Providing Bioimage Dataset for ExperimentHub
2022-04-28
Package
BioImageDbs 1.4.0
Last modified: 2022-04-27 12:08:51
Compiled: 2022-04-28 10:14:16
1 Utilisation and prospects of bioimage datasets
In recent years, there has been a growing need for data analysis using machine learning in the field of bioimaging research. Machine learning is an inductive approach using data, and the construction of models, such as image segmentation and classification, involves the use of image data itself. Therefore, the publication and sharing of bioimage datasets [1] as well as knowledge creation through providing metadata to bioimages [2,3] are important issues to be discussed. At present, there is no commonly used format for sharing bioimage datasets. Also, the data is scattered among various repositories. Therefore, different image repositories manage the data in different formats (image data itself and metadata, including image format, instruments/microscopes and biosamples).
In the data analysis and quantification using those images, it is assumed that several steps of image pre-processing are performed depending on the analysis environment used. However, the implementation of supervised learning starts with finding a repository of the bioimage dataset that contains original images and their corresponding supervised labels. Once the repository is found, the image data is downloaded from the repository, the data is loaded into each environment and it is prepared in a format suitable for analytical package. These processes are time consuming before the main analysis. Also, in most of the image repositories, the data are not published in a format suitable for reading and processing in R (.Rdata, etc.), and the data are not easy to use for R users.
For performing supervised learning of bioimage data, BioImageDbs provides R list objects of the original images and their corresponding supervised labels converted into a 4D or 5D array. After retrieving the data from ExperimentHub, it can be utilised for deep learning using Keras/Tensorflow [4] and other machine learning methods, without the need for pre-processing.
On the other hand, many image analysis packages are also available on R; however, there is a lack of standardisation in image analysis. The use of common, open datasets is one of the essential steps in standardising and comparing the analytical methods. The provision of the array data of images through ExperimentHub is also intended for applications such as (1) comparing models using common-sharing data among R users and (2) applying predictions to new datasets through transfer learning and fine-tuning based on these arrays.
2 Fetch Bioimage Datasets from ExperimentHub
The BioImageDbs package provides the metadata for all BioImage
databases in ExperimentHub.
The BioImageDbs package provides the metadata for bioimage datasets,
which is preprocessed as array format and saved in
ExperimentHub.
First we load/update the ExperimentHub resource.
library(ExperimentHub)
eh <- ExperimentHub()Next we list all BioImageDbs entries from ExperimentHub.
query(eh, "BioImage")## ExperimentHub with 73 records
## # snapshotDate(): 2022-04-19
## # $dataprovider: Satoshi Kume <satoshi.kume.1984@gmail.com>, CELL TRACKING C...
## # $species: Mus musculus, Homo sapiens, Rattus norvegicus, Drosophila melano...
## # $rdataclass: List, magick-image
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6874"]]'
##
## title
## EH6874 | EM_id0001_Brain_CA1_hippocampus_region_5dTensor.Rds
## EH6875 | EM_id0001_Brain_CA1_hippocampus_region_5dTensor_train_dataset.gif
## EH6876 | EM_id0002_Drosophila_brain_region_5dTensor.Rds
## EH6877 | EM_id0002_Drosophila_brain_region_5dTensor_train_dataset.gif
## EH6878 | LM_id0001_DIC_C2DH_HeLa_4dTensor.Rds
## ... ...
## EH6942 | EM_id0009_MurineBMMC_All_512_4dTensor_dataset.gif
## EH6943 | EM_id0010_HumanBlast_All_512_4dTensor.Rds
## EH6944 | EM_id0010_HumanBlast_All_512_4dTensor_dataset.gif
## EH6945 | EM_id0011_HumanJurkat_All_512_4dTensor.Rds
## EH6946 | EM_id0011_HumanJurkat_All_512_4dTensor_dataset.gifWe can confirm the metadata in ExperimentHub in Bioconductor S3 bucket
with mcols().
