counts_col = "cell_counts", 136 | #' ct_col = "cell_type") 137 | #' 138 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 139 | #' min.count = 5, 140 | #' min.prop = 0.25) 141 | #' 142 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 143 | #' scale_factor = 1000000) 144 | #' 145 | #' ct_list <- filt_gex_byhvg(pb_dat_list = ct_list, 146 | #' prior_hvg = NULL, 147 | #' var.threshold = 0) 148 | #' 149 | #' prior_hvg_test <- list("CM" = c("TTN"), 150 | #' "Fib" = c("POSTN")) 151 | #' 152 | #' ct_list <- filt_gex_bybckgrnd(pb_dat_list = ct_list, 153 | #' prior_mrks = prior_hvg_test) 154 | #' 155 | filt_gex_bybckgrnd <- function(pb_dat_list, prior_mrks) { 156 | 157 | # Current genes per view 158 | ct_genes <- purrr::map(pb_dat_list, rownames) %>% 159 | tibble::enframe("view","gene") %>% 160 | tidyr::unnest(.data$gene) 161 | 162 | prior_mrks_df <- prior_mrks %>% 163 | tibble::enframe("view_origin","gene") %>% 164 | tidyr::unnest(.data$gene) %>% 165 | dplyr::mutate(marker_gene = TRUE) 166 | 167 | # Here are genes that aren't cell type markers 168 | ok_genes <- ct_genes %>% 169 | dplyr::left_join(prior_mrks_df, by = "gene") %>% 170 | dplyr::filter(is.na(.data$marker_gene)) %>% 171 | dplyr::select_at(c("view", "gene")) 172 | 173 | # Here are genes selected as HVG that are marker 174 | # genes, we will keep only genes if they appear 175 | # in the right cell 176 | not_bckground_genes <- ct_genes %>% 177 | dplyr::left_join(prior_mrks_df, by = "gene") %>% 178 | stats::na.omit() %>% 179 | tidyr::unnest(c()) %>% 180 | dplyr::filter(.data$view == .data$view_origin) %>% 181 | dplyr::select_at(c("view", "gene")) 182 | 183 | clean_hvgs <- dplyr::bind_rows(ok_genes, 184 | not_bckground_genes) %>% 185 | dplyr::group_by(view) %>% 186 | tidyr::nest() %>% 187 | dplyr::mutate(data = map(.data$data, ~.x[[1]])) %>% 188 | tibble::deframe() 189 | 190 | pb_dat_list <- pb_dat_list %>% 191 | filt_gex_byhvg(pb_dat_list = ., 192 | prior_hvg = clean_hvgs, 193 | var.threshold = NULL) 194 | 195 | return(pb_dat_list) 196 | } 197 | -------------------------------------------------------------------------------- /R/filtering_basic.R: -------------------------------------------------------------------------------- 1 | #' Filter pseudobulk profiles 2 | #' 3 | #' @description 4 | #' Filter pseudobulk profiles of specific cell types based on the number of cells from which they were generated. 5 | #' 6 | #' @details 7 | #' 8 | #' This function assumes that you have a SummarizedExperiment object 9 | #' with information in `colData(object)` specifying the number of cells used 10 | #' for each profile and the cell-type grouping this profiles. The function then will 11 | #' select only the cell-types provided by the user and filter the profiles 12 | #' with less cells as the ones specified. 13 | #' 14 | #' @param pb_dat SummarizedExperiment generated from `MOFAcellulaR::create_init_exp()` 15 | #' @param cts A vector containing the names of cells to be used in the analysis 16 | #' @param ncells Number of minimum cells of each pseudobulk profile 17 | #' @param counts_col String pointing to the column in `colData(pb_dat)` where the number of cells per pseudobulk was stored 18 | #' @param ct_col String pointing to the column in `colData(pb_dat)` where the cell-type category per pseudobulk was stored 19 | #' 20 | #' @return A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 21 | #' @export 22 | #' 23 | #' @import SummarizedExperiment 24 | #' @import S4Vectors 25 | #' @import purrr 26 | #' 27 | #' @examples 28 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 29 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 30 | #' load(file.path(inputs_dir, "testcoldata.rda")) 31 | #' 32 | #' pb_obj <- create_init_exp(counts = testpbcounts, 33 | #' coldata = testcoldata) 34 | #' 35 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 36 | #' cts = c("Fib","CM"), 37 | #' ncells = 5, 38 | #' counts_col = "cell_counts", 39 | #' ct_col = "cell_type") 40 | #' 41 | filt_profiles <- function(pb_dat, 42 | cts,ncells = 50, 43 | counts_col = "cell_counts", 44 | ct_col = "cell_type"){ 45 | 46 | # by n of cells 47 | 48 | ix <- base::which(SummarizedExperiment::colData(pb_dat)[,counts_col] >= ncells) 49 | pb_dat <- pb_dat[, ix] 50 | 51 | # by views of interest 52 | 53 | if(is.null(cts)) { 54 | 55 | cts <- purrr::set_names(SummarizedExperiment::colData(pb_dat)[,ct_col] %>% 56 | unique()) 57 | 58 | } else { 59 | 60 | cts <- purrr::set_names(cts) 61 | 62 | } 63 | 64 | pb_dat_list <- purrr::map(cts, function(ctype) { 65 | 66 | ix <- base::which(SummarizedExperiment::colData(pb_dat)[,ct_col] == ctype) 67 | 68 | return(pb_dat[,ix]) 69 | 70 | }) 71 | 72 | return(pb_dat_list) 73 | 74 | } 75 | 76 | #' Filter lowly expressed genes from pseudobulk profiles 77 | #' 78 | #' @description 79 | #' Filter lowly expressed genes from pseudobulk profiles using `edgeR`. 80 | #' 81 | #' @details 82 | #' 83 | #' This function wraps `edgeR::filterByExpr()` to be applied to lists of SummarizedExperiments.It assumes 84 | #' that all samples are part of the same group. 85 | #' 86 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 87 | #' @param min.count Numeric, minimum counts per sample to be considered. Check `?edgeR::filterByExpr()` for details. 88 | #' @param min.prop Numeric, minimum proportion of samples containing the minimum counts. Check `?edgeR::filterByExpr()` for details. 89 | #' 90 | #' @return A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 91 | #' @export 92 | #' 93 | #' @import SummarizedExperiment 94 | #' @import S4Vectors 95 | #' @import edgeR 96 | #' @import purrr 97 | #' 98 | #' @examples 99 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 100 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 101 | #' load(file.path(inputs_dir, "testcoldata.rda")) 102 | #' 103 | #' pb_obj <- create_init_exp(counts = testpbcounts, 104 | #' coldata = testcoldata) 105 | #' 106 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 107 | #' cts = c("Fib","CM"), 108 | #' ncells = 5, 109 | #' counts_col = "cell_counts", 110 | #' ct_col = "cell_type") 111 | #' 112 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 113 | #' min.count = 5, 114 | #' min.prop = 0.25) 115 | filt_gex_byexpr <- function(pb_dat_list, min.count, min.prop) { 116 | 117 | pb_dat_red <- purrr::map(pb_dat_list, function(x) { 118 | 119 | useful_genes <- edgeR::filterByExpr(x, 120 | min.count = min.count, 121 | min.prop = min.prop) 122 | 123 | return(x[useful_genes, ]) 124 | }) 125 | 126 | return(pb_dat_red) 127 | 128 | } 129 | 130 | #' Filter views with not enough features 131 | #' 132 | #' @description 133 | #' Filter complete views based on their number of profiled features 134 | #' 135 | #' @details 136 | #' 137 | #' This function allows the user to control the number of minimum features per view 138 | #' 139 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 140 | #' @param ngenes Numeric, minimum number of features per view. 141 | #' 142 | #' @return A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 143 | #' @export 144 | #' 145 | #' @import SummarizedExperiment 146 | #' @import S4Vectors 147 | #' @import purrr 148 | #' 149 | #' @examples 150 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 151 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 152 | #' load(file.path(inputs_dir, "testcoldata.rda")) 153 | #' 154 | #' pb_obj <- create_init_exp(counts = testpbcounts, 155 | #' coldata = testcoldata) 156 | #' 157 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 158 | #' cts = c("Fib","CM"), 159 | #' ncells = 5, 160 | #' counts_col = "cell_counts", 161 | #' ct_col = "cell_type") 162 | #' 163 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 164 | #' min.count = 5, 165 | #' min.prop = 0.25) 166 | #' 167 | #' ct_list <- filt_views_bygenes(pb_dat_list = ct_list, 168 | #' ngenes = 15) 169 | #' 170 | filt_views_bygenes <- function(pb_dat_list, 171 | ngenes) { 172 | 173 | view_count <- pb_dat_list %>% 174 | purrr::map_dbl(~ .x %>% nrow()) 175 | 176 | view_count <- view_count[view_count >= ngenes] 177 | 178 | views <- names(view_count) 179 | 180 | return(pb_dat_list[views]) 181 | 182 | } 183 | 184 | #' Filter views with not enough samples 185 | #' 186 | #' @description 187 | #' Filter complete views based on their number of profiled samples 188 | #' 189 | #' @details 190 | #' 191 | #' This function allows the user to control the number of minimum samples per view 192 | #' 193 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 194 | #' @param nsamples Numeric, minimum number of samples per view. 195 | #' 196 | #' @return A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 197 | #' @export 198 | #' 199 | #' @import SummarizedExperiment 200 | #' @import S4Vectors 201 | #' @import purrr 202 | #' 203 | #' @examples 204 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 205 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 206 | #' load(file.path(inputs_dir, "testcoldata.rda")) 207 | #' 208 | #' pb_obj <- create_init_exp(counts = testpbcounts, 209 | #' coldata = testcoldata) 210 | #' 211 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 212 | #' cts = c("Fib","CM"), 213 | #' ncells = 5, 214 | #' counts_col = "cell_counts", 215 | #' ct_col = "cell_type") 216 | #' 217 | #' ct_list <- filt_views_bysamples(pb_dat_list = ct_list, 218 | #' nsamples = 2) 219 | #' 220 | filt_views_bysamples <- function(pb_dat_list, 221 | nsamples) { 222 | 223 | view_count <- pb_dat_list %>% 224 | purrr::map_dbl(~ .x %>% ncol()) 225 | 226 | view_count <- view_count[view_count >= nsamples] 227 | 228 | views <- names(view_count) 229 | 230 | return(pb_dat_list[views]) 231 | 232 | } 233 | 234 | #' Filter samples that have a limited coverage of features in a view 235 | #' 236 | #' @description 237 | #' Excludes samples from views with a coverage lower than the one stated 238 | #' 239 | #' @details 240 | #' 241 | #' This function allows the user to exclude samples that have a limited number of features that could create potential outliers 242 | #' 243 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 244 | #' @param prop_coverage Numeric, minimum proportion of coverage of features (different from 0). 245 | #' 246 | #' @return A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 247 | #' @export 248 | #' 249 | #' @import SummarizedExperiment 250 | #' @import S4Vectors 251 | #' @import purrr 252 | #' 253 | #' @examples 254 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 255 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 256 | #' load(file.path(inputs_dir, "testcoldata.rda")) 257 | #' 258 | #' pb_obj <- create_init_exp(counts = testpbcounts, 259 | #' coldata = testcoldata) 260 | #' 261 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 262 | #' cts = c("Fib","CM"), 263 | #' ncells = 5, 264 | #' counts_col = "cell_counts", 265 | #' ct_col = "cell_type") 266 | #' 267 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 268 | #' min.count = 5, 269 | #' min.prop = 0.25) 270 | #' 271 | #' ct_list <- filt_views_bygenes(pb_dat_list = ct_list, 272 | #' ngenes = 15) 273 | #' 274 | #' ct_list <- filt_samples_bycov(pb_dat_list = ct_list, 275 | #' prop_coverage = 0.9) 276 | #' 277 | #' 278 | filt_samples_bycov <- function(pb_dat_list, prop_coverage = 0.9) { 279 | 280 | pb_dat_list_new <- purrr::map(pb_dat_list, function(pb_obj) { 281 | 282 | mat <- SummarizedExperiment::assay(pb_obj, "counts") 283 | 284 | sample_vect <- (mat != 0) %>% colSums(.)