Data statistics DVF, available on explore.data.gouv.fr/immobilier. The files contain the number of sales, the average and the median of prices per m2. - Total DVF statistics: statistics by geographical scale, over the 10 semesters available. - Monthly DVF statistics: statistics by geographical scale and by month. ## Description of treatment The code allows statistics to be generated from the data of land value requests, aggregated at different scales, and their evolution over time (monthly). The following indicators have been calculated on a monthly basis and over the entire period available (10 semesters): * number of mutations * average prices per m2 * median of prices per m2 * Breakdown of sales prices by tranches for each type of property from: * houses * apartments * houses + apartments * commercial premises and for each scale from: * nation * Department * EPCI * municipality * Cadastral section The source data contain the following types of mutations: sale, sale in the future state of completion, sale of building land, tendering, expropriation and exchange. We have chosen to keep only sales, sales in the future state of completion and auctions for statistics*. In addition, for the sake of simplicity, we have chosen to keep only mutations that concern a single asset (excluding dependency)*. Our path is as follows: 1. for a transfer that would include assets of several types (e.g. a house + a commercial premises), it is not possible to reconstitute the share of the land value allocated to each of the assets included. 2. for a transfer that would include several assets of the same type (e.g. X apartments), the total value of the transfer is not necessarily equal to X times the value of an apartment, especially in the case where the assets are very different (area, work to be carried out, floor, etc.). We had initially kept these goods by calculating the price per m2 of the mutation by considering the goods of the mutation as a single good of an area to the sum of the surfaces of the goods, but this method, which ultimately concerned only a marginal quantity of goods, did not convince us for the final version. The price per m2 is then calculated by dividing the land value of the change by the surface area of the building of the property concerned. We finally exclude mutations for which we could not calculate the price per m2, as well as those whose price per m2 is more than € 100k (arbitrary choice)*. We have not incorporated any other outlier restrictions in order to maintain fidelity to the original data and to report potential anomalies. Displaying the median on the site reduces the impact of outliers on color scales. _*: The mentioned filters are applied for the calculation of statistics, but all mutations of the source files are well displayed on the application at the plot level.

This dataset includes intermediate data from RiboBase that generates translation efficiency (TE). The code to generate the files can be found at https://github.com/CenikLab/TE_model.

We uploaded demo HeLa .ribo files, but due to the large storage requirements of the full dataset, I recommend contacting Dr. Can Cenik directly to request access to the complete version of RiboBase if you need the original data.

The detailed explanation for each file:

human_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in human.

human_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in human.

human_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in human.

human_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in human.

human_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in human.

human_TE_rho.rda: TE proportional similarity data as genes by genes matrix in human.

mouse_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in mouse.

mouse_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in mouse.

mouse_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in mouse.

mouse_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in mouse.

mouse_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in mouse.

mouse_TE_rho.rda: TE proportional similarity data as genes by genes matrix in mouse.

All the data was passed quality control. There are 1054 mouse samples and 835 mouse samples:
* coverage > 0.1 X
* CDS percentage > 70%
* R2 between RNA and RIBO >= 0.188 (remove outliers)

All ribosome profiling data here is non-dedup winsorizing data paired with RNA-seq dedup data without winsorizing (even though it names as flatten, it just the same format of the naming)

####code
If you need to read rda data please use load("rdaname.rda") with R

If you need to calculate proportional similarity from clr data:
library(propr)
human_TE_homo_rho <- propr:::lr2rho(as.matrix(clr_data))
rownames(human_TE_homo_rho) <- colnames(human_TE_homo_rho) <- rownames(clr_data)

LOF calculation time (seconds) comparison.