mcols(query(eh, "BioImage"))## DataFrame with 73 rows and 15 columns
## title dataprovider species
## <character> <character> <character>
## EH6874 EM_id0001_Brain_CA1_.. https://www.epfl.ch/.. Mus musculus
## EH6875 EM_id0001_Brain_CA1_.. https://www.epfl.ch/.. Mus musculus
## EH6876 EM_id0002_Drosophila.. the ISBI 2012 Challe.. Drosophila melanogas..
## EH6877 EM_id0002_Drosophila.. the ISBI 2012 Challe.. Drosophila melanogas..
## EH6878 LM_id0001_DIC_C2DH_H.. CELL TRACKING CHALLE.. Homo sapiens
## ... ... ... ...
## EH6942 EM_id0009_MurineBMMC.. Pattern Recognition .. Mus musculus
## EH6943 EM_id0010_HumanBlast.. Pattern Recognition .. Homo sapiens
## EH6944 EM_id0010_HumanBlast.. Pattern Recognition .. Homo sapiens
## EH6945 EM_id0011_HumanJurka.. Pattern Recognition .. Homo sapiens
## EH6946 EM_id0011_HumanJurka.. Pattern Recognition .. Homo sapiens
## taxonomyid genome description coordinate_1_based
## <integer> <character> <character> <integer>
## EH6874 10090 NA 5D arrays with the b.. 1
## EH6875 10090 NA A animation file (.g.. 1
## EH6876 7227 NA 5D arrays with the b.. 1
## EH6877 7227 NA A animation file (.g.. 1
## EH6878 9606 NA 4D arrays with the m.. 1
## ... ... ... ... ...
## EH6942 10090 NA A animation file (.g.. 1
## EH6943 9606 NA 4D arrays with the m.. 1
## EH6944 9606 NA A animation file (.g.. 1
## EH6945 9606 NA 4D arrays with the m.. 1
## EH6946 9606 NA A animation file (.g.. 1
## maintainer rdatadateadded preparerclass
## <character> <character> <character>
## EH6874 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6875 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6876 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6877 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6878 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## ... ... ... ...
## EH6942 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6943 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6944 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6945 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## EH6946 Satoshi Kume <satosh.. 2021-05-18 BioImageDbs
## tags rdataclass
## <AsIs> <character>
## EH6874 3D images,bioimage,CellCulture,... List
## EH6875 animation,bioimage,CellCulture,... magick-image
## EH6876 3D image,bioimage,CellCulture,... List
## EH6877 animation,bioimage,CellCulture,... magick-image
## EH6878 bioimage,cell tracking,CellCulture,... List
## ... ... ...
## EH6942 2D images,bioimage,CellCulture,... magick-image
## EH6943 2D images,bioimage,CellCulture,... List
## EH6944 2D images,bioimage,CellCulture,... magick-image
## EH6945 2D images,bioimage,CellCulture,... List
## EH6946 2D images,bioimage,CellCulture,... magick-image
## rdatapath sourceurl sourcetype
## <character> <character> <character>
## EH6874 BioImageDbs/v01/EM_i.. https://github.com/k.. PNG
## EH6875 BioImageDbs/v01/EM_i.. https://github.com/k.. PNG
## EH6876 BioImageDbs/v01/EM_i.. https://github.com/k.. PNG
## EH6877 BioImageDbs/v01/EM_i.. https://github.com/k.. PNG
## EH6878 BioImageDbs/v01/LM_i.. https://github.com/k.. PNG
## ... ... ... ...
## EH6942 BioImageDbs/v02/EM_i.. https://github.com/k.. PNG
## EH6943 BioImageDbs/v02/EM_i.. https://github.com/k.. PNG
## EH6944 BioImageDbs/v02/EM_i.. https://github.com/k.. PNG
## EH6945 BioImageDbs/v02/EM_i.. https://github.com/k.. PNG
## EH6946 BioImageDbs/v02/EM_i.. https://github.com/k.. PNGWe can retrieve only the BioImageDbs tibble files as follows.
qr <- query(eh, c("BioImageDbs", "LM_id0001"))