/nrow(mat) 285 | 286 | sel_samples <- names(sample_vect[which(sample_vect >= prop_coverage)]) 287 | 288 | return(pb_obj[,sel_samples]) 289 | 290 | }) 291 | 292 | return(pb_dat_list_new) 293 | } 294 | -------------------------------------------------------------------------------- /R/get_associations.R: -------------------------------------------------------------------------------- 1 | #' Associate factors to covariates of interest 2 | #' 3 | #' @description 4 | #' Performs Analysis of Variance (ANOVA) or linear models to associate 5 | #' factor scores with covariates of interest provided by the user 6 | #' 7 | #' @details 8 | #' Given a covariate of interest and a defined test, this function tests for associations with factor scores. For 9 | #' categorical tests, ANOVAs are fitted, while for continous variables, linear models. P-values are corrected 10 | #' using the Benjamini-Hochberg procedure. 11 | #' 12 | #' @param model A MOFA2 model. 13 | #' @param metadata A data frame containing the annotations of the samples included in the MOFA model. 14 | #' @param sample_id_column A string character that refers to the column in `metadata` where the sample identifier is located. 15 | #' @param test_variable A string character that refers to the column in `metadata` where the covariate to be tested is located. 16 | #' @param test_type A string character ("categorical", "continuous"). 17 | #' @param categorical_type A string character ("parametric", "not_parametric"), only applies for categorical data. 18 | #' @param group Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided. 19 | #' 20 | #' 21 | #' @return A dataframe in a tidy format containing the p-values of the association tests per factor 22 | #' @export 23 | #' 24 | #' @import MOFA2 25 | #' @import tibble 26 | #' @import dplyr 27 | #' @import broom 28 | #' @import stats 29 | #' 30 | #' @examples 31 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 32 | #' model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 33 | #' metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 34 | #' metadata$var <- stats::rnorm(nrow(metadata)) 35 | #' 36 | #' categorical_assoc <- get_associations(model = model, 37 | #' metadata = metadata, 38 | #' sample_id_column = "sample", 39 | #' test_variable = "patient_group", 40 | #' test_type = "categorical", 41 | #' group = FALSE) 42 | #' 43 | #' continuous_assoc <- get_associations(model = model, 44 | #' metadata = metadata, 45 | #' sample_id_column = "sample", 46 | #' test_variable = "var", 47 | #' test_type = "continuous", 48 | #' group = FALSE) 49 | get_associations <- function(model, 50 | metadata, 51 | sample_id_column, 52 | test_variable, 53 | test_type = "categorical", 54 | categorical_type = "parametric", 55 | group = FALSE) { 56 | 57 | factors <- get_tidy_factors(model = model, 58 | metadata = metadata, 59 | factor = "all", 60 | group = group, 61 | sample_id_column = sample_id_column) 62 | 63 | # Get factors associated with patient group 64 | factors <- factors %>% 65 | dplyr::select_at(c("sample", test_variable, "Factor", "value")) %>% 66 | na.omit() %>% # this allows to make subsets of data 67 | dplyr::group_by(Factor) %>% 68 | tidyr::nest() %>% 69 | dplyr::mutate(pvalue = purrr::map(.data$data, function(dat) { 70 | 71 | # Fit ANOVAs if testing variable is categorical 72 | if(test_type == "categorical") { 73 | 74 | if(categorical_type == "parametric") { 75 | 76 | factor_aov <- stats::aov(as.formula(paste0("value ~ ", test_variable)), data = dat) %>% 77 | broom::tidy() %>% 78 | dplyr::filter(term == test_variable) %>% 79 | dplyr::select_at(c("term", "p.value")) 80 | 81 | return(factor_aov) 82 | 83 | } else { 84 | 85 | factor_kw <- stats::kruskal.test(as.formula(paste0("value ~ ", test_variable)), data = dat) %>% 86 | broom::tidy() %>% 87 | dplyr::mutate(term = test_variable) %>% 88 | dplyr::select_at(c("term", "p.value")) 89 | 90 | return(factor_kw) 91 | 92 | } 93 | 94 | } else { 95 | # Fit linear model if testing variable is continous 96 | factor_lm <- stats::lm(as.formula(paste0("value ~ ", test_variable)), data = dat) %>% 97 | broom::tidy() %>% 98 | dplyr::filter(term == test_variable) %>% 99 | dplyr::select_at(c("term", "p.value")) 100 | 101 | return(factor_lm) 102 | 103 | } 104 | 105 | })) %>% 106 | tidyr::unnest(pvalue) %>% 107 | dplyr::ungroup() %>% 108 | dplyr::mutate(adj_pvalue = stats::p.adjust(p.value)) 109 | 110 | expl_var <- factors %>% 111 | dplyr::select_at(c("Factor", "term", "p.value", "adj_pvalue")) 112 | 113 | return(expl_var) 114 | 115 | } 116 | -------------------------------------------------------------------------------- /R/get_tidy_factors.R: -------------------------------------------------------------------------------- 1 | #' Extract factor scores from a model in a tidy format with meta data 2 | #' 3 | #' @description 4 | #' Generates a tidy dataframe for factors of interest useful for plotting functions. 5 | #' 6 | #' @details 7 | #' 8 | #' This function simplifies the extraction of factor scores from MOFA models. 9 | #' 10 | #' @param model A MOFA2 model. 11 | #' @param metadata A data frame containing the annotations of the samples included in the MOFA model. 12 | #' @param factor A string character to define the factor to be extracted. Alternatively, "all" to get all factors. 13 | #' @param group Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided. 14 | #' @param sample_id_column A string character that refers to the column in `metadata` where the sample identifier is located. 15 | #' 16 | #' 17 | #' @return A dataframe in a tidy format containing the factor scores per sample together with metadata 18 | #' @export 19 | #' 20 | #' @import MOFA2 21 | #' @import tibble 22 | #' @import dplyr 23 | #' 24 | #' @examples 25 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 26 | #' model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 27 | #' metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 28 | #' all_factors <- get_tidy_factors(model = model, 29 | #' metadata = metadata, 30 | #' factor = "all", 31 | #' sample_id_column = "sample") 32 | #' Factor3 <- get_tidy_factors(model = model, 33 | #' metadata = metadata, 34 | #' factor = "Factor3", 35 | #' sample_id_column = "sample") 36 | get_tidy_factors <- function(model, 37 | metadata, 38 | factor, 39 | group = FALSE, 40 | sample_id_column = "sample") { 41 | 42 | if(group) { 43 | 44 | if(factor == "all") { 45 | #Here we return all factors and bind the distinct groups 46 | factor_scores <- MOFA2::get_factors(model, factors = "all") %>% 47 | base::do.call(base::rbind, .) 48 | 49 | } else { 50 | #Here we return specific factors 51 | factor_scores <- MOFA2::get_factors(model, factors = "all") %>% 52 | base::do.call(base::rbind, .) 53 | 54 | factor_scores <- factor_scores[, factor, drop= F] 55 | 56 | } 57 | 58 | } else { 59 | 60 | if(factor == "all") { 61 | #Here we return all factors 62 | factor_scores <- MOFA2::get_factors(model, factors = "all")[[1]] 63 | } else { 64 | #Here we return specific factors 65 | factor_scores <- MOFA2::get_factors(model, factors = "all")[[1]][,factor, drop= F] 66 | } 67 | 68 | } 69 | 70 | # Merge with provided meta_data 71 | factor_scores <- factor_scores %>% 72 | base::as.data.frame() %>% 73 | tibble::rownames_to_column(sample_id_column) %>% 74 | dplyr::left_join(metadata, 75 | by = sample_id_column) %>% 76 | tidyr::pivot_longer(-colnames(metadata), 77 | names_to = "Factor") %>% 78 | dplyr::rename("sample" = sample_id_column) 79 | 80 | return(factor_scores) 81 | 82 | } 83 | 84 | #' Extract feature weights scores from a model in a tidy format for a factor of interest 85 | #' 86 | #' @description 87 | #' Generates a tidy dataframe of feature weights for factors of interest useful for plotting function and enrichment analysis 88 | #' 89 | #' @details 90 | #' 91 | #' This function simplifies the extraction of factor feature weights from MOFA models. 92 | #' 93 | #' @param model A MOFA2 model. 94 | #' @param factor A string character to define the factor to be extracted. 95 | #' 96 | #' 97 | #' @return A dataframe in a tidy format containing the factor feature weights 98 | #' @export 99 | #' 100 | #' @import MOFA2 101 | #' @import dplyr 102 | #' @import purrr 103 | #' 104 | #' @examples 105 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 106 | #' model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 107 | #' gene_weights <- get_geneweights(model = model, 108 | #' factor = "Factor1") 109 | get_geneweights <- function(model, factor) { 110 | 111 | factor_loadings <- MOFA2::get_weights(model, as.data.frame = T) %>% 112 | base::as.data.frame() %>% 113 | dplyr::mutate(feature = purrr::map2_chr(view, feature, function(v, f) { 114 | 115 | gsub(paste0(v, "_"), "",f) 116 | 117 | })) %>% 118 | dplyr::rename("ctype" = view) %>% 119 | dplyr::rename("factors" = factor) %>% 120 | dplyr::filter(factors %in% factor) %>% 121 | dplyr::select(-factors) 122 | 123 | return(factor_loadings) 124 | 125 | } 126 | -------------------------------------------------------------------------------- /R/normalization.R: -------------------------------------------------------------------------------- 1 | #' TMM normalization of single cell data sets 2 | #' 3 | #' @description 4 | #' Performs for a list of SummarizedExperiments, TMM normalization scaled by a factor specified by the user. 5 | #' 6 | #' @details 7 | #' 8 | #' This function estimates TMM normalization factors and normalizes a list of gene count matrices, data 9 | #' is additionally scaled using a factor specified by the user and `log1p()` transformed 10 | #' 11 | #' @param pb_dat_list List of SummarizedExperiment generated from `MOFAcellulaR::filt_profiles()` 12 | #' @param scale_factor Numeric. Scaled counts are multiplied by this factor before log transformation 13 | #' @return A named list of SummarizedExperiments per cell type provided with normalized log transformed data in their `logcounts` assay 14 | #' @export 15 | #' 16 | #' @import SummarizedExperiment 17 | #' @import S4Vectors 18 | #' @import edgeR 19 | #' @import purrr 20 | #' 21 | #' @examples 22 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 23 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 24 | #' load(file.path(inputs_dir, "testcoldata.rda")) 25 | #' 26 | #' pb_obj <- create_init_exp(counts = testpbcounts, 27 | #' coldata = testcoldata) 28 | #' 29 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 30 | #' cts = c("Fib","CM"), 31 | #' ncells = 5, 32 | #' counts_col = "cell_counts", 33 | #' ct_col = "cell_type") 34 | #' 35 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 36 | #' min.count = 5, 37 | #' min.prop = 0.25) 38 | #' 39 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 40 | #' scale_factor = 1000000) 41 | tmm_trns <- function(pb_dat_list, scale_factor = 1000000) { 42 | 43 | pb_dat_red <- purrr::map(pb_dat_list, function(x) { 44 | all_nf <- edgeR::calcNormFactors(x, method = "TMM") 45 | sfs <- all_nf$samples$lib.size * all_nf$samples$norm.factors 46 | pb <- base::sweep(assay(x, "counts"), MARGIN = 2, sfs, FUN = "/") 47 | SummarizedExperiment::assay(x, "logcounts") <- base::log1p(pb * scale_factor) 48 | 49 | return(x) 50 | 51 | }) 52 | 53 | return(pb_dat_red) 54 | 55 | } 56 | -------------------------------------------------------------------------------- /R/plot_MOFA_hmap.R: -------------------------------------------------------------------------------- 1 | #' Visualize MOFA multicellular model 2 | #' 3 | #' @description 4 | #' Plots a heatmap with factor scores annotated by sample's information and factor's statistics 5 | #' 6 | #' @details 7 | #' This function summarizes a `MOFA2` model by plotting and clustering the factor scores across samples. 8 | #' Additionally, it allows you to annotate each sample with categorical or continous variables. Finally, 9 | #' for each factor, the amount of explained variance captured for each cell type is shown. If provided, 10 | #' summary of the association statistics and sample variables can be provided 11 | #' 12 | #' @param model A MOFA2 model. 13 | #' @param group Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided. 14 | #' @param metadata A data frame containing the annotations of the samples included in the MOFA model. 15 | #' @param sample_id_column A string character that refers to the column in `metadata` where the sample identifier is located. 16 | #' @param sample_anns A vector containing strings that refer to the columns in `metadata` to be used to annotate samples 17 | #' @param assoc_list A named list collecting results of `MOFAcellulaR::get_associations()` 18 | #' @param col_rows A named list of lists containing at the first index, all sample_anns used, 19 | #' and at the second index, all levels of an annotation within the first index. 20 | #' As required by `ComplexHeatmap::rowAnnotation` col parameter. 21 | #' 22 | #' @return A dataframe in a tidy format containing the manifold and the scatter plot 23 | #' @export 24 | #' 25 | #' @import MOFA2 26 | #' @import tibble 27 | #' @import dplyr 28 | #' @import purrr 29 | #' @import tidyr 30 | #' @import circlize 31 | #' @import grDevices 32 | #' @import ComplexHeatmap 33 | #' @import grid 34 | #' 35 | #' @examples 36 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 37 | #' model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 38 | #' metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 39 | #' metadata$var <- stats::rnorm(nrow(metadata)) 40 | #' 41 | #' categorical_assoc <- get_associations(model = model, 42 | #' metadata = metadata, 43 | #' sample_id_column = "sample", 44 | #' test_variable = "patient_group", 45 | #' test_type = "categorical", 46 | #' group = FALSE) 47 | #' 48 | #' continuous_assoc <- get_associations(model = model, 49 | #' metadata = metadata, 50 | #' sample_id_column = "sample", 51 | #' test_variable = "var", 52 | #' test_type = "continous", 53 | #' group = FALSE) 54 | #' 55 | #' 56 | #' assoc_list = list("categorical" = categorical_assoc, "continous" = continuous_assoc) 57 | #' 58 | #' plot_MOFA_hmap(model = model, 59 | #' group = FALSE, 60 | #' metadata = metadata, 61 | #' sample_id_column = "sample", 62 | #' sample_anns = c("patient_group", "batch", "var"), 63 | #' assoc_list = assoc_list) 64 | 65 | plot_MOFA_hmap <- function(model, 66 | group = FALSE, 67 | metadata, 68 | sample_id_column = "sample", 69 | sample_anns, 70 | assoc_list = NULL, 71 | col_rows = NULL) { 72 | 73 | # Build general aesthetics 74 | # aesthetics definition of the borders 75 | ht_opt$ROW_ANNO_PADDING <- grid::unit(2.5, "mm") 76 | ht_opt$COLUMN_ANNO_PADDING <- grid::unit(2.5, "mm") 77 | 78 | # Get the factor matrix to plot 79 | factor_matrix <- MOFA2::get_factors(model, 80 | factors = "all") %>% 81 | do.call(rbind, .) 82 | 83 | # Gradient colors -------------------------------------------------------------- 84 | # For factor scores 85 | max_fact <- factor_matrix %>% 86 | max() %>% 87 | abs() 88 | 89 | col_fun_fact <- circlize::colorRamp2(seq((max_fact + 0.5) * -1, 90 | max_fact + 0.5, 91 | length = 50), 92 | grDevices::hcl.colors(50,"Green-Brown",rev = T)) 93 | 94 | # 1. Row annotations (patient categories) 95 | # Here we find the right order in the meta-data ----------------------------------- 96 | row_anns <- MOFA2::get_factors(model, factors = "all") %>% 97 | do.call(rbind, .) %>% 98 | rownames() %>% 99 | tibble::enframe(value = sample_id_column) %>% 100 | dplyr::select_at(sample_id_column) %>% 101 | dplyr::left_join(metadata, by = sample_id_column) %>% 102 | dplyr::select_at(sample_anns) %>% 103 | as.data.frame() 104 | 105 | # Patient annotations ------------------------------------------------------------- 106 | if(is.null(col_rows)) { 107 | 108 | row_ha <- ComplexHeatmap::rowAnnotation(df = as.data.frame(row_anns), 109 | gap = grid::unit(2.5, "mm"), 110 | border = TRUE) 111 | 112 | } else { 113 | 114 | row_ha <- ComplexHeatmap::rowAnnotation(df = as.data.frame(row_anns), 115 | gap = grid::unit(2.5, "mm"), 116 | border = TRUE, 117 | col = col_rows) 118 | 119 | } 120 | 121 | # 2. Column annotations (R2 and associations) 122 | 123 | # 2.1 R2 124 | # Add explain variance per view, it must be a matrix, with 125 | # factors in row (ordered) and views in columns 126 | r2_list <- model@cache$variance_explained$r2_per_factor 127 | 128 | # If you have a grouped model, then we rename the views with the grouping 129 | if(group) { 130 | 131 | names_groups <- names(r2_list) 132 | 133 | r2_list <- purrr::map2(r2_list, names_groups, function(dat, nam) { 134 | 135 | dat_mod <- dat 136 | colnames(dat_mod) <- paste0(nam,"_", colnames(dat_mod)) 137 | return(dat_mod) 138 | 139 | }) 140 | 141 | } 142 | 143 | # Finally, homogeneous processing 144 | r2_per_factor <- r2_list %>% 145 | do.call(base::cbind, .) 146 | 147 | # Gradient colors -------------------------------------------------------------- 148 | # For R2 149 | max_r2 <- r2_per_factor %>% 150 | max(na.rm = TRUE) 151 | 152 | if(max_r2 < 90) { # Add tolerance buffer 153 | col_fun_r2 <- circlize::colorRamp2(seq(0, max_r2 + 5, length = 50), 154 | grDevices::hcl.colors(50,"Oranges",rev = T)) 155 | } else { 156 | col_fun_r2 <- circlize::colorRamp2(seq(0, 100, length = 50), 157 | grDevices::hcl.colors(50,"Oranges",rev = T)) 158 | } 159 | 160 | 161 | # 2.2 Associations with covariates 162 | # If you provide a list of associations... 163 | if(!is.null(assoc_list)) { 164 | 165 | # This is a matrix, with factors in rows and p-values 166 | # for tested covariates in columns 167 | assoc_pvals <- assoc_list %>% 168 | tibble::enframe(name = "test") %>% 169 | tidyr::unnest(c(value)) %>% 170 | dplyr::mutate(log_adjpval = -log10(.data$adj_pvalue)) %>% 171 | dplyr::select(test, Factor, log_adjpval) %>% 172 | tidyr::pivot_wider(names_from = test, 173 | values_from = log_adjpval) %>% 174 | dplyr::select(-Factor) %>% 175 | as.matrix() 176 | 177 | # Define aesthetics so as not to repeat the calculation 178 | # Association p-values 179 | col_fun_assoc <- circlize::colorRamp2(seq(0, max(assoc_pvals) + 0.5, length = 20), 180 | grDevices::hcl.colors(20,"Purples",rev = T)) 181 | 182 | 183 | column_ha <- ComplexHeatmap::HeatmapAnnotation("R2" = r2_per_factor, 184 | "pvalue" = assoc_pvals, 185 | gap = grid::unit(2.5, "mm"), 186 | border = TRUE, 187 | col = list(R2 = col_fun_r2, 188 | pvalue = col_fun_assoc)) 189 | 190 | } else { # Just add R2 info 191 | 192 | column_ha <- ComplexHeatmap::HeatmapAnnotation("R2" = r2_per_factor, 193 | gap = grid::unit(2.5, "mm"), 194 | border = TRUE, 195 | col = list(R2 = col_fun_r2)) 196 | 197 | } 198 | 199 | # Build the final heatmap 200 | scores_hmap <- ComplexHeatmap::Heatmap(factor_matrix, 201 | name = "factor_scores", 202 | right_annotation = row_ha, 203 | top_annotation = column_ha, 204 | cluster_columns = FALSE, 205 | show_row_dend = TRUE, 206 | show_row_names = FALSE, 207 | border = TRUE, 208 | gap = grid::unit(2.5, "mm"), 209 | col = col_fun_fact) 210 | 211 | ComplexHeatmap::draw(scores_hmap) 212 | 213 | 214 | } 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | 225 | 226 | 227 | 228 | 229 | 230 | 231 | 232 | 233 | -------------------------------------------------------------------------------- /R/plot_sample_2D.R: -------------------------------------------------------------------------------- 1 | #' Visualize sample variability in a 2D space 2 | #' 3 | #' @description 4 | #' Performs dimensionality reduction of factor scores for the visualization of sample-level variability 5 | #' 6 | #' @details 7 | #' For a `MOFA2` model it performs a multidimensional scaling (MDS) 8 | #' or uniform manifold approximation and projection (UMAP) of the factor 9 | #' scores. It allows you to color samples based on a covariate available in 10 | #' your meta-data. 11 | #' 12 | #' @param model A MOFA2 model. 13 | #' @param group Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided. 14 | #' @param method A string specifying if "UMAP" or "MDS" should be performed 15 | #' @param metadata A data frame containing the annotations of the samples included in the MOFA model. 16 | #' @param sample_id_column A string character that refers to the column in `metadata` where the sample identifier is located. 17 | #' @param color_by A string character that refers to the column in `metadata` where the covariate to be tested is located. 18 | #' @param ... inherited parameters of `uwot::umap()` 19 | #' 20 | #' 21 | #' @return A dataframe in a tidy format containing the manifold and the scatter plot 22 | #' @export 23 | #' 24 | #' @import MOFA2 25 | #' @import tibble 26 | #' @import dplyr 27 | #' @import uwot 28 | #' @import stats 29 | #' @import ggplot2 30 | #' 31 | #' @examples 32 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 33 | #' model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 34 | #' metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 35 | #' UMAP_embedding <- plot_sample_2D(model = model, 36 | #' group = FALSE, 37 | #' method = "UMAP", 38 | #' metadata = metadata, 39 | #' sample_id_column = "sample", 40 | #' color_by = "batch") 41 | plot_sample_2D <- function(model, 42 | group = FALSE, 43 | method = "UMAP", 44 | metadata, 45 | sample_id_column = "sample", 46 | color_by, 47 | ...) { 48 | 49 | # Get the matrix of factor scores 50 | if(group){ 51 | 52 | factors <- MOFA2::get_factors(model, factors = "all") %>% 53 | base::do.call(base::rbind, .) 54 | 55 | } else { 56 | 57 | factors <- MOFA2::get_factors(model, factors = "all")[[1]] 58 | 59 | } 60 | 61 | # Make specific reductions 62 | if(method == "MDS") { 63 | # Using eucledian 64 | factors_mds <- stats::cmdscale(stats::dist(factors)) %>% 65 | base::as.data.frame() 66 | 67 | colnames(factors_mds) <- c("MDS1", "MDS2") 68 | 69 | factors_mds <- factors_mds %>% 70 | as.data.frame() %>% 71 | tibble::rownames_to_column(sample_id_column) %>% 72 | dplyr::left_join(metadata, by = sample_id_column) %>% 73 | dplyr::rename("color_col" = color_by) 74 | 75 | mds_plt <- ggplot2::ggplot(factors_mds, ggplot2::aes(x = .data$MDS1, 76 | y = .data$MDS2, 77 | color = .data$color_col)) + 78 | ggplot2::geom_point(size = 2.5) + 79 | ggplot2::theme_classic() + 80 | ggplot2::theme(axis.text = ggplot2::element_text(size =12)) + 81 | ggplot2::labs(color = "") 82 | 83 | plot(mds_plt) 84 | 85 | return(factors_mds) 86 | 87 | } else if(method == "UMAP") { 88 | # UMAP of factors 89 | factors_umap <- uwot::umap(factors, ...) #Inherited parameters 90 | colnames(factors_umap) <- c("UMAP_1", "UMAP_2") 91 | 92 | factors_umap <- factors_umap %>% 93 | as.data.frame() %>% 94 | tibble::rownames_to_column(sample_id_column) %>% 95 | dplyr::left_join(metadata, by = sample_id_column) %>% 96 | dplyr::rename("color_col" = color_by) 97 | 98 | umap_plt <- ggplot2::ggplot(factors_umap, 99 | ggplot2::aes(x = .data$UMAP_1, 100 | y = .data$UMAP_2, 101 | color = color_col)) + 102 | ggplot2::geom_point(size = 2.5) + 103 | ggplot2::theme_classic() + 104 | ggplot2::theme(axis.text = ggplot2::element_text(size =12)) + 105 | ggplot2::labs(color = "") 106 | 107 | plot(umap_plt) 108 | 109 | return(factors_umap) 110 | 111 | } 112 | 113 | } 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | -------------------------------------------------------------------------------- /R/project_data.R: -------------------------------------------------------------------------------- 1 | #' Project new data into a known factor space 2 | #' 3 | #' @description 4 | #' This function uses the feature weights learned by a MOFA2 model to project unseen data to the factor space 5 | #' 6 | #' @details 7 | #' This function calculates the pseudoinverse of the feature weights of a `MOFA2` model and uses it to 8 | #' project unseen data into the factor space. Only shared features are used for the projection 9 | #' 10 | #' @param model A MOFA2 model. 