qr## ExperimentHub with 5 records
## # snapshotDate(): 2022-04-19
## # $dataprovider: CELL TRACKING CHALLENGE (http://celltrackingchallenge.net/2...
## # $species: Homo sapiens
## # $rdataclass: List, magick-image
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6878"]]'
##
## title
## EH6878 | LM_id0001_DIC_C2DH_HeLa_4dTensor.Rds
## EH6879 | LM_id0001_DIC_C2DH_HeLa_4dTensor_train_dataset.gif
## EH6880 | LM_id0001_DIC_C2DH_HeLa_4dTensor_Binary.Rds
## EH6881 | LM_id0001_DIC_C2DH_HeLa_4dTensor_Binary_train_dataset.gif
## EH6882 | LM_id0001_DIC_C2DH_HeLa_5dTensor.Rds#Import data
#BioImageDbs_image_Dat <- qr[[1]]3 5D Arrays from the ExperimentHub
The ordering of the array dimensions corresponds to the channels_last format (default) in R/Keras. The input shape of 5D array is to be batch, spatial_dim1, spatial_dim2, spatial_dim3 and channels. The number of this batch is the same as the number of the 3D image sets. The number of channels is 1 for grey images and 3 for RGB images.
4 4D Arrays from the ExperimentHub
The ordering of the array dimensions corresponds to the channels_last format (default) in R/Keras. The input shape of 4D array is to be batch, height, width and channels. The number of this batch is the same as the number of the 2D images.
5 Visualization of gif images from the ExperimentHub
As a test, we also provided gif files of some arrays for visualizations.
We visualize the files using magick::image_read function.
qr <- query(eh, c("BioImageDbs", ".gif"))
qr## ExperimentHub with 32 records
## # snapshotDate(): 2022-04-19
## # $dataprovider: Satoshi Kume <satoshi.kume.1984@gmail.com>, CELL TRACKING C...
## # $species: Mus musculus, Homo sapiens, Rattus norvegicus, Drosophila melano...
## # $rdataclass: magick-image
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6875"]]'
##
## title
## EH6875 | EM_id0001_Brain_CA1_hippocampus_region_5dTensor_train_dataset.gif
## EH6877 | EM_id0002_Drosophila_brain_region_5dTensor_train_dataset.gif
## EH6879 | LM_id0001_DIC_C2DH_HeLa_4dTensor_train_dataset.gif
## EH6881 | LM_id0001_DIC_C2DH_HeLa_4dTensor_Binary_train_dataset.gif
## EH6884 | LM_id0002_PhC_C2DH_U373_4dTensor_train_dataset.gif
## ... ...
## EH6935 | EM_id0008_Human_NB4_2D_All_Nuc_512_4dTensor_dataset.gif
## EH6937 | EM_id0008_Human_NB4_2D_All_Nuc_1024_4dTensor_dataset.gif
## EH6942 | EM_id0009_MurineBMMC_All_512_4dTensor_dataset.gif
## EH6944 | EM_id0010_HumanBlast_All_512_4dTensor_dataset.gif
## EH6946 | EM_id0011_HumanJurkat_All_512_4dTensor_dataset.gif#EM_id0001_Brain_CA1_hippocampus_region_5dTensor_train_data