11 | #' @param test_data A dataframe in multiview representation as generated by `MOFAcellulaR::pb_dat2MOFA` 12 | #' 13 | #' @return A matrix with samples in rows and projected factors in columns 14 | #' @export 15 | #' 16 | #' @import MOFA2 17 | #' @import tibble 18 | #' @import dplyr 19 | #' @import tidyr 20 | #' @import MASS 21 | #' 22 | #' @examples 23 | #' inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 24 | #' load(file.path(inputs_dir, "testpbcounts.rda")) 25 | #' load(file.path(inputs_dir, "testcoldata.rda")) 26 | #' pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 27 | #' 28 | #' ct_list <- filt_profiles(pb_dat = pb_obj, 29 | #' cts = c("Fib","CM"), 30 | #' ncells = 5, 31 | #' counts_col = "cell_counts", 32 | #' ct_col = "cell_type") 33 | #' 34 | #' ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 35 | #' min.count = 5, 36 | #' min.prop = 0.25) 37 | #' 38 | #' ct_list <- tmm_trns(pb_dat_list = ct_list, 39 | #' scale_factor = 1000000) 40 | #' 41 | #' ct_list <- center_views(pb_dat_list = ct_list) 42 | #' 43 | #' multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 44 | #' 45 | #' trained_model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 46 | #' 47 | #' projected_factors <- project_data(model = trained_model, test_data = multiview_dat) 48 | #' 49 | #' 50 | project_data <- function(model, test_data) { 51 | 52 | # 1. get loading matrix 53 | loading_matrix <- MOFA2::get_weights(model, 54 | as.data.frame = T) %>% 55 | base::as.data.frame() %>% 56 | dplyr::select(-view) %>% 57 | tidyr::pivot_wider(names_from = feature) %>% 58 | tibble::column_to_rownames("factor") %>% 59 | base::as.matrix() 60 | 61 | # 2. calculate pseudo-inverse 62 | # returns features in rows, factors in columns 63 | inv_loading <- MASS::ginv(loading_matrix) 64 | rownames(inv_loading) <- colnames(loading_matrix) 65 | colnames(inv_loading) <- rownames(loading_matrix) 66 | 67 | # 3. prepare test data 68 | # test data should be transposed so as to have patients in rows, features in columns 69 | test_data_mat <- test_data %>% 70 | dplyr::select(-view) %>% 71 | tidyr::pivot_wider(names_from = sample, 72 | values_from = value, 73 | values_fill = 0) %>% 74 | tibble::column_to_rownames("feature") %>% 75 | base::as.matrix() %>% 76 | base::t() 77 | 78 | # 4. check shared features 79 | shared_features <- intersect(rownames(inv_loading), 80 | colnames(test_data_mat)) 81 | 82 | inv_loading <- inv_loading[shared_features, ] 83 | 84 | test_data_mat <- test_data_mat[, shared_features] 85 | 86 | # 5. project test data 87 | projected_factors <- test_data_mat %*% inv_loading 88 | 89 | return(projected_factors) 90 | } 91 | -------------------------------------------------------------------------------- /R/utils-pipe.R: -------------------------------------------------------------------------------- 1 | #' Pipe operator 2 | #' 3 | #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 4 | #' 5 | #' @name %>% 6 | #' @rdname pipe 7 | #' @keywords internal 8 | #' @export 9 | #' @importFrom magrittr %>% 10 | #' @usage lhs \%>\% rhs 11 | #' @param lhs A value or the magrittr placeholder. 12 | #' @param rhs A function call using the magrittr semantics. 13 | #' @return The result of calling `rhs(lhs)`. 14 | NULL 15 | -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | --- 4 | 5 | 6 | 7 | ```{r, include = FALSE} 8 | knitr::opts_chunk$set( 9 | collapse = TRUE, 10 | comment = "#>", 11 | out.width = "100%" 12 | ) 13 | ``` 14 | 15 | ## Overview 16 | 17 | Cross-condition single-cell atlases are essential in the characterization of human disease. In these complex experimental designs, patient samples are profiled across distinct cell types and clinical conditions to describe disease processes at the cellular level. However, most of the current analysis tools are limited to pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes and the effects of other biological and technical factors in the variation of gene expression. Here we propose a computational framework for an unsupervised analysis of samples from cross-condition single cell atlases and for the identification of multicellular programs associated with disease. Our framework based on probabilistic factor analysis implemented in [MOFA](https://www.embopress.org/doi/full/10.15252/msb.20178124) and [MOFA+](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) incorporates the variation of patient samples across cell types and allows the joint analysis of independent patient cohorts facilitating study integration. 18 | 19 | `MOFAcellulaR` is package that facilitates the implementation of MOFA models to single cell data 20 | 21 | 22 | 23 | ## Installation 24 | 25 | You can install the latest stable and development versions from GitHub with `remotes`: 26 | 27 | - stable 28 | 29 | ```{r github_install, eval=FALSE} 30 | # install.packages("remotes") 31 | remotes::install_github("saezlab/MOFAcellulaR") 32 | ``` 33 | 34 | ## Usage 35 | 36 | Start by reading `vignette("MOFAcellulaR")` to learn how to use the helping functions of **MOFAcellulaR** to run your MOFA models. 37 | 38 | ## Citation 39 | If you use **MOFAcellulaR** for your research please cite the [following publication](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1): 40 | 41 | > Ramirez-Flores RO, Lanzer JD, Dimitrov D, Velten B, Saez-Rodriguez J. “Multicellular factor analysis for a tissue-centric understanding of disease” BioRxiv. 2023. DOI: [10.1101/2023.02.23.529642](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1) 42 | 43 | Also, don't forget to cite MOFA's original publications 44 | 45 | >Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC & Stegle O (2020) MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol 21: 111 46 | 47 | >Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W & Stegle O (2018) Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol 14: e8124 48 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ## Overview 5 | 6 | Cross-condition single-cell atlases are essential in the 7 | characterization of human disease. In these complex experimental 8 | designs, patient samples are profiled across distinct cell types and 9 | clinical conditions to describe disease processes at the cellular level. 10 | However, most of the current analysis tools are limited to pairwise 11 | cross-condition comparisons, disregarding the multicellular nature of 12 | disease processes and the effects of other biological and technical 13 | factors in the variation of gene expression. Here we propose a 14 | computational framework for an unsupervised analysis of samples from 15 | cross-condition single cell atlases and for the identification of 16 | multicellular programs associated with disease. Our framework based on 17 | probabilistic factor analysis implemented in 18 | [MOFA](https://www.embopress.org/doi/full/10.15252/msb.20178124) and 19 | [MOFA+](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) 20 | incorporates the variation of patient samples across cell types and 21 | allows the joint analysis of independent patient cohorts facilitating 22 | study integration. 23 | 24 | `MOFAcellulaR` is package that facilitates the implementation of MOFA 25 | models to single cell data 26 | 27 | 28 | 29 | ## Installation 30 | 31 | You can install the latest stable and development versions from GitHub 32 | with `remotes`: 33 | 34 | - stable 35 | 36 | ``` r 37 | # install.packages("remotes") 38 | remotes::install_github("saezlab/MOFAcellulaR") 39 | ``` 40 | 41 | ## Usage 42 | 43 | Start by reading `vignette("MOFAcellulaR")` to learn how to use the 44 | helping functions of **MOFAcellulaR** to run your MOFA models. 45 | 46 | ## Citation 47 | 48 | If you use **MOFAcellulaR** for your research please cite the [following 49 | publication](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1): 50 | 51 | > Ramirez-Flores RO, Lanzer JD, Dimitrov D, Velten B, Saez-Rodriguez J. 52 | > “Multicellular factor analysis for a tissue-centric understanding of 53 | > disease” BioRxiv. 2023. DOI: 54 | > [10.1101/2023.02.23.529642](https://www.biorxiv.org/content/10.1101/2023.02.23.529642v1) 55 | 56 | Also, don’t forget to cite MOFA’s original publications 57 | 58 | > Argelaguet R, Arnol D, Bredikhin D, Deloro Y, Velten B, Marioni JC & 59 | > Stegle O (2020) MOFA+: a statistical framework for comprehensive 60 | > integration of multi-modal single-cell data. Genome Biol 21: 111 61 | 62 | > Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, 63 | > Buettner F, Huber W & Stegle O (2018) Multi-Omics Factor Analysis-a 64 | > framework for unsupervised integration of multi-omics data sets. Mol 65 | > Syst Biol 14: e8124 66 | -------------------------------------------------------------------------------- /_pkgdown.yml: -------------------------------------------------------------------------------- 1 | url: https://saezlab.github.io/MOFAcellulaR/ 2 | template: 3 | bootstrap: 5 4 | 5 | -------------------------------------------------------------------------------- /inst/extdata/testcoldata.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saezlab/MOFAcellulaR/8cb0785989281f23c1b941db202442624ea9d6bd/inst/extdata/testcoldata.rda -------------------------------------------------------------------------------- /inst/extdata/testmetadata.rds: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saezlab/MOFAcellulaR/8cb0785989281f23c1b941db202442624ea9d6bd/inst/extdata/testmetadata.rds -------------------------------------------------------------------------------- /inst/extdata/testmodel.hdf5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saezlab/MOFAcellulaR/8cb0785989281f23c1b941db202442624ea9d6bd/inst/extdata/testmodel.hdf5 -------------------------------------------------------------------------------- /inst/extdata/testpbcounts.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/saezlab/MOFAcellulaR/8cb0785989281f23c1b941db202442624ea9d6bd/inst/extdata/testpbcounts.rda -------------------------------------------------------------------------------- /man/center_views.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/create_init_exp.R 3 | \name{center_views} 4 | \alias{center_views} 5 | \title{Center view-data} 6 | \usage{ 7 | center_views(pb_dat_list) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | } 12 | \value{ 13 | A named list of SummarizedExperiments per cell type provided with centered pseudobulk profiles 14 | } 15 | \description{ 16 | Centers each element of a list of SummarizedExperiments 17 | } 18 | \details{ 19 | Given that the MOFA model in general uses centered data, when interested in projecting new 20 | data to a new manifold, it is needed to perform centering. 21 | } 22 | \examples{ 23 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 24 | load(file.path(inputs_dir, "testpbcounts.rda")) 25 | load(file.path(inputs_dir, "testcoldata.rda")) 26 | pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 27 | 28 | ct_list <- filt_profiles(pb_dat = pb_obj, 29 | cts = c("Fib","CM"), 30 | ncells = 5, 31 | counts_col = "cell_counts", 32 | ct_col = "cell_type") 33 | 34 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 35 | min.count = 5, 36 | min.prop = 0.25) 37 | 38 | ct_list <- tmm_trns(pb_dat_list = ct_list, 39 | scale_factor = 1000000) 40 | 41 | ct_list <- center_views(pb_dat_list = ct_list) 42 | 43 | multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 44 | } 45 | -------------------------------------------------------------------------------- /man/create_init_exp.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/create_init_exp.R 3 | \name{create_init_exp} 4 | \alias{create_init_exp} 5 | \title{Single-cell SummarizedExperiment object} 6 | \usage{ 7 | create_init_exp(counts, coldata) 8 | } 9 | \arguments{ 10 | \item{counts}{Named numeric matrix with features in rows and samples in columns.} 11 | 12 | \item{coldata}{A data frame containing the annotations of the samples.} 13 | } 14 | \value{ 15 | SummarizedExperiment with provided data 16 | } 17 | \description{ 18 | Creates a SummarizedExperiment object necessary to build a multi-view representation. 19 | } 20 | \details{ 21 | This function is the first step for a multicellular factor analysis. 22 | It collects in a single object the pseudobulk counts of a single cell experiment 23 | and its annotations. 24 | } 25 | \examples{ 26 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 27 | load(file.path(inputs_dir, "testpbcounts.rda")) 28 | load(file.path(inputs_dir, "testcoldata.rda")) 29 | pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 30 | } 31 | -------------------------------------------------------------------------------- /man/filt_gex_bybckgrnd.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_adv.R 3 | \name{filt_gex_bybckgrnd} 4 | \alias{filt_gex_bybckgrnd} 5 | \title{Filter background expression of marker genes} 6 | \usage{ 7 | filt_gex_bybckgrnd(pb_dat_list, prior_mrks) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{prior_mrks}{A named list providing marker genes per cell type. Names should be identical as in \code{pb_dat_list}} 13 | } 14 | \value{ 15 | A named list of SummarizedExperiments per cell type provided with filtered normalized log transformed data in their \code{logcounts} assay 16 | } 17 | \description{ 18 | For a collection of matrices, we exclude features that are considered background based on prior 19 | knowledge of marker genes 20 | } 21 | \details{ 22 | Performs filtering of highly variable genes (after data transformation). 23 | This is based on marker genes. The assumption is that background gene expression can be traced 24 | by expression of cell type marker genes in cell types which shouldn't express the gene. 25 | Marker genes will be only kept in the matrix if they are expressed in the expected cell type 26 | } 27 | \examples{ 28 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 29 | load(file.path(inputs_dir, "testpbcounts.rda")) 30 | load(file.path(inputs_dir, "testcoldata.rda")) 31 | 32 | pb_obj <- create_init_exp(counts = testpbcounts, 33 | coldata = testcoldata) 34 | 35 | ct_list <- filt_profiles(pb_dat = pb_obj, 36 | cts = c("Fib","CM"), 37 | ncells = 5, 38 | counts_col = "cell_counts", 39 | ct_col = "cell_type") 40 | 41 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 42 | min.count = 5, 43 | min.prop = 0.25) 44 | 45 | ct_list <- tmm_trns(pb_dat_list = ct_list, 46 | scale_factor = 1000000) 47 | 48 | ct_list <- filt_gex_byhvg(pb_dat_list = ct_list, 49 | prior_hvg = NULL, 50 | var.threshold = 0) 51 | 52 | prior_hvg_test <- list("CM" = c("TTN"), 53 | "Fib" = c("POSTN")) 54 | 55 | ct_list <- filt_gex_bybckgrnd(pb_dat_list = ct_list, 56 | prior_mrks = prior_hvg_test) 57 | 58 | } 59 | -------------------------------------------------------------------------------- /man/filt_gex_byexpr.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_basic.R 3 | \name{filt_gex_byexpr} 4 | \alias{filt_gex_byexpr} 5 | \title{Filter lowly expressed genes from pseudobulk profiles} 6 | \usage{ 7 | filt_gex_byexpr(pb_dat_list, min.count, min.prop) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{min.count}{Numeric, minimum counts per sample to be considered. Check \code{?edgeR::filterByExpr()} for details.} 13 | 14 | \item{min.prop}{Numeric, minimum proportion of samples containing the minimum counts. Check \code{?edgeR::filterByExpr()} for details.} 15 | } 16 | \value{ 17 | A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 18 | } 19 | \description{ 20 | Filter lowly expressed genes from pseudobulk profiles using \code{edgeR}. 21 | } 22 | \details{ 23 | This function wraps \code{edgeR::filterByExpr()} to be applied to lists of SummarizedExperiments.It assumes 24 | that all samples are part of the same group. 25 | } 26 | \examples{ 27 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 28 | load(file.path(inputs_dir, "testpbcounts.rda")) 29 | load(file.path(inputs_dir, "testcoldata.rda")) 30 | 31 | pb_obj <- create_init_exp(counts = testpbcounts, 32 | coldata = testcoldata) 33 | 34 | ct_list <- filt_profiles(pb_dat = pb_obj, 35 | cts = c("Fib","CM"), 36 | ncells = 5, 37 | counts_col = "cell_counts", 38 | ct_col = "cell_type") 39 | 40 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 41 | min.count = 5, 42 | min.prop = 0.25) 43 | } 44 | -------------------------------------------------------------------------------- /man/filt_gex_byhvg.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_adv.R 3 | \name{filt_gex_byhvg} 4 | \alias{filt_gex_byhvg} 5 | \title{Indentify highly variable genes} 6 | \usage{ 7 | filt_gex_byhvg(pb_dat_list, prior_hvg = NULL, var.threshold = 1) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{prior_hvg}{NULL by default. Alternatively, a named list with a character vector containing features to select.} 13 | 14 | \item{var.threshold}{Numeric. Inherited from \code{scran::getTopHVGs()}. Minimum threshold on the metric of variation} 15 | } 16 | \value{ 17 | A named list of SummarizedExperiments per cell type provided with filtered normalized log transformed data in their \code{logcounts} assay 18 | } 19 | \description{ 20 | Identifies highly variable features from a log-normalized count matrix or filters matrices by a list of genes 21 | provided by the user. 22 | } 23 | \details{ 24 | This function estimates highly variable genes per cell type using \code{scran::getTopHVGs}. Alternatively, this function 25 | allows the user to provide the features to be used in each cell type. If prior genes are used, for cell types 26 | where this information is missing, highly variable genes will be calculated 27 | } 28 | \examples{ 29 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 30 | load(file.path(inputs_dir, "testpbcounts.rda")) 31 | load(file.path(inputs_dir, "testcoldata.rda")) 32 | 33 | pb_obj <- create_init_exp(counts = testpbcounts, 34 | coldata = testcoldata) 35 | 36 | ct_list <- filt_profiles(pb_dat = pb_obj, 37 | cts = c("Fib","CM"), 38 | ncells = 5, 39 | counts_col = "cell_counts", 40 | ct_col = "cell_type") 41 | 42 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 43 | min.count = 5, 44 | min.prop = 0.25) 45 | 46 | ct_list <- tmm_trns(pb_dat_list = ct_list, 47 | scale_factor = 1000000) 48 | 49 | ct_list <- filt_gex_byhvg(pb_dat_list = ct_list, 50 | prior_hvg = NULL, 51 | var.threshold = 0) 52 | 53 | } 54 | -------------------------------------------------------------------------------- /man/filt_profiles.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_basic.R 3 | \name{filt_profiles} 4 | \alias{filt_profiles} 5 | \title{Filter pseudobulk profiles} 6 | \usage{ 7 | filt_profiles( 8 | pb_dat, 9 | cts, 10 | ncells = 50, 11 | counts_col = "cell_counts", 12 | ct_col = "cell_type" 13 | ) 14 | } 15 | \arguments{ 16 | \item{pb_dat}{SummarizedExperiment generated from \code{MOFAcellulaR::create_init_exp()}} 17 | 18 | \item{cts}{A vector containing the names of cells to be used in the analysis} 19 | 20 | \item{ncells}{Number of minimum cells of each pseudobulk profile} 21 | 22 | \item{counts_col}{String pointing to the column in \code{colData(pb_dat)} where the number of cells per pseudobulk was stored} 23 | 24 | \item{ct_col}{String pointing to the column in \code{colData(pb_dat)} where the cell-type category per pseudobulk was stored} 25 | } 26 | \value{ 27 | A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 28 | } 29 | \description{ 30 | Filter pseudobulk profiles of specific cell types based on the number of cells from which they were generated. 31 | } 32 | \details{ 33 | This function assumes that you have a SummarizedExperiment object 34 | with information in \code{colData(object)} specifying the number of cells used 35 | for each profile and the cell-type grouping this profiles. The function then will 36 | select only the cell-types provided by the user and filter the profiles 37 | with less cells as the ones specified. 38 | } 39 | \examples{ 40 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 41 | load(file.path(inputs_dir, "testpbcounts.rda")) 42 | load(file.path(inputs_dir, "testcoldata.rda")) 43 | 44 | pb_obj <- create_init_exp(counts = testpbcounts, 45 | coldata = testcoldata) 46 | 47 | ct_list <- filt_profiles(pb_dat = pb_obj, 48 | cts = c("Fib","CM"), 49 | ncells = 5, 50 | counts_col = "cell_counts", 51 | ct_col = "cell_type") 52 | 53 | } 54 | -------------------------------------------------------------------------------- /man/filt_samples_bycov.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_basic.R 3 | \name{filt_samples_bycov} 4 | \alias{filt_samples_bycov} 5 | \title{Filter samples that have a limited coverage of features in a view} 6 | \usage{ 7 | filt_samples_bycov(pb_dat_list, prop_coverage = 0.9) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{prop_coverage}{Numeric, minimum proportion of coverage of features (different from 0).} 13 | } 14 | \value{ 15 | A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 16 | } 17 | \description{ 18 | Excludes samples from views with a coverage lower than the one stated 19 | } 20 | \details{ 21 | This function allows the user to exclude samples that have a limited number of features that could create potential outliers 22 | } 23 | \examples{ 24 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 25 | load(file.path(inputs_dir, "testpbcounts.rda")) 26 | load(file.path(inputs_dir, "testcoldata.rda")) 27 | 28 | pb_obj <- create_init_exp(counts = testpbcounts, 29 | coldata = testcoldata) 30 | 31 | ct_list <- filt_profiles(pb_dat = pb_obj, 32 | cts = c("Fib","CM"), 33 | ncells = 5, 34 | counts_col = "cell_counts", 35 | ct_col = "cell_type") 36 | 37 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 38 | min.count = 5, 39 | min.prop = 0.25) 40 | 41 | ct_list <- filt_views_bygenes(pb_dat_list = ct_list, 42 | ngenes = 15) 43 | 44 | ct_list <- filt_samples_bycov(pb_dat_list = ct_list, 45 | prop_coverage = 0.9) 46 | 47 | 48 | } 49 | -------------------------------------------------------------------------------- /man/filt_views_bygenes.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_basic.R 3 | \name{filt_views_bygenes} 4 | \alias{filt_views_bygenes} 5 | \title{Filter views with not enough features} 6 | \usage{ 7 | filt_views_bygenes(pb_dat_list, ngenes) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{ngenes}{Numeric, minimum number of features per view.} 13 | } 14 | \value{ 15 | A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 16 | } 17 | \description{ 18 | Filter complete views based on their number of profiled features 19 | } 20 | \details{ 21 | This function allows the user to control the number of minimum features per view 22 | } 23 | \examples{ 24 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 25 | load(file.path(inputs_dir, "testpbcounts.rda")) 26 | load(file.path(inputs_dir, "testcoldata.rda")) 27 | 28 | pb_obj <- create_init_exp(counts = testpbcounts, 29 | coldata = testcoldata) 30 | 31 | ct_list <- filt_profiles(pb_dat = pb_obj, 32 | cts = c("Fib","CM"), 33 | ncells = 5, 34 | counts_col = "cell_counts", 35 | ct_col = "cell_type") 36 | 37 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 38 | min.count = 5, 39 | min.prop = 0.25) 40 | 41 | ct_list <- filt_views_bygenes(pb_dat_list = ct_list, 42 | ngenes = 15) 43 | 44 | } 45 | -------------------------------------------------------------------------------- /man/filt_views_bysamples.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/filtering_basic.R 3 | \name{filt_views_bysamples} 4 | \alias{filt_views_bysamples} 5 | \title{Filter views with not enough samples} 6 | \usage{ 7 | filt_views_bysamples(pb_dat_list, nsamples) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{nsamples}{Numeric, minimum number of samples per view.