qr[1]## ExperimentHub with 1 record
## # snapshotDate(): 2022-04-19
## # names(): EH6875
## # package(): BioImageDbs
## # $dataprovider: https://www.epfl.ch/labs/cvlab/data/data-em/
## # $species: Mus musculus
## # $rdataclass: magick-image
## # $rdatadateadded: 2021-05-18
## # $title: EM_id0001_Brain_CA1_hippocampus_region_5dTensor_train_dataset.gif
## # $description: A animation file (.gif) of the train dataset of EM_id0001_Br...
## # $taxonomyid: 10090
## # $genome: NA
## # $sourcetype: PNG
## # $sourceurl: https://github.com/kumeS/BioImageDbs
## # $sourcesize: NA
## # $tags: c("animation", "bioimage", "CellCulture", "electron
## # microscopy", "microscope", "scanning electron microscopy",
## # "segmentation", "Tissue")
## # retrieve record with 'object[["EH6875"]]'##Display the gif image
#magick::image_read(qr[[1]])
Figure 1: EM_id0001_Brain_CA1_hippocampus_region_5dTensor_train_dataset.gif
Figure 2: EM_id0002_Drosophila_brain_region_5dTensor_train_dataset.gif
Figure 3: EM_id0003_J558L_4dTensor_train_dataset.gif
Figure 4: EM_id0004_PrHudata_4dTensor_train_dataset.gif
Figure 5: EM_id0005_Mouse_Kidney_2D_All_Mito_1024_4dTensor_dataset.gif
Figure 6: EM_id0005_Mouse_Kidney_2D_All_Nuc_1024_4dtensor.Rds
Figure 7: EM_id0006_Rat_Liver_2D_All_Mito_1024_4dTensor_dataset.gif
Figure 8: EM_id0006_Rat_Liver_2D_All_Nuc_1024_4dTensor_dataset.gif
Figure 9: EM_id0007_Mouse_Kidney_MultiScale_All_Low_Glomerulus_1024_4dTensor_dataset.gif
Figure 10: EM_id0007_Mouse_Kidney_MultiScale_All_Middle_Podocyte_1024_4dTensor_dataset.gif
Figure 11: EM_id0008_Human_NB4_2D_All_Cel_512_4dTensor_dataset.gif
Figure 12: EM_id0008_Human_NB4_2D_All_Nuc_1024_4dTensor_dataset.gif
Figure 13: EM_id0009_MurineBMMC_All_512_4dTensor_dataset.gif
Figure 14: EM_id0010_HumanBlast_All_512_4dTensor_dataset.gif
Figure 15: EM_id0011_HumanJurkat_All_512_4dTensor_dataset.gif
Figure 16: LM_id0001_DIC_C2DH_HeLa_4dTensor_train_dataset.gif
Figure 17: LM_id0002_PhC_C2DH_U373_4dTensor_train_dataset.gif
Figure 18: LM_id0003_Fluo_N2DH_GOWT1_4dTensor_train_dataset.gif
6 A simple execution command using Keras/Tensorflow
We select a data array and a label array from the data list and assign them to variables. These variables are then used as the x and y arguments of the fit (<keras.engine.training.Model>) function of Keras as an example. The model in Keras should be prepared before the execution.
## Not Run ##
# qr <- query(eh, c("BioImageDbs"))
# BioImageData <- qr[[1]]
# data <- BioImageData$Train$Train_Original
# labels <- BioImageData$Train$Train_GroundTruth
# dim(data); dim(labels)
# model %>% fit( x = data, y = labels )7 About the imaging dataset and its metadata in BioImageDbs
For this dataset in BioImageDbs, the published open data was used as follows:
For cellular ultra-microstructures, electron microscopy-based imaging data of mouse B myeloma cell line J558L (ex. EM_id0003_J558L_4dTensor.Rda) [5] and primary human T cell isolated from peripheral blood mononuclear cells (ex. EM_id0004_PrHudata_4dTensor.Rda) [5], Human NB-4 cell (ex. EM_id0008_Human_NB4_2D_All_Cel_512_4dTensor.Rds) [3], murine bone marrow derived-mast cells (ex. EM_id0009_MurineBMMC_All_512_4dTensor.Rds) [5], human blasts (ex. EM_id0010_HumanBlast_All_512_4dTensor.Rds) [5], and human T-cell line Jurkat (ex. EM_id0011_HumanJurkat_All_512_4dTensor.Rds) [5] were used.