} 13 | } 14 | \value{ 15 | A named list of SummarizedExperiments per cell type provided with filtered pseudobulk profiles 16 | } 17 | \description{ 18 | Filter complete views based on their number of profiled samples 19 | } 20 | \details{ 21 | This function allows the user to control the number of minimum samples per view 22 | } 23 | \examples{ 24 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 25 | load(file.path(inputs_dir, "testpbcounts.rda")) 26 | load(file.path(inputs_dir, "testcoldata.rda")) 27 | 28 | pb_obj <- create_init_exp(counts = testpbcounts, 29 | coldata = testcoldata) 30 | 31 | ct_list <- filt_profiles(pb_dat = pb_obj, 32 | cts = c("Fib","CM"), 33 | ncells = 5, 34 | counts_col = "cell_counts", 35 | ct_col = "cell_type") 36 | 37 | ct_list <- filt_views_bysamples(pb_dat_list = ct_list, 38 | nsamples = 2) 39 | 40 | } 41 | -------------------------------------------------------------------------------- /man/get_associations.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/get_associations.R 3 | \name{get_associations} 4 | \alias{get_associations} 5 | \title{Associate factors to covariates of interest} 6 | \usage{ 7 | get_associations( 8 | model, 9 | metadata, 10 | sample_id_column, 11 | test_variable, 12 | test_type = "categorical", 13 | categorical_type = "parametric", 14 | group = FALSE 15 | ) 16 | } 17 | \arguments{ 18 | \item{model}{A MOFA2 model.} 19 | 20 | \item{metadata}{A data frame containing the annotations of the samples included in the MOFA model.} 21 | 22 | \item{sample_id_column}{A string character that refers to the column in \code{metadata} where the sample identifier is located.} 23 | 24 | \item{test_variable}{A string character that refers to the column in \code{metadata} where the covariate to be tested is located.} 25 | 26 | \item{test_type}{A string character ("categorical", "continuous").} 27 | 28 | \item{categorical_type}{A string character ("parametric", "not_parametric"), only applies for categorical data.} 29 | 30 | \item{group}{Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided.} 31 | } 32 | \value{ 33 | A dataframe in a tidy format containing the p-values of the association tests per factor 34 | } 35 | \description{ 36 | Performs Analysis of Variance (ANOVA) or linear models to associate 37 | factor scores with covariates of interest provided by the user 38 | } 39 | \details{ 40 | Given a covariate of interest and a defined test, this function tests for associations with factor scores. For 41 | categorical tests, ANOVAs are fitted, while for continous variables, linear models. P-values are corrected 42 | using the Benjamini-Hochberg procedure. 43 | } 44 | \examples{ 45 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 46 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 47 | metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 48 | metadata$var <- stats::rnorm(nrow(metadata)) 49 | 50 | categorical_assoc <- get_associations(model = model, 51 | metadata = metadata, 52 | sample_id_column = "sample", 53 | test_variable = "patient_group", 54 | test_type = "categorical", 55 | group = FALSE) 56 | 57 | continuous_assoc <- get_associations(model = model, 58 | metadata = metadata, 59 | sample_id_column = "sample", 60 | test_variable = "var", 61 | test_type = "continuous", 62 | group = FALSE) 63 | } 64 | -------------------------------------------------------------------------------- /man/get_geneweights.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/get_tidy_factors.R 3 | \name{get_geneweights} 4 | \alias{get_geneweights} 5 | \title{Extract feature weights scores from a model in a tidy format for a factor of interest} 6 | \usage{ 7 | get_geneweights(model, factor) 8 | } 9 | \arguments{ 10 | \item{model}{A MOFA2 model.} 11 | 12 | \item{factor}{A string character to define the factor to be extracted.} 13 | } 14 | \value{ 15 | A dataframe in a tidy format containing the factor feature weights 16 | } 17 | \description{ 18 | Generates a tidy dataframe of feature weights for factors of interest useful for plotting function and enrichment analysis 19 | } 20 | \details{ 21 | This function simplifies the extraction of factor feature weights from MOFA models. 22 | } 23 | \examples{ 24 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 25 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 26 | gene_weights <- get_geneweights(model = model, 27 | factor = "Factor1") 28 | } 29 | -------------------------------------------------------------------------------- /man/get_tidy_factors.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/get_tidy_factors.R 3 | \name{get_tidy_factors} 4 | \alias{get_tidy_factors} 5 | \title{Extract factor scores from a model in a tidy format with meta data} 6 | \usage{ 7 | get_tidy_factors( 8 | model, 9 | metadata, 10 | factor, 11 | group = FALSE, 12 | sample_id_column = "sample" 13 | ) 14 | } 15 | \arguments{ 16 | \item{model}{A MOFA2 model.} 17 | 18 | \item{metadata}{A data frame containing the annotations of the samples included in the MOFA model.} 19 | 20 | \item{factor}{A string character to define the factor to be extracted. Alternatively, "all" to get all factors.} 21 | 22 | \item{group}{Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided.} 23 | 24 | \item{sample_id_column}{A string character that refers to the column in \code{metadata} where the sample identifier is located.} 25 | } 26 | \value{ 27 | A dataframe in a tidy format containing the factor scores per sample together with metadata 28 | } 29 | \description{ 30 | Generates a tidy dataframe for factors of interest useful for plotting functions. 31 | } 32 | \details{ 33 | This function simplifies the extraction of factor scores from MOFA models. 34 | } 35 | \examples{ 36 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 37 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 38 | metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 39 | all_factors <- get_tidy_factors(model = model, 40 | metadata = metadata, 41 | factor = "all", 42 | sample_id_column = "sample") 43 | Factor3 <- get_tidy_factors(model = model, 44 | metadata = metadata, 45 | factor = "Factor3", 46 | sample_id_column = "sample") 47 | } 48 | -------------------------------------------------------------------------------- /man/pb_dat2MOFA.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/create_init_exp.R 3 | \name{pb_dat2MOFA} 4 | \alias{pb_dat2MOFA} 5 | \title{Create MOFA-ready dataframe} 6 | \usage{ 7 | pb_dat2MOFA(pb_dat_list, sample_column = "donor_id") 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | } 12 | \value{ 13 | Data frame in a multiview representation 14 | } 15 | \description{ 16 | Creates from a list of SummarizedExperiments a multi-view representation for MOFA 17 | } 18 | \details{ 19 | This function is the last data preparation step for a multicellular factor analysis. 20 | It collects a collection of cell-type-specific SummarizedExperiments into a 21 | single data frame ready to be used in MOFA. Features are modified 22 | so as to reflect their cell type of origin. 23 | } 24 | \examples{ 25 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 26 | load(file.path(inputs_dir, "testpbcounts.rda")) 27 | load(file.path(inputs_dir, "testcoldata.rda")) 28 | pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 29 | 30 | ct_list <- filt_profiles(pb_dat = pb_obj, 31 | cts = c("Fib","CM"), 32 | ncells = 5, 33 | counts_col = "cell_counts", 34 | ct_col = "cell_type") 35 | 36 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 37 | min.count = 5, 38 | min.prop = 0.25) 39 | 40 | ct_list <- tmm_trns(pb_dat_list = ct_list, 41 | scale_factor = 1000000) 42 | 43 | multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 44 | } 45 | -------------------------------------------------------------------------------- /man/pipe.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils-pipe.R 3 | \name{\%>\%} 4 | \alias{\%>\%} 5 | \title{Pipe operator} 6 | \usage{ 7 | lhs \%>\% rhs 8 | } 9 | \arguments{ 10 | \item{lhs}{A value or the magrittr placeholder.} 11 | 12 | \item{rhs}{A function call using the magrittr semantics.} 13 | } 14 | \value{ 15 | The result of calling \code{rhs(lhs)}. 16 | } 17 | \description{ 18 | See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 19 | } 20 | \keyword{internal} 21 | -------------------------------------------------------------------------------- /man/plot_MOFA_hmap.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/plot_MOFA_hmap.R 3 | \name{plot_MOFA_hmap} 4 | \alias{plot_MOFA_hmap} 5 | \title{Visualize MOFA multicellular model} 6 | \usage{ 7 | plot_MOFA_hmap( 8 | model, 9 | group = FALSE, 10 | metadata, 11 | sample_id_column = "sample", 12 | sample_anns, 13 | assoc_list = NULL, 14 | col_rows = NULL 15 | ) 16 | } 17 | \arguments{ 18 | \item{model}{A MOFA2 model.} 19 | 20 | \item{group}{Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided.} 21 | 22 | \item{metadata}{A data frame containing the annotations of the samples included in the MOFA model.} 23 | 24 | \item{sample_id_column}{A string character that refers to the column in \code{metadata} where the sample identifier is located.} 25 | 26 | \item{sample_anns}{A vector containing strings that refer to the columns in \code{metadata} to be used to annotate samples} 27 | 28 | \item{assoc_list}{A named list collecting results of \code{MOFAcellulaR::get_associations()}} 29 | 30 | \item{col_rows}{A named list of lists containing at the first index, all sample_anns used, 31 | and at the second index, all levels of an annotation within the first index. 32 | As required by \code{ComplexHeatmap::rowAnnotation} col parameter.} 33 | } 34 | \value{ 35 | A dataframe in a tidy format containing the manifold and the scatter plot 36 | } 37 | \description{ 38 | Plots a heatmap with factor scores annotated by sample's information and factor's statistics 39 | } 40 | \details{ 41 | This function summarizes a \code{MOFA2} model by plotting and clustering the factor scores across samples. 42 | Additionally, it allows you to annotate each sample with categorical or continous variables. Finally, 43 | for each factor, the amount of explained variance captured for each cell type is shown. If provided, 44 | summary of the association statistics and sample variables can be provided 45 | } 46 | \examples{ 47 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 48 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 49 | metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 50 | metadata$var <- stats::rnorm(nrow(metadata)) 51 | 52 | categorical_assoc <- get_associations(model = model, 53 | metadata = metadata, 54 | sample_id_column = "sample", 55 | test_variable = "patient_group", 56 | test_type = "categorical", 57 | group = FALSE) 58 | 59 | continuous_assoc <- get_associations(model = model, 60 | metadata = metadata, 61 | sample_id_column = "sample", 62 | test_variable = "var", 63 | test_type = "continous", 64 | group = FALSE) 65 | 66 | 67 | assoc_list = list("categorical" = categorical_assoc, "continous" = continuous_assoc) 68 | 69 | plot_MOFA_hmap(model = model, 70 | group = FALSE, 71 | metadata = metadata, 72 | sample_id_column = "sample", 73 | sample_anns = c("patient_group", "batch", "var"), 74 | assoc_list = assoc_list) 75 | } 76 | -------------------------------------------------------------------------------- /man/plot_sample_2D.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/plot_sample_2D.R 3 | \name{plot_sample_2D} 4 | \alias{plot_sample_2D} 5 | \title{Visualize sample variability in a 2D space} 6 | \usage{ 7 | plot_sample_2D( 8 | model, 9 | group = FALSE, 10 | method = "UMAP", 11 | metadata, 12 | sample_id_column = "sample", 13 | color_by, 14 | ... 15 | ) 16 | } 17 | \arguments{ 18 | \item{model}{A MOFA2 model.} 19 | 20 | \item{group}{Boolean flag TRUE/FALSE, to specify if a grouped MOFA model is provided.} 21 | 22 | \item{method}{A string specifying if "UMAP" or "MDS" should be performed} 23 | 24 | \item{metadata}{A data frame containing the annotations of the samples included in the MOFA model.} 25 | 26 | \item{sample_id_column}{A string character that refers to the column in \code{metadata} where the sample identifier is located.} 27 | 28 | \item{color_by}{A string character that refers to the column in \code{metadata} where the covariate to be tested is located.} 29 | 30 | \item{...}{inherited parameters of \code{uwot::umap()}} 31 | } 32 | \value{ 33 | A dataframe in a tidy format containing the manifold and the scatter plot 34 | } 35 | \description{ 36 | Performs dimensionality reduction of factor scores for the visualization of sample-level variability 37 | } 38 | \details{ 39 | For a \code{MOFA2} model it performs a multidimensional scaling (MDS) 40 | or uniform manifold approximation and projection (UMAP) of the factor 41 | scores. It allows you to color samples based on a covariate available in 42 | your meta-data. 43 | } 44 | \examples{ 45 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 46 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 47 | metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 48 | UMAP_embedding <- plot_sample_2D(model = model, 49 | group = FALSE, 50 | method = "UMAP", 51 | metadata = metadata, 52 | sample_id_column = "sample", 53 | color_by = "batch") 54 | } 55 | -------------------------------------------------------------------------------- /man/project_data.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/project_data.R 3 | \name{project_data} 4 | \alias{project_data} 5 | \title{Project new data into a known factor space} 6 | \usage{ 7 | project_data(model, test_data) 8 | } 9 | \arguments{ 10 | \item{model}{A MOFA2 model.} 11 | 12 | \item{test_data}{A dataframe in multiview representation as generated by \code{MOFAcellulaR::pb_dat2MOFA}} 13 | } 14 | \value{ 15 | A matrix with samples in rows and projected factors in columns 16 | } 17 | \description{ 18 | This function uses the feature weights learned by a MOFA2 model to project unseen data to the factor space 19 | } 20 | \details{ 21 | This function calculates the pseudoinverse of the feature weights of a \code{MOFA2} model and uses it to 22 | project unseen data into the factor space. Only shared features are used for the projection 23 | } 24 | \examples{ 25 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 26 | load(file.path(inputs_dir, "testpbcounts.rda")) 27 | load(file.path(inputs_dir, "testcoldata.rda")) 28 | pb_obj <- create_init_exp(counts = testpbcounts, coldata = testcoldata) 29 | 30 | ct_list <- filt_profiles(pb_dat = pb_obj, 31 | cts = c("Fib","CM"), 32 | ncells = 5, 33 | counts_col = "cell_counts", 34 | ct_col = "cell_type") 35 | 36 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 37 | min.count = 5, 38 | min.prop = 0.25) 39 | 40 | ct_list <- tmm_trns(pb_dat_list = ct_list, 41 | scale_factor = 1000000) 42 | 43 | ct_list <- center_views(pb_dat_list = ct_list) 44 | 45 | multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list) 46 | 47 | trained_model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 48 | 49 | projected_factors <- project_data(model = trained_model, test_data = multiview_dat) 50 | 51 | 52 | } 53 | -------------------------------------------------------------------------------- /man/tmm_trns.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/normalization.R 3 | \name{tmm_trns} 4 | \alias{tmm_trns} 5 | \title{TMM normalization of single cell data sets} 6 | \usage{ 7 | tmm_trns(pb_dat_list, scale_factor = 1e+06) 8 | } 9 | \arguments{ 10 | \item{pb_dat_list}{List of SummarizedExperiment generated from \code{MOFAcellulaR::filt_profiles()}} 11 | 12 | \item{scale_factor}{Numeric. Scaled counts are multiplied by this factor before log transformation} 13 | } 14 | \value{ 15 | A named list of SummarizedExperiments per cell type provided with normalized log transformed data in their \code{logcounts} assay 16 | } 17 | \description{ 18 | Performs for a list of SummarizedExperiments, TMM normalization scaled by a factor specified by the user. 19 | } 20 | \details{ 21 | This function estimates TMM normalization factors and normalizes a list of gene count matrices, data 22 | is additionally scaled using a factor specified by the user and \code{log1p()} transformed 23 | } 24 | \examples{ 25 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 26 | load(file.path(inputs_dir, "testpbcounts.rda")) 27 | load(file.path(inputs_dir, "testcoldata.rda")) 28 | 29 | pb_obj <- create_init_exp(counts = testpbcounts, 30 | coldata = testcoldata) 31 | 32 | ct_list <- filt_profiles(pb_dat = pb_obj, 33 | cts = c("Fib","CM"), 34 | ncells = 5, 35 | counts_col = "cell_counts", 36 | ct_col = "cell_type") 37 | 38 | ct_list <- filt_gex_byexpr(pb_dat_list = ct_list, 39 | min.count = 5, 40 | min.prop = 0.25) 41 | 42 | ct_list <- tmm_trns(pb_dat_list = ct_list, 43 | scale_factor = 1000000) 44 | } 45 | -------------------------------------------------------------------------------- /vignettes/.gitignore: -------------------------------------------------------------------------------- 1 | *.html 2 | *.R 3 | -------------------------------------------------------------------------------- /vignettes/get-started.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Running a multicellular factor analysis in a cross-condition single-cell atlas" 3 | output: rmarkdown::html_vignette 4 | vignette: > 5 | %\VignetteIndexEntry{get-started} 6 | %\VignetteEngine{knitr::rmarkdown} 7 | %\VignetteEncoding{UTF-8} 8 | --- 9 | 10 | ```{r, include = FALSE} 11 | knitr::opts_chunk$set( 12 | collapse = TRUE, 13 | comment = "#>" 14 | ) 15 | ``` 16 | 17 | ```{r setup, message=FALSE, warning=FALSE} 18 | library(MOFAcellulaR) 19 | library(dplyr) 20 | ``` 21 | 22 | ## Multicellular factor analysis 23 | 24 | We repurposed the statistical framework of multi-omics factor analysis [(MOFA)](https://www.embopress.org/doi/full/10.15252/msb.20178124) and [MOFA+](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1) to analyze cross-condition single cell atlases. These atlases profile molecular readouts (eg. gene expression) of individual cells per sample that can be classified into groups based on lineage (cell types) or functions (cell states). We assumed that this nested design could be represented as a multi-view dataset of a collection of patients, where each individual view contains the summarized information of all the features of a cell type per patient (eg. pseudobulk). In this data representation there can be as many views as cell types in the original atlas. MOFA then is used to estimate a latent space that captures the variability of patients across the distinct cell types. The estimated factors composing the latent space can be interpreted as a multicellular program that captures coordinated expression patterns of distinct cell types. The cell type specific gene expression patterns can be retrieved from the factor loadings, where each gene of each cell type would contain a weight that contributes to the factor score. Similarly, as in the application of MOFA to multiomics data, the factors can be used for an unsupervised analysis of samples or can be associated to biological or technical covariates of the original samples. Additionally, the reconstruction errors per view and factor can be used to prioritize cell types associated with covariates of interest. 25 | 26 | ## Data 27 | 28 | Here we show how to use MOFA for a multicellular factor analysis by applying it to a cross-condition atlas. 29 | 30 | As an example, we will use a toy dataset containing the pseudobulk gene expression information of 22 samples across 3 cell types 31 | 32 | ```{r import} 33 | inputs_dir <- base::system.file("extdata", package = "MOFAcellulaR") 34 | load(file.path(inputs_dir, "testpbcounts.rda")) 35 | load(file.path(inputs_dir, "testcoldata.rda")) 36 | ``` 37 | 38 | ```{r} 39 | testcoldata %>% 40 | dplyr::select(donor_id) %>% 41 | dplyr::group_by(donor_id) %>% 42 | dplyr::summarise(n()) %>% 43 | head() 44 | ``` 45 | 46 | ```{r} 47 | testcoldata %>% 48 | dplyr::select(cell_type) %>% 49 | dplyr::group_by(cell_type) %>% 50 | dplyr::summarise(n()) %>% 51 | head() 52 | ``` 53 | 54 | ## 1. Processing pseudobulk expression profiles 55 | 56 | We will assume that regardless of the preferred way of storing your pseudobulk data, a count matrix (genes in rows, samples in columns) will be accompanied by the annotations of the columns that contain the information of the cell type and sample of origin of the pseudobulk expression vector. 57 | 58 | If you are starting from a single cell data set, we recommend you to use functions like `scuttle::summarizeAssayByGroup()` to generate objects as the ones used in this vignette. 59 | 60 | In the example data `testpbcounts` contains a expression matrix. 61 | 62 | ```{r} 63 | testpbcounts[1:5,1:5] 64 | ``` 65 | 66 | And `testcoldata` contains the information of each sample (column) of `testpbcounts` in a named `data.frame`. 67 | 68 | ```{r} 69 | testcoldata %>% 70 | head() 71 | ``` 72 | 73 | The necessary components of `testcoldata` are the `donor_id` column that refers to the sample of interest (eg. patient), `cell_type` that will define the views in our multicellular factor analysis, and `cell_counts` that will allow to perform quality control filtering. 74 | 75 | `MOFAcellulaR` provides a series of useful tools to go from these two data objects to a MOFA ready dataframe that we be used to performed group factor analysis. 76 | 77 | First, we create an initial `SummarizedExperiment` object that will allow to make all the processing. 78 | 79 | ```{r} 80 | pb_obj <- MOFAcellulaR::create_init_exp(counts = testpbcounts, coldata = testcoldata) 81 | ``` 82 | 83 | Then, we will create a list of `SummarizedExperiment` of samples and cell-types of interest. 84 | 85 | An initial quality control step is to filter out pseudobulk samples coming from a low number of cells, under the assumption that the gene count estimates coming from a small population of cells may be unreliable. The actual number of cells needed for a pseudobulk profile is arbitrary and it is an empirical decision of the analyzer. If no sample filtering is required, it is possible also to define that in the helping functions. 86 | 87 | In this example we only work with cardiomyocytes (CM) and fibroblasts (Fib) 88 | 89 | ```{r} 90 | ct_list <- MOFAcellulaR::filt_profiles(pb_dat = pb_obj, 91 | cts = c("Fib","CM"), 92 | ncells = 0, # Change to your knowledge!! 93 | counts_col = "cell_counts", # This refers to the column name in testcoldata where the number of cells per profile was stored 94 | ct_col = "cell_type") # This refers to the column name in testcoldata where the cell-type label was stored 95 | ``` 96 | 97 | Once profiles and views have been defined, it is possible to filter complete views that have very few samples 98 | 99 | ```{r} 100 | ct_list <- MOFAcellulaR::filt_views_bysamples(pb_dat_list = ct_list, 101 | nsamples = 2) 102 | ``` 103 | 104 | The next step, requires to identify lowly expressed genes and highly variable genes per cell-type independently. We reuse the same criteria as `edgeR` to identify lowly expressed genes. Similarly as the number of cells, this parameters should be decided by the analyst. 105 | 106 | ```{r} 107 | ct_list <- MOFAcellulaR::filt_gex_byexpr(pb_dat_list = ct_list, 108 | min.count = 5, # Modify!! 109 | min.prop = 0.25) # Modify!! 110 | ``` 111 | Since we override within MOFAcellulaR the group parameter, all samples are assumed to be of the same condition group. 