For bio-tissue ultra-microstructures, electron microscopy-based imaging data of the mouse brain (ex. EM_id0001_Brain_CA1_hippocampus_region_5dTensor.Rda) [6,7], Drosophila brain (ex. EM_id0002_Drosophila_brain_region_5dTensor.Rda) [8,9], mouse kidney (ex. EM_id0005_Mouse_Kidney_2D_All_Nuc_1024_4dtensor.Rds) [10] and rat liver (ex. EM_id0006_Rat_Liver_2D_All_Mito_1024_4dtensor.Rds) [10] were used.
For cell tracking, light microscopy-based imaging data of the human HeLa cells on a flat glass (ex. LM_id0001_DIC_C2DH_HeLa_4dTensor.Rda) [11,12], human glioblastoma-astrocytoma U373 cells on a polyacrylamide substrate (ex. LM_id0002_PhC_C2DH_U373_4dTensor.Rda) [11,12] and GFP-GOWT1 mouse stem cells (ex. LM_id0003_Fluo_N2DH_GOWT1_4dTensor.Rda) [13] were used.
The values of the supervised labels were provided as array data with binary or multiple values. The detailed information was described in the metadata file of BioImageDbs. Some of cell tracking data were obtained from the cell tracking challenge.
Session information
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] magick_2.7.3 BioImageDbs_1.4.0 ExperimentHub_2.4.0
## [4] AnnotationHub_3.4.0 BiocFileCache_2.4.0 dbplyr_2.1.1
## [7] BiocGenerics_0.42.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] Biobase_2.56.0 httr_1.4.2
## [3] sass_0.4.1 bit64_4.0.5
## [5] jsonlite_1.8.0 bslib_0.3.1
## [7] shiny_1.7.1 assertthat_0.2.1
## [9] interactiveDisplayBase_1.34.0 BiocManager_1.30.17
## [11] highr_0.9 stats4_4.2.0
## [13] blob_1.2.3 GenomeInfoDbData_1.2.8
## [15] tiff_0.1-11 yaml_2.3.5
## [17] BiocVersion_3.15.2 pillar_1.7.0
## [19] RSQLite_2.2.12 lattice_0.20-45
## [21] glue_1.6.2 digest_0.6.29
## [23] promises_1.2.0.1 XVector_0.36.0
## [25] htmltools_0.5.2 httpuv_1.6.5
## [27] pkgconfig_2.0.3 bookdown_0.26
## [29] zlibbioc_1.42.0 fftwtools_0.9-11
## [31] purrr_0.3.4 xtable_1.8-4
## [33] jpeg_0.1-9 later_1.3.0
## [35] tibble_3.1.6 KEGGREST_1.36.0
## [37] EBImage_4.38.0 generics_0.1.2
## [39] IRanges_2.30.0 ellipsis_0.3.2
## [41] cachem_1.0.6 withr_2.5.0
## [43] cli_3.3.0 magrittr_2.0.3
## [45] crayon_1.5.1 mime_0.12
## [47] memoise_2.0.1 evaluate_0.15
## [49] fansi_1.0.3 tools_4.2.0
## [51] lifecycle_1.0.1 stringr_1.4.0
## [53] S4Vectors_0.34.0 locfit_1.5-9.5
## [55] AnnotationDbi_1.58.0 Biostrings_2.64.0
## [57] compiler_4.2.0 jquerylib_0.1.4
## [59] GenomeInfoDb_1.32.0 rlang_1.0.2
## [61] grid_4.2.0 RCurl_1.98-1.6
## [63] htmlwidgets_1.5.4 rappdirs_0.3.3
## [65] bitops_1.0-7 rmarkdown_2.14
## [67] abind_1.4-5 DBI_1.1.2
## [69] curl_4.3.2 R6_2.5.1
## [71] knitr_1.39 dplyr_1.0.8
## [73] fastmap_1.1.0 bit_4.0.4
## [75] utf8_1.2.2 filelock_1.0.2
## [77] stringi_1.7.6 Rcpp_1.0.8.3
## [79] vctrs_0.4.1 png_0.1-7
## [81] tidyselect_1.1.2 xfun_0.30References
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