112 | 113 | Once genes per view have been defined, it is also possible to filter complete views with a few genes 114 | 115 | ```{r} 116 | ct_list <- filt_views_bygenes(pb_dat_list = ct_list, 117 | ngenes = 15) 118 | ``` 119 | 120 | Additionaly, you would like to filter samples within a view that have a large number of 0 values across features, here refer as samples with low feature coverage (we have observed that these affect the MOFA model by fitting factors associated exclusively with outlier samples). Here we expect that each sample within a view has a 90% coverage of features. 121 | 122 | ```{r} 123 | ct_list <- filt_samples_bycov(pb_dat_list = ct_list, 124 | prop_coverage = 0.9) 125 | ``` 126 | 127 | 128 | Normalization of pseudobulk expression profiles using Trimmed Mean of the M-values (TMM) from `edgeR::calcNormFactors` is then performed 129 | 130 | ```{r} 131 | ct_list <- MOFAcellulaR::tmm_trns(pb_dat_list = ct_list, 132 | scale_factor = 1000000) 133 | ``` 134 | 135 | Identification of highly variable genes per cell type is performed with `scran::getTopHVGs()`, however you can also provide your own list of highly variable genes if preferred. As a suggestion, we consider this step optional if the number of features is relatively low. 136 | 137 | ```{r} 138 | ct_list <- MOFAcellulaR::filt_gex_byhvg(pb_dat_list = ct_list, 139 | prior_hvg = NULL, 140 | var.threshold = 0) 141 | ``` 142 | 143 | Finally, it is possible to veto genes to be part of the model for specific cell-types. The way we deal with this is to create a dictionary of exclusive genes for a given cell-type. These could be for example marker genes. 144 | 145 | In this vignette, we will explicitly make TTN a gene exclusive for cardiomyocytes, and POSTN exclusive to fibroblasts based on prior knowledge, avoiding these to be background genes for other cell types. 146 | 147 | ```{r} 148 | prior_hvg_test <- list("CM" = c("TTN"), 149 | "Fib" = c("POSTN")) 150 | 151 | ct_list <- MOFAcellulaR::filt_gex_bybckgrnd(pb_dat_list = ct_list, 152 | prior_mrks = prior_hvg_test) 153 | ``` 154 | 155 | Once final genes per view have been defined, we can filter again views with not enough genes 156 | 157 | ```{r} 158 | ct_list <- MOFAcellulaR::filt_views_bygenes(pb_dat_list = ct_list, 159 | ngenes = 15) 160 | ``` 161 | 162 | To convert the cell-type list into a MOFA ready object we just run the following line 163 | 164 | ```{r} 165 | multiview_dat <- pb_dat2MOFA(pb_dat_list = ct_list, 166 | sample_column = "donor_id") 167 | ``` 168 | 169 | All the previous steps can be concatenated using `%>%` for your convenience 170 | 171 | ```{r,eval=TRUE, warning=FALSE} 172 | 173 | multiview_dat <- MOFAcellulaR::create_init_exp(counts = testpbcounts, 174 | coldata = testcoldata) %>% 175 | MOFAcellulaR::filt_profiles(pb_dat = ., 176 | cts = c("Fib","CM", "Endo"), 177 | ncells = 0, 178 | counts_col = "cell_counts", # This refers to the column name in testcoldata where the number of cells per profile was stored 179 | ct_col = "cell_type") %>% 180 | MOFAcellulaR::filt_views_bysamples(pb_dat_list = ., 181 | nsamples = 2) %>% 182 | MOFAcellulaR::filt_gex_byexpr(pb_dat_list = ., 183 | min.count = 5, 184 | min.prop = 0.25) %>% 185 | MOFAcellulaR::filt_views_bygenes(pb_dat_list = ., 186 | ngenes = 15) %>% 187 | MOFAcellulaR::filt_samples_bycov(pb_dat_list = ., 188 | prop_coverage = 0.9) %>% 189 | MOFAcellulaR::tmm_trns(pb_dat_list = ., 190 | scale_factor = 1000000) %>% 191 | MOFAcellulaR::filt_gex_byhvg(pb_dat_list = ., 192 | prior_hvg = NULL, 193 | var.threshold = 0) %>% 194 | MOFAcellulaR::filt_gex_bybckgrnd(pb_dat_list = ., 195 | prior_mrks = prior_hvg_test) %>% 196 | MOFAcellulaR::filt_views_bygenes(pb_dat_list = ., 197 | ngenes = 15) %>% 198 | MOFAcellulaR::filt_samples_bycov(pb_dat_list = ., 199 | prop_coverage = 0.9) %>% 200 | MOFAcellulaR::pb_dat2MOFA(pb_dat_list = ., 201 | sample_column = "donor_id") 202 | ``` 203 | 204 | ## 2. Fitting a MOFA model 205 | 206 | Once the single cell data is transformed into a multi-view representation, now we can use MOFA to run a multicellular factor analysis. 207 | 208 | We will try to identify 6 factors that explain the variability between patients captured by the seven different cell-types. 209 | 210 | MOFA self-regularizes and will indicate a potential optimal number of factors useful to describe the variability of your data, we advise to follow the indications of [MOFA](https://biofam.github.io/MOFA2/tutorials.html) 211 | 212 | Every factor captures coordination of gene expression across cell types and will be called multicellular gene factors for the rest of the vignette. 213 | 214 | It is important to clarify what these factors capture: 215 | 216 | a) Coordinated expression of identical genes (generalistic response) across cell-types 217 | b) Coordinated expression of different genes (cell-type specific response) across cell-types 218 | 219 | Fitting the model should take seconds. 220 | 221 | ```{r, eval=FALSE} 222 | MOFAobject <- MOFA2::create_mofa(multiview_dat) 223 | 224 | data_opts <- MOFA2::get_default_data_options(MOFAobject) 225 | train_opts <- MOFA2::get_default_training_options(MOFAobject) 226 | model_opts <- MOFA2::get_default_model_options(MOFAobject) 227 | 228 | # This avoids the regularization of multicellular programs per cell type. 229 | # This avoids less sparse gene weights 230 | model_opts$spikeslab_weights <- FALSE 231 | 232 | # Define the number of factors needed 233 | model_opts$num_factors <- 5 234 | 235 | # Prepare MOFA model: 236 | MOFAobject <- MOFA2::prepare_mofa(object = MOFAobject, 237 | data_options = data_opts, 238 | model_options = model_opts, 239 | training_options = train_opts) 240 | 241 | outfile <- file.path("./vignettemodel.hdf5") 242 | 243 | model <- MOFA2::run_mofa(MOFAobject, outfile) 244 | ``` 245 | 246 | ```{r, eval=T, echo=F} 247 | model <- MOFA2::load_model(file.path(inputs_dir, "testmodel.hdf5")) 248 | ``` 249 | 250 | ## 3. Exploring the MOFA model 251 | 252 | ### Exporting model outputs 253 | 254 | For convenience, we provide functions to explore the results of your model complementary to the ones already provided by [MOFA](https://biofam.github.io/MOFA2/tutorials.html) documentation. 255 | 256 | These functions are based on the idea that users will have extra information regarding the samples they analyzed. We provide supplemental annotations of the toy object. 257 | 258 | ```{r} 259 | metadata <- readRDS(file.path(inputs_dir, "testmetadata.rds")) 260 | head(metadata) 261 | ``` 262 | 263 | Our sample meta data can also contain continous measurements if needed, for example a clinical variable `fake_var` 264 | 265 | ```{r} 266 | set.seed(145) 267 | metadata$fake_var <- stats::rnorm(nrow(metadata)) 268 | ``` 269 | 270 | You can obtain the factor scores of each of your samples by calling the next function 271 | 272 | ```{r} 273 | all_factors <- MOFAcellulaR::get_tidy_factors(model = model, 274 | metadata = metadata, 275 | factor = "all", 276 | sample_id_column = "sample") 277 | 278 | head(all_factors) 279 | ``` 280 | 281 | You can specify also which factor you are interested and perform any type of statistical analysis of interest 282 | 283 | ```{r} 284 | Factor3 <- MOFAcellulaR::get_tidy_factors(model = model, 285 | metadata = metadata, 286 | factor = "Factor3", 287 | sample_id_column = "sample") 288 | 289 | head(Factor3) 290 | ``` 291 | 292 | Each factor is composed by a linear combination of genes per cell-type, and it is possible to extract the weights for each factor with a practical function 293 | 294 | ```{r} 295 | gene_weights <- MOFAcellulaR::get_geneweights(model = model, factor = "Factor1") 296 | head(gene_weights) 297 | ``` 298 | 299 | These gene loadings are essential if you want to map cell-type specific processes to bulk and spatial transcriptomics, since they can be treated as gene sets. If you are interested in this, we refer to [decoupleR](https://saezlab.github.io/decoupleR/#:~:text=decoupleR%20is%20a%20Bioconductor%20package,and%20weight%20of%20network%20interactions.) and [decoupler-py](https://decoupler-py.readthedocs.io/en/latest/) that explain in detail how to perform enrichment analysis with this type of weighted gene sets. 300 | 301 | Alternatively, the gene loadings can be reduced to functional or cellular processes by enriching gene sets provided by literature in each cell-type specific signature. Treat your gene loading matrix as scaled transcriptomics and perform your enrichment test of preference, see decoupleR's documentation for this. 302 | 303 | ### Visualizing sample variability 304 | 305 | As an initial exploratory analysis, one may want to visualize samples in a 2D space, here we provide a plotting function that allows to perform this using UMAPs or multidimensional-scaling plots. 306 | 307 | ```{r, fig.height = 3, fig.width = 4} 308 | UMAP_embedding <- MOFAcellulaR::plot_sample_2D(model = model, 309 | method = "UMAP", 310 | metadata = metadata, 311 | sample_id_column = "sample", 312 | color_by = "patient_group") 313 | 314 | ``` 315 | 316 | ### Performing statistical analyses 317 | 318 | To facilitate the exploration of the model, we provide a wrapper that performs association tests between factor scores and covariates of the samples, the covariates can be continuous or categorical. In the case of continuous variables a linear model is fitted. For categorical variables, analysis of variance (ANOVA) is performed. 319 | 320 | ```{r} 321 | categorical_assoc <- MOFAcellulaR::get_associations(model = model, 322 | metadata = metadata, 323 | sample_id_column = "sample", 324 | test_variable = "patient_group", 325 | test_type = "categorical", 326 | group = FALSE) 327 | 328 | categorical_assoc 329 | ``` 330 | 331 | 332 | ```{r} 333 | continuous_assoc <- MOFAcellulaR::get_associations(model = model, 334 | metadata = metadata, 335 | sample_id_column = "sample", 336 | test_variable = "fake_var", 337 | test_type = "continuous", 338 | group = FALSE) 339 | 340 | continuous_assoc 341 | ``` 342 | 343 | ### Visualizing the complete model 344 | 345 | With MOFA you are able to interpret your model in distinct ways: 346 | 347 | 1) First it reduces the variability of samples across cell-types inferring a latent space. See `MOFAcellulaR::get_tidy_factors` 348 | 349 | 2) The latent space captures certain percentage of the variability of the original data, in this case the variability of samples within a single cell-type. A full exploration of the model can be done using: 350 | 351 | ```{r} 352 | model@cache$variance_explained$r2_total 353 | ``` 354 | 355 | 3) Each latent variable contributes in explaining the variability of the original data and that can also be used to prioritize signals. 356 | 357 | ```{r} 358 | model@cache$variance_explained$r2_per_factor$single_group[,,drop = F] 359 | ``` 360 | 361 | For example, in this model, we previously identified Factor1 to be associated with our patient grouping, and based on the explaned variance, we can say that while it represents a multicellular program of the three cell-types we analyzed, Factor1 mainly captures the variability of samples within CMs. 362 | 363 | 4) Each latent variable represents a multicellular program that can be explored in detail with `MOFAcellulaR::get_geneweights` 364 | 365 | To visualize the distinct components of the model, we provide a heatmap plotting function that collects the distinct levels of results of the model 366 | 367 | ```{r, fig.width=4.5, fig.height=6} 368 | assoc_list <- list("categorical" = categorical_assoc, "continuous" = continuous_assoc) 369 | 370 | plot_MOFA_hmap(model = model, 371 | group = FALSE, 372 | metadata = metadata, 373 | sample_id_column = "sample", 374 | sample_anns = c("patient_group", "batch", "fake_var"), 375 | assoc_list = assoc_list) 376 | ``` 377 | 378 | ```{r} 379 | utils::sessionInfo() 380 | ``` 381 | --------------------------------------------------------------------------------